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 .
DETAILED ACTION
Response to Amendment
In the amendment dated 07/01/2026, the following occurred: Claims 1, 9, 16 and 18 have been amended. Claim 12 was canceled.
Claims 1-11 and 13-20 are currently pending.
Claim Objections
Claim 3 is objected to for the following informality: “… tracking the patient over a period of time to obtain readmission data; and providing the ADT data and the readmission data to the care transition.” should read “… tracking the patient over a period of time to obtain readmission data; and providing the ADT data and the readmission data to the care transition model.”
Claim 11 is objected to for the following informality: “The computer readable media of claim 9, wherein the instructions further cause the care transition system to: track the patient …” should read “The computer readable media of claim 9, wherein the instructions further cause the care transition model to: track the patient …”.
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 1-11 and 13-20 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.
Claims 1, 9 and 18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites methods and one or more non-transitory computer readable media for transition of care and prioritization system, which are within a statutory category.
Regarding claims 1 and 9, the limitation of (claim 1 being representative) training using historical discharge data, historical readmission data and insurance data associated with a plurality of patients and a plurality of healthcare facilities, wherein the insurance data is used to supplement incomplete historical discharge data and incomplete historical readmission data; receiving admission-discharge-transfer (ADT) data for a patient from a healthcare facility discharging the patient after a health event, wherein communication protocols are available for processing electronic health records; predicting a likelihood of readmission for the patient to a healthcare facility based on the ADT data; outputting a readmission profile for the patient, wherein the readmission profile for the patient is based on the likelihood of readmission for the patient; assigning a placement of the patient in a call list based on the readmission profile for the patient; and transmitting a notification to the patient based on the placement of the patient in the call list regarding claim 9- the limitation determine a priority for the patient based on the likelihood of readmission, wherein the priority represents a relative importance for initiating a follow up with the patient; generate a priority list based on the priority for the patient, wherein the priority list defines an order for patient follow up; and regarding claim 18- the limitation of receiving contact information associated with a target date of contact; determining a reason for the contact; generating a pre-contact notification frequency based on the reason for the contact and the contact information associated with the patient, wherein the pre-contact notification frequency represents a recommended frequency for a pre-contact notification, and wherein the pre-contact notification comprises an advance notification regarding the contact; generating a pre-contact notification schedule based on the pre-contact notification frequency; and transmit the pre-contact notifications to the patient ahead of the target date of the contact according to the pre-contact notification schedule as drafted, is a process that, under the broadest reasonable interpretation, covers a method organizing human activity but for the recitation of generic computer components. The claims encompass a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to train a model, receive admission-discharge-transfer (ADT) data, predict a likelihood of readmission, output a readmission profile, assign a placement of the patient in a call list, transmit a notification, determine a priority for the patient, generate a priority list, receive contact information, determine a reason for the contact, generate a pre-contact notification frequency, generate a pre-contact notification schedule and transmit the pre-contact notifications to the patient according to the pre-contact notification schedule in the manner described in the identified abstract idea, supra. The rules or instructions are the claimed steps of “training… receiving… predicting… outputting…assigning … transmitting… determining… generating… receiving…determining… generating… generating…and transmit the pre-contact notifications to the patient according to the pre-contact notification schedule” as indicated supra.
Other than reciting generic computer components (discussed infra), i.e., (in claim 1) a processor, (in claim 9) one or more non-transitory computer readable media, one or more processors and a care services system, (in claim 18) a processor, the claimed invention amounts to managing personal behavior or interaction between people (i.e., rules or instructions). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People (e.g. social activities, teaching, following rules or instructions)” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, claims 1 and 18 recites the additional element of a processor. Claim 9 recites the additional elements of one or more non-transitory computer readable media, one or more processors and a care services system. These additional elements are not exclusively defined by the applicant and are recited at a high-level of generality (i.e., a generic computer components for enabling access to medical information or for performing generic computer functions, see Spec. at para. [0034] and [0051]) such that they amounts to no more than mere instructions to apply the exception using a generic computer component. As set forth in MPEP 2106.04(d) “merely including instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Claim 1 further recite the additional elements of a care transition model and a user display. Claim 9 further recites the additional element of a care transition model. The additional element of the user display is recited at a high level of generality (i.e. a general means to output/display data) and amount to extra solution activity. The additional element of the care transition model is the application of mathematical concepts as described in the Spec. at para. [0039] that states the care transition model may be a machine learning model, such as a neural network, a random forest classifier, gradient boosting model, linear regression, or another algorithm configured to predict the likelihood of readmission to a healthcare facility. This mathematical concept is applied to (“apply it’) the abstract idea. MPEP 2106.04(d)(I) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of the processor, the one or more non-transitory computer readable media, the one or more processors and the care services system to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). Moreover, using generic computer components to perform abstract ideas does not provide a necessary inventive concept. See Alice, 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention”). Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
Also as discussed with respect to integration of the abstract idea into a practical application, the additional element of the user display was considered extra-solution activity. This has been re-evaluated under the “significantly more” analysis and determined to be well-understood, routine, conventional activity in the field. The prior art of record indicates that outputting data to a user display is well-understood, routine, conventional activity (see Kerr (US 2002/0190975) at para. [0011] and Loeffler (US 2015/0062152) at para. [0015]). Well-understood, routine, conventional activity cannot provide an inventive concept (“significantly more”). The additional element of the care transition model was determined to be (“apply it”) to the identified abstract idea. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP2106.05(1)(A) indicates that merely saying “apply it’ or equivalent to the abstract idea cannot provide an inventive concept (“significantly more’). As such the claim is not patent eligible.
The examiner notes that: A well-known, general-purpose computer has been determined by the courts to be a well-understood, routine and conventional element (see, e.g., Alice Corp. v. CLS Bank; see also MPEP 2106.05(d)); Receiving and/or transmitting data over a network (“a communications network”) has also been recognized by the courts as a well - understood, routine and conventional function (see, e.g., buySAFE v. Google; MPEP 2016(d)(II)); and Performing repetitive calculations is/are also well-understood, routine and conventional computer functions when they are claimed in a merely generic manner (see, e.g., Parker v. Flook; MPEP 2016.05(d)).
Claims 2-8, 10, 11, 13-17 and 19-20 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim(s) 2 and 10 further merely describe(s) the likelihood of readmission. Claims(s) 3 and 11 further merely describe(s) tracking the patient over a period of time to obtain readmission data and providing the ADT data and the readmission data to the care transition. Claims(s) 4 further merely describe(s) determining a priority. Claims(s) 5 and 13 further merely describe(s) the priority is further based on a likely cost of readmission. Claims(s) 6 further merely describe(s) placing the patient on a priority list for follow up. Claims(s) 7 and 15 further merely describe(s) a confidence metric. Claims(s) 8 and 14 further merely describe(s) receiving data from an application programming interface (API). Claims(s) 16 further merely describe(s) retrieving contact information, analyzing a history of contact and contact information, generating a confidence metric and generating a recommendation. Claim 16 also include the additional element of “a database” which is interpreted to be extra-solution activity as it stores data, and this does not provide practical application or significantly more. Claims(s) 17 further merely describe(s) analyzing demographic information associated with the patient. Claims(s) 19 further merely describe(s) the pre-contact notifications are transmitted automatically. Claims(s) 20 further merely describe(s) determining the type of pre-contact notification based on the contact information associated with the patient. Claims 2-8, 10, 11, 13-17 and 19-20 further define the abstract idea, fall within certain methods of organizing human activity and are rejected for the same reason presented above with respect to claims 1, 9 and 18. Claim(s) 8 and 14 also include the additional element of “an application programming interface (API)” which is interpreted to be extra-solution activity as data is received from the API and this does not provide practical application or significantly more. Claim(s) 11 also include the additional element of “a care transition system” which is interpreted the same as the care service system and does not provide practical application or significantly more for the same reasons.
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 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.
Claims 1-11 and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang (US 2020/0402665) and in further view of Gopal (US 2016/0358282).
REGARDING CLAIM 1
Zhang discloses a computer implemented method comprising: training a care transition model using historical discharge data, historical readmission data and insurance data associated with a plurality of patients and a plurality of healthcare facilities, wherein the insurance data is used to supplement incomplete historical discharge data and incomplete historical readmission data ([0053] teaches a readmission risk forecasting module (interpreted by examiner as a care transition model) that can facilitate forecasting readmission risk profile information for a patient that reflects a probability of readmission of the patient. The readmission risk forecasting model 106 can comprise one or more machine learning models that have been trained to predict information regarding likelihood of readmission of a patient following discharge based on learned correlations in various factors associated with the patient's medical history, the patient's cause for admission (also referred to as the readmission index), demographic factors, and the like. [0054] teaches clinical features included in the medical history data that can be used as input to the readmission risk forecasting model can include but are not limited to: past hospital stays/admissions and associated information regarding past courses of care and length of stay (LOS). [0104] teaches that historical patient data for past patients can be used to train and develop the readmission risk forecasting model. The training data used to train the readmission risk forecasting model can include post-discharge information tracked for the patients following discharge, including information regarding if they were readmitted and if so, when, where and reason for readmission (interpreted by examiner as training using historical discharge data and historical readmission data associated with a plurality of patients and a plurality of healthcare facilities) [0060] teaches that the readmission risk forecasting module can group the patient data for input to the readmission risk forecasting model into three sets of risk factors, where the data is exacted from insurance claims data files associated with the patient in various electronic data sources (interpreted by Examiner as insurance data)); receiving by a processor admission-discharge-transfer (ADT) data for a patient from a healthcare facility discharging the patient after a health event, wherein the processor is configured with communication protocols for processing electronic health record data ([0103] teaches receiving patient data comprising at least some of the various factors and data points input to the readmission forecasting model and [0104] teaches that historical patient data can be used to train and develop the readmission risk forecasting model (interpreted by examiner as receiving by a processor admission-discharge-transfer (ADT) data for a patient from a healthcare facility discharging the patient after a health event) [0162] teaches the illustrated aspects can also be practiced in distributed computing environments in which tasks are performed by remote processing devices that are linked through a communications network.); predicting by the processor a likelihood of readmission for the patient to a healthcare facility based on the ADT data using the care transition model ([0106] teaches the readmission risk forecasting model can include a readmission risk score that comprises a value that reflects an expected probability of readmission of the patient determined using the readmission risk forecasting model. In some embodiments, the readmission risk score can comprise a percentage score (e.g., from 0-100%), wherein the higher the score, the higher the probability of readmission (interpreted by examiner as predicting by the processor a likelihood of readmission for the patient to a healthcare facility based on the ADT data using the care transition model)); outputting a readmission profile for the patient to a user display, wherein the readmission profile for the patient is based on the likelihood of readmission for the patient ([0103] teaches generate readmission profile information for the patient and [0106] teaches the readmission risk profile information that can be output. [0123] teaches the readmission risk profile information (e.g., a predicted readmission risk score representative of a predicted risk (probability) of unplanned readmission) can be presented to one or more entities (e.g., clinicians) through an interactive user interface (UI) (interpreted by examiner as outputting a readmission profile for the patient to a user display, wherein the readmission profile for the patient is based on the likelihood of readmission for the patient));
Zhang does not explicitly disclose assigning a placement of the patient in a call list based on the readmission profile for the patient; and automatically operating a communication system to transmit a notification to the patient based on the placement of the patient in the call list, however Gopal discloses:
assigning a placement of the patient in a call list based on the readmission profile for the patient; and automatically operating a communication system to transmit a notification to the patient based on the placement of the patient in the call list (Gopal at [0031] teaches the readmission predictive model may be applied to member data to identify members at risk that will soon be discharged from the hospital. A threshold score comparison is made, program and/or intervention filter criteria are applied, and a daily referral list is generated (interpreted by Examiner as the call list). The referral list is generated in connection with member hospital discharges so that, as appropriate, each member may be enrolled in or start participating in a program or intervention as soon as possible after leaving the hospital. Because many readmissions occur within a few weeks or days of a patient's discharge from the hospital, timely intervention is important in reducing the likelihood of a readmission. The daily referral list allows the health benefits provider to identify members that are leaving the hospital, and high risk candidates for readmission. Appropriate programs and interventions may be defined at the time of discharge so that the likelihood of readmission is reduced (interpreted by Examiner as assigning a placement of the patient in a call list based on the readmission profile for the patient) [0023] teaches nurses, clinical specialists, and other representatives of the health benefits provider may interact with members to provide information about programs and interventions and other assistance related to the member's health conditions or problems and [0026] teaches making a phone call as part of an intervention (interpreted by Examiner as means to transmit a notification to the patient based on the placement of the patient in the call list)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the readmission methods of Zhang to incorporate assigning a placement of the patient in a call list based on the readmission profile for the patient; and automatically operating a communication system to transmit a notification to the patient based on the placement of the patient in the call list as taught by Gopal, with the motivation of managing patient health conditions and problems and reducing the likelihood that they return to the hospital. (Gopal at [abstract]).
REGARDING CLAIM 2
Zhang and Gopal disclose the limitation of claim 1.
Zhang further discloses:
The method of claim 1, wherein the likelihood of readmission is further based on a health history of the patient ([0053] teaches the readmission risk forecasting model can comprise one or more machine learning models that have been trained to predict information regarding likelihood of readmission of a patient following discharge based on learned correlations in various factors associated with the patient's medical history).
REGARDING CLAIM 3
Zhang and Gopal disclose the limitation of claim 1.
Zhang further discloses:
The method of claim 1, wherein the care transition model is further trained by: tracking the patient over a period of time to obtain readmission data; and providing the ADT data and the readmission data to the care transition ([0104] teaches patient data and the training data which include post-discharge information tracked for the patients following discharge, including information regarding if they were readmitted and if so, when, where and reason for readmission are provided to train the readmission risk forecasting model).
REGARDING CLAIM 4
Zhang and Gopal disclose the limitation of claim 1.
Zhang further discloses:
The method of claim 1, further comprising determining by the processor a priority for the patient based on the likelihood of readmission ([0076] teaches type and priority of admission with values (e.g., emergency, unknown, trauma etc.)).
REGARDING CLAIM 5
Zhang and Gopal disclose the limitation of claim 1.
Zhang further discloses:
The method of claim 4, wherein the priority is further based on a likely cost of readmission ([0110] teaches adverse events are one of the main contributing factors. The predicted causes for unplanned admission can be focused on the most common conditions as found in the data. Amongst others, the disclosed systems expect these to include the following causes, identified as the most common and also most costly causes of readmission).
REGARDING CLAIM 6
Zhang and Gopal disclose the limitation of claim 1.
Zhang further discloses:
The method of claim 5, further comprising placing the patient on a priority list for follow up based on the priority ([0025] teaches the model can identify patients with high unplanned admission risk, whose risk explanation profile matches with patients who have died within 6 months (interpreted by examiner as means for placing the patient on a priority list for follow up)).
REGARDING CLAIM 7
Zhang and Gopal disclose the limitation of claim 1.
Zhang further discloses:
The method of claim 1, wherein a confidence metric is generated by the care transition model ([0122] teaches the system can generate notification indicating that level of confidence in the accuracy of the predictions generated by the readmission risk forecasting model on the patient data).
REGARDING CLAIM 8
Zhang and Gopal disclose the limitation of claim 1.
Zhang further discloses:
The method of claim 1, wherein the ADT data is received from an application programming interface (API) associated with the healthcare facility ([0032] teaches the disclosed systems can be built into a web-based application for ease of use and to provide immediate access and feedback for the clinicians, [0033] teaches API’s to access the models, [0036] teaches the disclosed systems will build the web-based user interface to enable the interactions between users and the prediction results with contributing features and their scores).
REGARDING CLAIMS 9-15
Claims 9-11 and 13-15 are analogous to Claims 1-8 thus Claims 9-11 and 13-15 are similarly analyzed and rejected in a manner consistent with the rejection of Claims 1-8.
Claims 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang (US 2020/0402665), in view of Gopal (US 2016/0358282) and in further view of and in further view of Kusens (US 2013/0117038).
REGARDING CLAIM 16
Zhang and Gopal disclose the limitation of claim 9.
Zhang further discloses:
The computer readable media of claim 9, wherein the instructions further cause the care transition system to: retrieve contact information associated with the patient from a database ([0123] teaches patient data can be stored in database and [0062] teaches emergency contact type and emergency contact person (interpreted by examiner as retrieving contact information associated with a patient from a database));
Zhang does not explicitly disclose analyzing by the processor a history of contact associated with the patient and the contact information associated with the patient, however Gopal discloses:
analyze a history of contact associated with the patient and the contact information associated with the patient (Gopal at [0023] teaches the clinical profile database comprises a complete profile for a covered member including contact information, demographic profile data such as age and gender, claims data for medical and/or pharmacy claims submitted by the member to the health benefits provider, a contact history with details regarding communications between the member and the health benefits provider (e.g., mailings, telephone calls, emails, web site visits, and other outreach efforts), and participation data related to clinical programs and interventions in which the member has been enrolled and/or participated. A clinical care advance system may be used by nurses and clinical specialists to access the member's clinical profile and claims data and to assist them in providing services to members and [0030] teaches each member contact with the health benefits provider may be recorded. For example, participation data for members that are asked to periodically report health status indicators may be tracked. Members that do not report in when expected may be contacted by a representative of the health benefits provider (interpreted by examiner as means for analyzing a history of contact associated with the patient and the contact information associated with the patient));
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the contact information of Zhang to incorporate analyzing a history of contact associated with the patient and the contact information associated with the patient as taught by Gopal, with the motivation of managing patient health conditions and problems and reducing the likelihood that they return to the hospital. (Gopal at [abstract]).
Zhang and Gopal do not explicitly disclose generating a recommendation as to which contact information to use to contact the patient, however Kusens discloses:
generate a confidence metric associated with the contact information based on the analysis of the history of contact, wherein the confidence metric represents a likelihood of successful contact via the contact information; and generate a recommendation as to which contact information to use to contact the patient (Kusens at [0085] teaches a preferred method of contact stored in the information handling system for the provider and/or patient and that any suitable means of notification may be employed: text, voice, e-mail, and/or paper mail notifications could be sent (interpreted by Examiner as means to generate a confidence metric associated with the contact information and a recommendation as to which contact information to use to contact the patient)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the contact information of Zhang and Gopal to incorporate generating a recommendation as to which contact information to use to contact the patient as taught by Kusens, with the motivation of improving reliability of preventive care recommendations (Kusens at [0024]).
REGARDING CLAIM 17
Zhang, Gopal and Kusens disclose the limitation of claim 16.
Zhang further discloses:
The method of claim 16 further comprising: analyzing demographic information associated with the patient (Zhang at [0053] teaches analyzing demographic factors).
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 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.
Claims 18-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jamison (US 2020/0402665).
REGARDING CLAIM 18
Jamison discloses a computer implemented method comprising: receiving by a processor contact information associated with a patient and a target date of contact ([0031] teaches providing information such as a contact's addresses, phone numbers, important dates, history of interactions with the user and [0041] teaches setting reminders for those important dates (interpreted by examiner as the target date of contact)); determining by the processor a reason for the contact ([0038] teaches reason for corresponding with a contact); generating by the processor a pre-contact notification frequency based on the reason for the contact and the contact information associated with the patient, wherein the pre-contact notification frequency represents a recommended frequency for a pre-contact notification, and wherein the pre-contact notification comprises an advance notification regarding the contact ([0001] teaches frequencies of correspondence with contacts and [0007] teaches frequency of the correspondence over time with the contact); generating by the processor a pre-contact notification schedule based on the pre- contact notification frequency ([0019] teaches displaying a contact schedule); and automatically operating a communication system to transmit the pre-contact notification to the patient ahead of the target date of the contact according to the pre-contact notification schedule ([0061] teaches provide an alert or other notification/prompting of an event and [0062] teaches when an important date approaches the user will receive e-mail notification of such event).
REGARDING CLAIM 19
Jamison further discloses:
The method of claim 18, wherein the pre-contact notifications are transmitted automatically (Jamison at [0037] teaches automatically sending data (interpreted by examiner as the pre-contact notifications) to contacts).
REGARDING CLAIM 20
Jamison further discloses:
The method of claim 18 further comprising determining the type of pre-contact notification based on the contact information associated with the patient (Jamison at [0060] teaches type of contact).
Response to Arguments
Rejection under 35 U.S.C. § 101
Regarding the rejection of claims 1-11 and 13-20, the Examiner has considered the Applicant’s arguments, but does not find them persuasive. Applicant argues:
Amended claim 1 does not recite a method of managing personal behavior or interactions between people (e.g., social activities, teaching, following rules or instructions). See MPEP 2106.04(a)(2)(II)(C). As amended, Claim 1 recites methods for data processing and analysis rather than managing interaction between people. Claim 1 does not recite rules or instructions for managing human behavior. Instead, claim 1 recites processing data to train a care transition model, utilize the care transition model to analyze data to generate a probabilistic prediction for readmission, and automatically operate a communication system to transmit notifications to a patient..
Regarding 1, The Examiner respectfully disagrees. Claim 1 encompass a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to train a model, receive admission-discharge-transfer (ADT) data, predict a likelihood of readmission, output a readmission profile, assign a placement of the patient in a call list, transmit a notification in the manner described in the identified abstract idea, supra. The rules or instructions are the claimed steps of “training… receiving… predicting… outputting…assigning … and transmitting a notifications” as indicated supra. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People (e.g. social activities, teaching, following rules or instructions)” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The claimed features recited in Example 46 include "automatically operating the sorting gate, by the processor sending a control signal to the sorting gate" and "automatically sending a control signal to the feed dispenser to dispense a therapeutically effective amount of supplemental salt." Appendix 1 at page 32. Appendix 1 states that the recited features integrates the judicial exception into a practical application. In particular, the recited features "[do] not merely link the judicial exception to a technical field, but instead adds a meaningful limitation in that it employs the information provided by the judicial exception... to operate the gate control mechanism and route the animals, thus avoiding the need for the farmer to visually evaluate the behavior of each animal in the herd on a continual basis." Similarly, amended claim 1 recites "automatically operating a communication system to transmit a notification to the patient based on the placement of the patient in the call list." Claim 1 adds a meaningful limitation in that it employs the patient placement in the call list to automatically operate a communication system to transmit a notification to the patient, thus avoiding the need for a care provider to evaluate patient priority and manually contact patients on a continual basis. For example, the processor may automatically operate a telephonic communication system to transmit a pre-recorded message or a text message to the patient. See e.g., Specification at [0079]-[0080].
Regarding 2, the Examiner respectfully disagrees. Applicants claim 1 is unlike that of Example 46. Example 46, claim 2 was found eligible because limitation d adds a meaningful limitation in that it can employ the information provided by the judicial exception (the mental analysis of whether the animal is exhibiting an aberrant behavioral pattern indicative of grass tetany) to operate the feed dispenser. Limitation (d) in combination with the feed dispenser enables the control of appropriate farm equipment based on the automatic detection of grass tetany, which goes beyond merely automating the abstract idea. Using the information obtained via the judicial exception to take corrective action such that the monitoring component is operable to control the feed dispenser in a particular way is an “other meaningful limitation” that integrates the judicial exception into the overall livestock management scheme and accordingly practically applies the exception, such that the claim is not directed to the judicial exception. Moreover, Example 46, claim 3 was found eligible because step d specifies that the processor automatically operates the sorting gate to route the animals in the herd based on the behavior of the animals. not merely link the judicial exception to a technical field, but instead adds a meaningful limitation in that it employs the information provided by the judicial exception (the mental analysis of whether the animal is exhibiting an aberrant behavioral pattern) to operate the gate control mechanism and route the animals, thus avoiding the need for the farmer to visually evaluate the behavior of each animal in the herd on a continual basis. Something physical was controlled in Example 46. Applicants claim 1 merely automates the abstract ideas and does not use the information obtained via the judicial exception to take corrective action. Transmitting a notification is an abstract idea and cannot integrate the abstract idea into a practical application. There is no control of any physical action and sending a notification is mere data transmission.
Rejection under 35 U.S.C. § 102 and 103
Regarding the rejection of claims 1-11 and 13-20, the Examiner has considered the Applicant’s arguments, but does not find them persuasive. Applicant argues:
for example, Zhang does not disclose using insurance data to supplement incomplete historical discharge data and incomplete historical readmission data. See e.g., Zhang at [0104]. Zhang also does not disclose using insurance data as training data to train the care transition model..
Regarding 1, The Examiner respectfully disagrees. Zhang at [0060] teaches that the readmission risk forecasting module can group the patient data for input to the readmission risk forecasting model into three sets of risk factors, where the data is exacted from insurance claims data files associated with the patient in various electronic data sources, which is interpreted by Examiner as insurance data.
Amended claim 9 recites, in part, "determine a priority for the patient based on the likelihood of readmission, wherein the priority represents a relative importance for initiating a follow up with the patient; generate a priority list based on the priority for the patient, wherein the priority list defines an order for patient follow up; and automatically operate a communication system to transmit a notification to the patient based on the priority list." … Zhang fails to disclose this feature.
Regarding 2, The Examiner respectfully disagrees. Zhang discloses predicting a likelihood of readmission for the patient at para. [0016], determining a priority for the patient based on the likelihood of readmission at para. [0076] and placing the patient on a priority list for follow up based on the priority, at para. [0025] with a confidence metric and generating notifications, at para. [0122]. Given the broadest reasonable interpretation, the cited reference teaches the claimed feature.
Kusen does not disclose generating a confidence metric representing the likelihood of successful contact via the contact information or recommending the contact information based on the likelihood of successful contact.
Regarding 3, The Examiner respectfully disagrees. Kusens at [0085] teaches a preferred method of contact stored in the information handling system for the provider and/or patient and that any suitable means of notification may be employed: text, voice, e-mail, and/or paper mail notifications could be sent, which is interpreted by Examiner as means to generate a confidence metric associated with the contact information and a recommendation as to which contact information to use to contact the patient, as the preferred contact automatically means a higher confidence metric is associated with that contact information.
Conclusion
Applicant’s amendment necessitated the new grounds of rejection presented in this Office action. THIS ACTION IS MADE FINAL. See MPEP §706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record though not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Ryan (US 2012/0046965) discloses readmission risk assessment.
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/L.T.K./Examiner, Art Unit 3683
/ROBERT W MORGAN/Supervisory Patent Examiner, Art Unit 3683
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