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
Claims 1, 9, 16 and 20 have been amended.
Claims 1-14 and 16-20 as presented December 10, 2025 are currently pending and considered below.
Claim Objections
Claim 9 is objected to because of the following informalities: “wherein the the processor is part of a wearable electronic device”. Appropriate correction is required. For the purposes of compact prosecution, claim 9 will be interpreted as reading “wherein the
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-6, 8-14 and 16-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-4, 6-11 and 14-16 of U.S. Patent No. 11,682,495 B in view of Miladin (US 2014/0062694 A1) and Basu (US 2018/0203978 A1). Claims 7 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 5 of U.S. Patent No. 11,682,495 B2 in view of Miladin, Basu and Zhang et al (“Kernel-based Conditional Independence Test and Application in Causal Discovery”, Max Planck Institute for Intelligent Systems, 2012). Although the claims at issue are not identical, they are not patentably distinct from each other as shown below.
Instant Application
U.S. Patent No. 11,682,495 B2
1. A structured medical data classification system for classifying structured medical data, with each item of structured medical data comprising one or more fields and one or more values in the one or more respective fields, comprising:
1. A structured medical data classification system for classifying structured medical data for prophylaxis of adverse pregnancy outcomes for a patient, with each item of structured medical data comprising one or more fields and one or more values in the one or more fields, comprising:
an interface configured for periodically measuring patient data comprising a set of medical attributes of the patient, wherein a timing of the measurement of the patient data is based on updates to a risk profile generated for the patient, and wherein selection of medical attributes for measurement of the patient data is based on the updates to the risk profile;
determining that a checkpoint in a treatment timeline for monitoring the adverse pregnancy outcomes for the patient is reached;
based on the checkpoint in the treatment timeline, updating the risk profile of the patient, the risk profile representing the one or more risk factors for adverse pregnancy outcomes for the patient, wherein updating the risk profile comprises:
a processor; and a memory in communication with the processor, the memory storing instructions for performing operations comprising:
at least one processor; and a memory in communication with the processor, the memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
parsing, by the processor, one or more items of structured medical data to retrieve values of respective fields of the one or more items of structured medical data, the one or more retrieved values representing a set of medical attributes of a patient;
parsing one or more items of structured medical data to retrieve values of respective fields of the one or more items of structured medical data, the one or more retrieved values representing a set of medical attributes describing a medical status of the patient;
wherein the set of medical attributes comprise at least one of a depression scale, violence scale, a vitamin use, existence of substance abuse, demographic data, an access of a user interface of a tracking application, an appointment attendance, a mood, and a stress level of the patient, data describing vaginal flora, presence of a sexually transmitted disease, lower genital tract inflammatory milieu during pregnancy, pregnancy history, race, marital status, maternal periconceptional nutritional status, pregnancy nutritional status, approximate blood alcohol level, a count of fetal kicks or contractions, or a smoking status of the patient;
accessing the memory and selecting, from the memory, a classifier based at least one of the medical attributes in the set that are measured, the classifier being trained with training data from one or more other patients, the training data representing a known pregnancy outcome for the one or more other patients in relation to a set of medical attributes describing medical statuses of the one or more other patients;
accessing the memory and selecting, from the memory, the machine learning classifier based at least one of the medical attributes in the set, the machine learning classifier being pre-trained using the training data;
training a machine learning classifier to classify the structured medical data into a risk profile that represents one or more risk factors for adverse pregnancy outcomes for the patient, the machine learning classifier being trained with training data from one or more other patients, the training data representing a known pregnancy outcome for the one or more other patients;
applying the classifier to the set of attributes to classify the one or more items of structured medical data to update the risk profile, the risk profile including a plurality of risk factors for the patient;
applying the machine learning classifier to the set of medical attributes to classify the one or more items of structured medical data into the risk profile that represents the one or more risk factors;
a user interface that renders one or more controls for input of medical confirmation data that confirms one or more of the risk factors of the risk profile; and
generating a user interface that presents one or more controls for input of medical confirmation data, being measured at the checkpoint in the treatment timeline, that confirms one or more of the risk factors of the risk profile;
a transmitter that transmits, over one or more communication protocols and to a remote medical device, an alert that specifies confirmation of the one or more of the risk factors.
transmitting, over one or more communication protocols and to a remote medical device, an alert that specifies confirmation of the one or more of the risk factors, wherein pre-training the machine learning classifier reduces or eliminates a latency of transmitting the alert caused by training the machine learning classifier; and
updating a medical status of the patient based on an outcome of treatment provided to the patient in response to the transmitted alert;
wherein the machine learning classifier is generated by performing graph-learning comprising:
receiving data representing medical attributes of a plurality of patients, wherein the medical attributes comprise the set of medical attributes of the patient;
classifying each of the patients of the plurality of patients into one or more health outcomes; and
generating a graph of nodes and edges, wherein a node represents a medical attribute, and wherein an edge represents a causal relationship between connected medical attributes;
wherein the patient is not included in the plurality of patients, and wherein the graph-learning further comprises:
generating a set of decision trees by performing, for each decision tree of the set, operations comprising:
selecting a subset of the plurality of patients by sampling from the plurality of patients; and
selecting a medical attribute of the subset of the plurality of patients that splits the subset of the plurality of patients into two groups of approximately equal size;
determining, using the set of decision trees, a classification of the set of medical attributes for the patient; and
generating the risk profile of the patient based on the classification of the set of medical attributes for the patient.
2. The system of claim 1, wherein the alert comprises an answer to a question that is customized to address a risk factor of the risk profile of the patient.
2. (Original) The system of claim 1, wherein the alert comprises an answer to a question that is customized to address a risk factor of the risk profile of the patient.
3. The system of claim 1, wherein the classifier is generated by performing graph-learning comprising:
see claim 1
receiving data representing attributes of a plurality of patients, wherein the attributes comprise the set of medical attributes of the patient;
see claim 1
classifying each of the patients of the plurality of patients into one or more health outcomes; and
see claim 1
generating a graph of nodes and edges, wherein a node represents an attribute, and wherein an edge represents a causal relationship between connected attributes.
see claim 1
4. The system of claim 3, wherein the patient is not included in the plurality of patients, and wherein the graph-learning further comprises:
see claim 1
generating a set of decision trees by performing, for each decision tree of the set, operations comprising:
see claim 1
selecting a subset of the plurality of patients by sampling from the plurality of patients; and
see claim 1
selecting an attribute of the subset of the plurality of patients that splits the subset of the plurality of patients into two groups of approximately equal size;
see claim 1
determining, using the set of decision trees, a classification of the set of attributes for the patient; and
see claim 1
generating the risk profile of the patient based on the classification of the set of attributes for the patient.
see claim 1
5. The system of claim 1, wherein the one or more risk factors include a risk of an adverse pregnancy outcome for the patient.
3. The system of claim 1, wherein the one or more risk factors include a risk of an adverse pregnancy outcome for the patient.
6. The system of claim 1, further comprising: updating the classifier based on a reported outcome of treatment provided to the patient in response to the transmitted alert.
4. The system of claim 1, further comprising:
updating the machine learning classifier based on a reported outcome of treatment provided to the patient in response to the transmitted alert.
7. The system of claim 3, further comprising: executing logic representing a kernel conditional independence test to the data representing the attributes of the plurality of patients;
5. The system of claim 1, further comprising: executing logic to test the data representing the medical attributes of the plurality of patients, the test being configured to identify a general dependence or conditional dependence between or among the medical attributes, the test comprising:
applying a linear model to the data representing the attributes of the plurality of patients;
testing a marginal independence of each pair of the medical attributes using a linear model;
and based on application of the kernel conditional independent test and the linear model, generating the classifier.
when the linear model does not detect a correlation between a given pair of medical attributes, applying one or more transformations to each of the medical attributes of the pair of medical attributes to determine whether the correlation exists between the given pair of medical attributes; and generating the machine learning classifier based on determining whether correlations exist between each pair of the medical attributes, the determined correlations providing an indication of the one or more of the risk factors of the risk profile.
8. The system of claim 1, wherein the one or more risk factors include a risk of suicide for the patient.
6. The system of claim 1, wherein the one or more risk factors include a risk of suicide for the patient.
9. The system of claim 1, wherein the the processor is part of a wearable electronic device and wherein the one or more retrieved values comprise measured physiological data from the wearable electronic device.
7. The system of claim 1, further comprising a wearable electronic device and wherein receiving the set of medical attributes comprises receiving physiological data from the wearable electronic device.
10. The system of claim 1, wherein the user interface displays one or more controls enabling the patient to request immediate medical attention.
8. The system of claim 1, wherein the user interface displays one or more controls enabling the patient to request immediate medical attention.
11. The system of claim 10, wherein the immediate medical attention comprises receiving transportation to a medical facility.
9. The system of claim 8, wherein the immediate medical attention comprises receiving transportation to a medical facility.
12. The system of claim 1, wherein the confirmation data comprises answers to one or more medical questions.
10. The system of claim 1, wherein the confirmation data comprises answers to one or more medical questions.
13. The system of claim 1, wherein the set of medical attributes comprises physiological data.
11. The system of claim 1, wherein the set of medical attributes comprises physiological data.
14. The system of claim 1, wherein the set of medical attributes include data representing one or more of vaginal flora, presence of a sexually transmitted disease, lower genital tract inflammatory milieu during pregnancy, pregnancy history, race, marital status, maternal periconceptional nutritional status, pregnancy nutritional status, approximate blood alcohol level, and smoking status.
see claim 1
Claim 1 of U.S. Patent No. 11,682,495 B2 includes the limitations of claim 1 of the instant application except periodically measuring patient data comprising a set of medical attributes of the patient, wherein a timing of the measurement of the patient data is based on updates to a risk profile generated for the patient, and wherein selection of medical attributes for measurement of the patient data is based on the updates to the risk profile.
However, Miladin teaches periodically measuring patient data comprising a set of medical attributes of the patient (e.g. see [0070], [0077]). It would have been obvious to one of ordinary skill in the art to include periodically measuring patient data comprising a set of medical attributes of the patient as taught by Zhang, since this allows for reacting in a timely manner to a change in the patient’s condition (Miladin [0005]).
Miladin does not teach a timing of the measurement of the patient data is based on updates to a risk profile generated for the patient and selection of medical attributes for measurement of the patient data is based on the updates to the risk profile. However, Basu teaches a timing of the measurement of the patient data is based on updates to a risk profile generated for the patient and selection of medical attributes for measurement of the patient data is based on the updates to the risk profile (e.g. see [0102]-[0108]). It would have been obvious to one of ordinary skill in the art to include a timing of the measurement of the patient data is based on updates to a risk profile generated for the patient and selection of medical attributes for measurement of the patient data is based on the updates to the risk profile as taught by Basu, since this allows for an “accurate and continuously updating risk” (Basu [0003]).
Claim 5 of U.S. Patent No. 11,682,495 B2 includes the limitations of claim 7 of the instant application except a kernel conditional independence test.
However, Zhang teaches a kernel conditional independence test (e.g. see abstract, section 1, section 4.2). It would have been obvious to one of ordinary skill in the art to include the kernel conditional independence test as taught by Zhang, since this allows for exploiting more complete information of data (Zhang, section 5).
Claims 16-20 of the instant application recite substantially similar limitations as claims 1-15 of the instant application, and, as such are rejected for similar reasons as given above. See also claims 14-16 of U.S. Patent No. 11,682,495 B2.
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 1-14 and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Claims 1-14 recite a structured medical data classification system for classifying structured medical data, with each item of structured medical data comprising one or more fields and one or more values in the one or more respective fields, which is within the statutory category of a machine. Claims 16-19 recite a method for classifying structured medical data, with each item of structured medical data comprising one or more fields and one or more values in the one or more respective fields, which is within the statutory category of a process. Claim 20 recite a non-transitory computer-readable medium for classifying structured medical data, with each item of structured medical data comprising one or more fields and one or more values in the one or more respective fields, the non-transitory computer readable medium configured to cause one or more processing devices to perform operations, which is within the statutory category of an article of manufacture.
Step 2A - Prong One:
Regarding Prong One of Step 2A, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they "recite" a judicial exception or in other words whether a judicial exception is "set forth" or "described" in the claims. An "abstract idea" judicial exception is subject matter that falls within at least one of the following groupings: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes. Representative independent claim 1 includes limitations that recite at least one abstract idea.
Specifically, independent claim 1 recites: A structured medical data classification system for classifying structured medical data, with each item of structured medical data comprising one or more fields and one or more values in the one or more respective fields, comprising:
an interface configured for periodically measuring patient data comprising a set of medical attributes of the patient, wherein a timing of the measurement of the patient data is based on updates to a risk profile generated for the patient, and wherein selection of medical attributes for measurement of the patient data is based on the updates to the risk profile;
a processor; and a memory in communication with the processor, the memory storing instructions for performing operations comprising:
parsing, by the processor, one or more items of structured medical data to retrieve values of respective fields of the one or more items of structured medical data, the one or more retrieved values representing a set of medical attributes of a patient;
accessing the memory and selecting, from the memory, a classifier based at least one of the medical attributes in the set that are measured, the classifier being trained with training data from one or more other patients, the training data representing a known pregnancy outcome for the one or more other patients in relation to a set of medical attributes describing medical statuses of the one or more other patients;
applying the classifier to the set of attributes to classify the one or more items of structured medical data to update the risk profile, the risk profile including a plurality of risk factors for the patient;
a user interface that renders one or more controls for input of medical confirmation data that confirms one or more of the risk factors of the risk profile; and
a transmitter that transmits, over one or more communication protocols and to a remote medical device, an alert that specifies confirmation of the one or more of the risk factors.
Other than the steps performed by the generic computer components, the underlined limitations are directed to methods of organizing human activity. The claim recites steps of periodically measuring patient data, parsing structured medical data, selecting a classifier, applying the classifier to the set of attributes, inputting medical confirmation data and transmitting an alert specifying confirmation of the risk factors. These steps, under its broadest reasonable interpretation, are categorized as methods of organizing human activity, specifically associated with managing personal behavior or relationships or interactions between people (e.g. automated risk identification and alerting). The claim encompasses a person following rules or instructions to process data in the manner described in the abstract idea. If the 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” grouping of abstract ideas. See MPEP § 2106.04(a). The Examiner further notes that “Certain Methods of Organizing Human Activity” includes a person's interaction with a computer (see October 2019 Update: Subject Matter Eligibility at Pg. 5). The abstract idea for Claims 16 and 20 are identical as the abstract idea for Claim 1, because the only difference between Claim 1 and 16 is that Claim 1 recites a system, whereas Claim 10 recites a method, and because the only difference between Claims 1 and 20 is that Claim 1 recites a system, whereas Claim 20 recites a non-transitory computer readable medium. Any limitation not identified above as part of methods of organizing human activity, are deemed “additional elements” and will be discussed further in detail below. Accordingly, claims 1, 16 and 20 recite at least one abstract idea.
Similarly, dependent claims 2-8, 11-14 and 17-19 further narrow the abstract idea described in the independent claims. Claims 2 and 17 further describe the alert. Claims 3, 4, 7 and 18-19 further describe the classifier. Claims 5 and 8 further describes the risk factors. Claim 6 describes updating the classifier. Claim 12 further describes the confirmation data. Claims 13 and 14 further describe the set of medical attributes. Claims 3, 4, 7 and 18-19 partially narrow the abstract idea as described above, and also introduce additional element(s) which will be discussed in Step 2A Prong 2 and Step 2B. These limitations only serve to further limit the abstract idea and hence, are directed toward fundamentally the same abstract ideas as independent claims 1, 16 and 20, even when considered individually and as an ordered combination.
Step 2A - Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. It must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a "practical application."
In the present case, claims 1-14 and 16-20 as a whole do not integrate the abstract idea into a practical application because they do not impose meaningful limits on practicing the abstract idea. The additional elements or combination of additional elements, beyond the above-noted at least one abstract idea will be described as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the “abstract idea(s)”).
Specifically, independent claim 1 recites: A structured medical data classification system for classifying structured medical data, with each item of structured medical data comprising one or more fields and one or more values in the one or more respective fields, comprising:
an interface configured for periodically measuring patient data comprising a set of medical attributes of the patient, wherein a timing of the measurement of the patient data is based on updates to a risk profile generated for the patient, and wherein selection of medical attributes for measurement of the patient data is based on the updates to the risk profile;
a processor; and a memory in communication with the processor, the memory storing instructions for performing operations comprising:
parsing, by the processor, one or more items of structured medical data to retrieve values of respective fields of the one or more items of structured medical data, the one or more retrieved values representing a set of medical attributes of a patient;
accessing the memory and selecting, from the memory, a classifier based at least one of the medical attributes in the set that are measured, the classifier being trained with training data from one or more other patients, the training data representing a known pregnancy outcome for the one or more other patients in relation to a set of medical attributes describing medical statuses of the one or more other patients;
applying the classifier to the set of attributes to classify the one or more items of structured medical data to update the risk profile, the risk profile including a plurality of risk factors for the patient;
a user interface that renders one or more controls for input of medical confirmation data that confirms one or more of the risk factors of the risk profile; and
a transmitter that transmits, over one or more communication protocols and to a remote medical device, an alert that specifies confirmation of the one or more of the risk factors
The independent claims recites the additional elements of a system, processor, memory, transmitter, user interface, communication protocols, remote medical device and non-transitory computer-readable medium that implement the identified abstract idea. system, processor, memory, transmitter, user interface, communication protocols, remote medical device and non-transitory computer-readable medium are not described by the applicant and are recited at a high-level of generality such that they amounts to no more than mere instructions to apply the exception using a generic computer component. See pg. 28 para. 2 – pg. 30 para. 1 of the specification.
The dependent claims 3-4, 7, 9-10 and 18-19 recite additional element(s) that implement the identified abstract idea. Claims 3-4, 7 and 18-19 recite graph-learning and a kernel conditional independence test. Claim 9 recites a wearable electronic device. Claim 10 describes the user interface and merely narrows the additional element recited in the independent claim. However, these additional elements do not integrate the abstract idea into a practical application because, as stated above, they represent mere instructions to apply the abstract idea on a computer (i.e., merely invoking the computer structure as a tool used to execute the limitations).
Accordingly, the claims as a whole do not integrate the abstract idea into a practical application as they do not impose any meaningful limits on practicing the abstract idea.
Step 2B
Regarding Step 2B, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
When viewed as a whole, claims 1-14 and 16-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite processes that are routine and well-known in the art and simply implements the process on a computer(s) is not enough to qualify as "significantly more."
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a system, processor, memory, transmitter, user interface, communication protocols, remote medical device and non-transitory computer-readable medium to perform the noted steps amount 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”).
The dependent claims 3-4, 7, 9-10 and 18-19 recite additional element(s) that implement the identified abstract idea. Claims 3-4, 7 and 18-19 recite graph-learning and a kernel conditional independence test. Claim 9 recites a wearable computing device. Claim 10 describes the user interface and merely narrow the additional element recited in the independent claim. However, these functions are not deemed significantly more than the abstract idea because, as stated above, they represent mere instructions to apply the abstract idea on a computer (i.e., merely invoking the computer structure as a tool used to execute the limitations).
Therefore, claims 1-14 and 16-20 are rejected under 35 USC §101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 9-14, 16, 17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Shrivastava (US 2018/0052961 A1) in further view of Miladin (US 2014/0062694 A1), Basu (US 2018/0203978 A1) and Salafia (US 2017/0091402 A1).
Regarding claim 1, Shrivastava teaches: A structured medical data classification system for classifying structured medical data, with each item of structured medical data comprising one or more fields and one or more values in the one or more respective fields (system for prediction a health condition of a patient, e.g. see [0001], [0042]-[0043]; data points/features are extracted from medical records comprising notes, test results, observations, etc., including text-based nursing notes which may be structured under headings, e.g. see [0021], [0030], [0057]-[0061], and clinical investigation data and sensor data, e.g. see [0032], [0046], [0060]), comprising:
a processor; and a memory in communication with the processor, the memory storing instructions for performing operations comprising (e.g. see [0044], [0048])
parsing, by the processor, one or more items of structured medical data to retrieve values of respective fields of the one or more items of structured medical data, the one or more retrieved values representing a set of medical attributes of a patient; (the processor is configured to extract data points (i.e. medical attributes) of a patient from the first set of medical records and the second set of medical records comprising notes, test results, medications, personal statistics, vital signs, observations, etc., including parsing words under structured headings in nursing notes, investigation reports and sensor data and assigning importance, e.g. see [0030], [0046], [0057]-[0061])
accessing the memory and selecting, from the memory, a classifier […], the classifier being trained with training data from one or more other patients, […]; (classifiers are trained on specific health conditions on “the set of medical records of the plurality of patients, e.g. see [0056], [0090], [0054]; “the trained one or more classifiers may be stored in the memory 204 for later use” and are accessed for prediction, e.g. see [0085], [0047])
applying the classifier to the set of attributes to classify the one or more items of structured medical data to update the risk profile, the risk profile including a plurality of risk factors for the patient; (predicting the risk of occurrence of the health condition (i.e. risk profile) based on the trained one or more classifiers and the one or more medical records of the patient; the classifier utilizes the extracted data points from the one or more medical records of the patient (the input of data points/features used by the classifier constitute “a plurality of risk factors” that contribute to the risk determination, also see [0057]-[0061]), e.g. see [0034], [0101]-[0102], [0090])
a user interface that renders one or more controls for input of medical […] data […]; and (the display may be configured to render one or more user interface for transmitting or receiving data pertaining to the patient, e.g. medical record data, e.g. see [0052], [0028])
a transmitter that transmits, over one or more communication protocols and to a remote medical device, an alert […]. (an alert generation unit that may generate an alert to an electronic device associated with a doctor or a healthcare professional, e.g. see [0101]; communication occurs over transceivers over a network, e.g. see [0036], [0049])
Shrivastava does not teach:
an interface configured for periodically measuring patient data comprising a set of medical attributes of the patient […]
medical confirmation data that confirms one or more of the risk factors of the risk profile;
an alert that specifies confirmation of the one or more of the risk factors
However, Miladin in the analogous art of health management for a patient (e.g. see [0002]) teaches:
an interface configured for periodically measuring patient data comprising a set of medical attributes of the patient […] (“a portable wireless device having…an interactive remote patient-monitoring module (IRPMM)” that receives patient input, e.g. see [0008], [0011], Fig. 1; the “IRPMM can include connected…data from clinical data modules 120, e.g. a module for taking/measuring or accepting data”, e.g. see [0070]; periodic or daily monitoring of patient data, e.g. see [0077])
medical confirmation data that confirms one or more of the risk factors of the risk profile; (request and receive clinical data and subjective data from the patient (each piece of data entered by the patient is confirmation of that specific’s factor’s status and these factors contribute to the risk profile), e.g. see [0008], [0011], [0015], [0070]; the baseline comparative results and/or indication of severity generated from the patient’s response to a series of questions from the IRPMM can be used to confirm the worsening of a chronic condition (the indication of severity is derived from the confirming the risk factor), e.g. see [0078], [0082], [0012]; [0091] discusses Fig. 12, which illustrates a screen with the summary of the data input by the patient, and Fig. 13 which illustrates a screen for the patient to confirm the summary information (i.e. risk factors))
an alert that specifies confirmation of the one or more of the risk factors (the system’s algorithm/decision trees can provide a certified decision and/or protocol or interventional method of treatment in response to the clinical data input by the patient to a healthcare provider, e.g. see [0012], [0077], [0014]; “the system can include validation for an end-to-end chain of custody with respect to information and/or actions provided, received and/or taken by the patient and/or clinician. For example, the validation for end-to-end chain of custody can include a visual and/or audible signal”, e.g. see [0014], [0019])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Shrivastava to include an interface configured for periodically measuring patient data comprising a set of medical attributes, medical confirmation data that confirms one or more of the risk factors of the risk profile, and an alert that specifies confirmation of the one or more of the risk factors as taught by Miladin, for the purposes of reacting in a timely manner to a change in the patient’s condition (Miladin [0005]).
Shrivastava and Miladin do not teach:
wherein a timing of the measurement of the patient data is based on updates to a risk profile generated for the patient, and wherein selection of medical attributes for measurement of the patient data is based on the updates to the risk profile;
selecting a classifier based at least one of the medical attributes in the set that are measured
However, Basu in the analogous art of machine learning models for predicting a health risk (e.g. see [0003]) teaches:
wherein a timing of the measurement of the patient data is based on updates to a risk profile generated for the patient, and wherein selection of medical attributes for measurement of the patient data is based on the updates to the risk profile; (evaluating data using a machine learning model to generate “one or more risk scores for the user”; based on this evaluation, the system actively adjusts the frequency (i.e. timing) of data collection, e.g. “increase the frequency” of active periodic measurements for the risk of decompensation; furthermore, the system adjust the frequency of specific periodic data features while maintaining or decreasing other features, e.g. the “least informative periodic measurements may be decreased” (i.e. selection of medical attributes), e.g. see [0102]-[0108])
selecting a classifier based at least one of the medical attributes in the set that are measured (measuring a change in the “reflected wave arrival time (RWAT)” and based on the change, selecting a “secondary set of dynamic data classifiers” to evaluate the patient, e.g. see [0100]-[0101])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Shrivastava and Miladin to include a timing of the measurement of the patient data based on updates to a risk profile generated for the patient, selection of medical attributes for measurement of the patient data based on the updates to the risk profile and selecting a classifier based at least one of the medical attributes in the set that are measured as taught by Basu, for the purposes of “provid[ing] an accurate and continuously updating risk” (Basu [0003]) and optimizing data collection to “reduce patient taxation” and “extend the device's battery life” (Basu [0106]).
Shrivastava, Miladin and Basu does not teach:
the training data representing a known pregnancy outcome for the one or more other patients in relation to a set of medical attributes describing medical statuses of the one or more other patients
However, Salafia in the analogous art of predicting a health risk (e.g. see [0006]) teaches:
the training data representing a known pregnancy outcome for the one or more other patients in relation to a set of medical attributes describing medical statuses of the one or more other patients (“The adverse pregnancy risk model will be constructed from data obtained from the sub population”, e.g. see [0061]; “ultrasound and pregnancy data were pooled from approximately 2,335 pregnancies”, e.g. see [0060]; “the invention provides for a database containing information from over 2000 pregnancies…The clinical histories of these pregnancies are available, including adverse outcomes such as premature membrane rupture, preeclampsia, pre-term labor, placental abruption, chronic inflammation, and gestational diabetes mellitus.”, e.g. see [0049]; “The associations of placental metrics from early gestation and delivery, and adverse pregnancy outcomes are used to construct pregnancy risk prediction models based on algorithms”, e.g. see [0050]; “The inventive algorithms can be validated on a reserved test database containing first trimester placental measures and data regarding pregnancy outcomes.”, e.g. see [0053])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Shrivastava, Miladin and Basu to include training data representing a known pregnancy outcome for the one or more other patients in relation to a set of medical attributes describing medical statuses of the one or more other patients as taught by Salafia, for the purposes of “identify[ing] at risk placental growth patterns early in pregnancy, before the pregnancy is obviously clinically compromised” (Salafia [0007]).
Regarding claim 2, Shrivastava, Miladin, Basu and Salafia teach the system of claim 1 as described above.
Shrivastava does not teach:
wherein the alert comprises an answer to a question that is customized to address a risk factor of the risk profile of the patient
However, Miladin in the analogous art teaches:
wherein the alert comprises an answer to a question that is customized to address a risk factor of the risk profile of the patient (the system’s algorithm/decision trees can provide a certified decision and/or protocol or interventional method of treatment in response to the clinical data input by the patient to a healthcare provider, e.g. see [0012], [0077], [0014]; questions for the patient, e.g. peak flow, breathlessness, sputum quantity, can be customized based on baseline data and known conditions, e.g. COPD, CHF; the answers to these questions are used to provide a summary and/or associated indication of severity for each illness in order to determine appropriate intervention(s), and report the indication of severity and/or a suggested intervention protocol for a clinician's review, approval, modification, and the like e.g. see [0078]-[0082])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Shrivastava to include the alert comprises an answer to a question that is customized to address a risk factor of the risk profile of the patient as taught by Miladin, for the purposes of indicating the severity of the illness (Miladin [0082]).
Regarding claim 9, Shrivastava, Miladin, Basu and Salafia teach the system of claim 1 as described above.
Shrivastava does not teach:
wherein the the processor is part of a wearable electronic device and wherein the one or more retrieved values comprise measured physiological data from the wearable electronic
However, Basu in the analogous art teaches:
wherein the the processor is part of a wearable electronic device and wherein the one or more retrieved values comprise measured physiological data from the wearable electronic device (“an example sensor-and-logic system in the form of a wearable electronic device 210…configured to measure, analyze, and/or report one or more health parameters of a wearer…an example of wearable computing device 120, and may be configured as a wrist-worn cardiovascular physiology monitor”, e.g. see [0031]; “Wearable electronic device 210 includes various functional components integrated into primary device 212. In particular, primary device 212 includes a compute system”, e.g. see [0037]; “The compute system 232 includes a data-storage machine 237 to hold data and instructions, and a logic machine 238 to execute the instructions.”, e.g. see [0039]; “Logic machine 816 may include one or more processors configured to execute software instructions.”, e.g. see [0112]; “Wearable computing device 120 may be used to continuously collect dynamically-changing data from the user regarding the user's heart health. For example, wearable computing device 120 may collect data regarding user ll0's heart rate, heart rate variability, pulse pressure wave morphology, and other features”, e.g. see [0025])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Shrivastava to include the computing device comprises a wearable electronic device and wherein receiving the set of attributes comprises receiving physiological data from the wearable electronic device as taught by Basu, for the purposes of “provid[ing] continuous data streams that are accurate without being invasive”(Basu [0030]).
Regarding claim 10, Shrivastava, Miladin, Basu and Salafia teach the system of claim 1 as described above.
Shrivastava does not teach:
wherein the user interface displays one or more controls enabling the patient to request immediate medical attention
However, Miladin in the analogous art teaches:
wherein the user interface displays one or more controls enabling the patient to request immediate medical attention (Fig. 14 illustrates a screen for the patient with additional options for the patient, such as calling a nurse, 911, etc., e.g. see [0091])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Shrivastava to include the user interface displays one or more controls enabling the patient to request immediate medical attention as taught by Miladin, for the purposes of reacting in a timely manner to a change in the patient’s condition (Miladin [0005]).
Regarding claim 11, Shrivastava, Miladin, Basu and Salafia teach the system of claim 10 as described above.
Shrivastava does not teach:
wherein the immediate medical attention comprises receiving transportation to a medical facility
However, Miladin in the analogous art teaches:
wherein the immediate medical attention comprises receiving transportation to a medical facility (requesting a call to 911 (this typically includes emergency transportation to a medical facility), e.g. see [0091], Fig. 14)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Shrivastava to include the immediate medical attention comprises receiving transportation to a medical facility as taught by Miladin, for the purposes of reacting in a timely manner to a change in the patient’s condition (Miladin [0005]).
Regarding claim 12, Shrivastava, Miladin, Basu and Salafia teach the system of claim 1 as described above.
Shrivastava does not teach:
wherein the confirmation data comprises answers to one or more medical questions
However, Miladin in the analogous art teaches:
wherein the confirmation data comprises answers to one or more medical questions (the system’s algorithm/decision trees can provide a certified decision and/or protocol or interventional method of treatment in response to the clinical data input by the patient to a healthcare provider, e.g. see [0012], [0077], [0014]; questions for the patient, e.g. peak flow, breathlessness, sputum quantity, can be customized based on baseline data and known conditions, e.g. COPD, CHF; the answers to these questions are used to provide a summary and/or associated indication of severity for each illness in order to determine appropriate intervention(s), and report the indication of severity and/or a suggested intervention protocol for a clinician's review, approval, modification, and the like, e.g. see [0078]-[0082], e.g. see [0091], Fig. 14)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Shrivastava to include the confirmation data comprises answers to one or more medical questions as taught by Miladin, for the purposes of determining the severity of the illness (Miladin [0082]).
Regarding claim 13, Shrivastava, Miladin, Basu and Salafia teach the system of claim 1 as described above.
Shrivastava further teaches:
wherein the set of medical attributes comprises physiological data (patient data includes physiological parameters, e.g. see [0087]-[0088])
Regarding claim 14, Shrivastava, Miladin, Basu and Salafia teach the system of claim 1 as described above.
Shrivastava does not teach:
wherein the set of medical attributes include data representing one or more of vaginal flora, presence of a sexually transmitted disease, lower genital tract inflammatory milieu during pregnancy, pregnancy history, race, marital status, maternal periconceptional nutritional status, pregnancy nutritional status, approximate blood alcohol level, and smoking status
However, Miladin in the analogous art teaches:
wherein the set of medical attributes include data representing one or more of vaginal flora, presence of a sexually transmitted disease, lower genital tract inflammatory milieu during pregnancy, pregnancy history, race, marital status, maternal periconceptional nutritional status, pregnancy nutritional status, approximate blood alcohol level, and smoking status (the IRPMM is tailored with specific data requests and can ask specific questions such as “Are you still smoking?”, e.g. see [0070]-[0071], [0082])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Shrivastava to include the set of medical attributes as described above and as taught by Miladin, for the purposes of tailoring the system to the individual patient (Miladin [0011]).
Claims 16 and 20 recite substantially similar limitations as those already addressed in claim 1, and, as such are rejected for similar reasons as given above.
Claim 17 recites substantially similar limitations as those already addressed in claim 2, and, as such is rejected for similar reasons as given above.
Claims 3, 7 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Shrivastava, Miladin, Basu and Salafia in further view of Zhang et al (“Kernel-based Conditional Independence Test and Application in Causal Discovery”, Max Planck Institute for Intelligent Systems, 2012).
Regarding claim 3, Shrivastava, Miladin, Basu and Salafia teach the system of claim 1 as described above.
Shrivastava further teaches:
wherein the classifier is generated by […] comprising: receiving data representing attributes of a plurality of patients, wherein the attributes comprise the set of medical attributes of the patient; classifying each of the patients of the plurality of patients into one or more health outcomes; and (generating a classifier involves extracting data points/features (i.e. attributes) from historical data of the plurality of patients, applying risk assessment algorithms trained on historical patient data of the plurality of patients and their outcomes and generating risk predictions, e.g. see [0045]-[0047], [0034])
generating a graph of nodes and edges, wherein a node represents an attribute, and wherein an edge represents a causal relationship between connected attributes
Shrivastava, Miladin, Basu and Salafia do not teach:
performing graph-learning comprising: generating a graph of nodes and edges, wherein a node represents an attribute, and wherein an edge represents a causal relationship between connected attributes
However, Zhang in the analogous art of learning the fundamental structure of relationships within data (e.g. see section 4.2) teaches:
performing graph-learning comprising: generating a graph of nodes and edges, wherein a node represents an attribute, and wherein an edge represents a causal relationship between connected attributes (independence and conditional independence play a central role in causal discovery and Bayesian network learning, e.g. see section 1 para. 1; constraint-based methods like the PC algorithm recover the graph structure by exploiting the conditional independences that can be found in the data; the output is a directed acyclic graph (DAG) representing the true causal structure, where nodes are variables (i.e. attributes), and the learned graph structure implies causal relationships or their absence, e.g. see section 4.2)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Shrivastava, Miladin, Basu and Salafia to include performing graph-learning comprising generating a graph of nodes and edges, wherein a node represents an attribute and an edge represents a causal relationship between connected attributes, as taught by Zhang, for the purposes of applying a method that outperforms existing techniques in accuracy and speed (Zhang, section 5).
Regarding claim 7, Shrivastava, Miladin and Zhang teach the system of claim 3 as described above.
Shrivastava does not teach:
applying a linear model to the data representing the attributes of the plurality of patients; and based on application of the kernel […] and the linear model, generating the classifier (a parameterized similarity measure, which can be a Gaussian kernel, can be used with a logistic regression (i.e. linear model) with similarity values as input features; the system learns parameters of both the similarity function (kernel) and the logistic regression model, e.g. see [0064], [0070])
Shrivastava and Miladin do not teach:
executing logic representing a kernel conditional independence test to the data representing the attributes of the plurality of patients;
However, Zhang in the analogous art teaches:
executing logic representing a kernel conditional independence test to the data representing the attributes of the plurality of patients; (a Kernel-based Conditional Independence Test (KCI-test) is used for testing conditional independence in continuous variables and is applied in Bayesian network learning and causal discovery to recover the graph structure, e.g. see abstract, section 1, section 4.2)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Shrivastava and Miladin to include executing logic representing a kernel conditional independence test to the data representing the attributes of the plurality of patients, as taught by Zhang, for the purposes of exploiting more complete information of data (Zhang, section 5).
Claim 18 recites substantially similar limitations as those already addressed in claim 3, and, as such is rejected for similar reasons as given above.
Claims 4 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Shrivastava, Miladin, Basu, Salafia and Zhang in further view of Fillet (US 2008/0086272 A1).
Regarding claim 4, Shrivastava, Miladin and Zhang teach the system of claim 3 as described above.
Shrivastava, Miladin and Zhang teach graph learning and generating the risk profile of the patient as described above.
Shrivastava further teaches:
wherein the patient is not included in the plurality of patients […] (the classifier generated from training data is used to classify a new patient, e.g. see [0034])
Shrivastava, Miladin and Zhang do not teach:
generating a set of decision trees by performing, for each decision tree of the set, operations comprising: selecting a subset of the plurality of patients by sampling from the plurality of patients; and
selecting an attribute of the subset of the plurality of patients that splits the subset of the plurality of patients into two groups of approximately equal size;
determining, using the set of decision trees, a classification of the set of attributes for the patient
However, Fillet in the analogous art of facilitating medical predictions (e.g. see [0014]) teaches:
generating a set of decision trees by performing, for each decision tree of the set, operations comprising: selecting a subset of the plurality of patients by sampling from the plurality of patients; and (determining a classifier using four or more ensemble decision tree methods, e.g. see [0056], [0015]; each tree of the ensemble can be built from a bootstrap sample drawn from the original learning set, e.g. a sample of the same size as the original sample drawn with replacement from this sample, e.g. see [0057])
selecting an attribute of the subset of the plurality of patients that splits the subset of the plurality of patients into two groups of approximately equal size; (k attributes are selected at random among all candidate input attributes, an optimal split threshold is determined for each one of these and the “best” split is selected among these latter, e.g. see [0058]; candidate tests can be ranked according to score measure that evaluates their capability to discriminate among the different classes, e.g. see [0053])
determining, using the set of decision trees, a classification of the set of attributes for the patient (the classification attributed to a new patient is represented by the majority class among classes predicted by all trees of the ensemble for this patient, e.g. see [0096])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Shrivastava, Miladin, Basu, Salafia and Zhang to include generating a set of decision trees by performing, for each decision tree of the set, selecting a subset of the plurality of patients by sampling from the plurality of patients, selecting an attribute of the subset of the plurality of patients that splits the subset of the plurality of patients into two groups of approximately equal size and determining, using the set of decision trees, a classification of the set of attributes for the patient, as taught by Fillet, for the purposes of improving accuracy in classifying a new patient (Fillet [0096]).
Claim 19 recites substantially similar limitations as those already addressed in claim 4, and, as such is rejected for similar reasons as given above.
Claims 5 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Shrivastava, Miladin, Basu and Salafia in further view of Kivela (US 2007/0173698 A1).
Regarding claim 5, Shrivastava, Miladin, Basu and Salafia teach the system of claim 1 as described above.
Shrivastava, Miladin, Basu and Salafia do not teach:
wherein the one or more risk factors include a risk of an adverse pregnancy outcome for the patient
The claim limitation of an “adverse pregnancy outcome” is interpreted as being an intended use. Applicant is remined that, typically, no patentable distinction is made by an intended use unless some structural difference is imposed by the use on the structure or material recited in the claim, or some manipulative difference is imposed by the use on the action recited in the claim. An intended use generally does not impart a patentable distinction if it merely states an intention or is a description of how the claimed apparatus is to be used (See MPEP 2111.05). For the purposes of compact prosecution, reference Kivela has been cited to teach this limitation.
However, Kivela in the analogous art of patient risk management (e.g. see [0006]) teaches:
wherein the one or more risk factors include a risk of an adverse pregnancy outcome for the patient (a patient’s chief complaint list and a physician’s potential diagnosis list are used to generate a list of fail-safes for a patient, which correspond to potential high-risk diagnoses, e.g. see [0056]-[0057], [0015], [0059]; Table 1 lists “Pregnancy related” as a chief complaint and associated fail-safes; Table 3 lists “Pregnant (ectopic)” as a fail-safe with a weighted rank; Table 2 lists physician potential diagnoses (that are pregnancy related) including abortion, ectopic pregnancy, placenta previa, preeclampsia and pregnancy and associated fail-safes) .
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Shrivastava, Miladin, Basu and Salafia to include one or more risk factors of a risk of an adverse pregnancy outcome for the patient, as taught by Kivela, for the purposes of providing a comprehensive assessment of the patient’s potential diagnoses (Kivela, [0010]).
Regarding claim 8, Shrivastava, Miladin, Basu and Salafia teach the system of claim 1 as described above.
Shrivastava, Miladin, Basu and Salafia do not teach:
wherein the one or more risk factors include a risk of suicide for the patient
The claim limitation of a “risk of suicide” is interpreted as being an intended use. Applicant is remined that, typically, no patentable distinction is made by an intended use unless some structural difference is imposed by the use on the structure or material recited in the claim, or some manipulative difference is imposed by the use on the action recited in the claim. An intended use generally does not impart a patentable distinction if it merely states an intention or is a description of how the claimed apparatus is to be used (See MPEP 2111.05). For the purposes of compact prosecution, reference Kivela has been cited to teach this limitation.
However, Kivela in the analogous art teaches:
wherein the one or more risk factors include a risk of suicide for the patient (a patient’s chief complaint list and a physician’s potential diagnosis list are used to generate a list of fail-safes for a patient, which correspond to potential high-risk diagnoses, e.g. see [0056]-[0057], [0015], [0059]; Table 3 lists “Suicidality assessed” as a fail-safe with a weighted rank; Table 2 lists physician potential diagnoses including “Suicide gesture” and “Suicide ideation/attempt” and associated fail-safes).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Shrivastava, Miladin, Basu and Salafia to include one or more risk factors of a risk of suicide for the patient for the patient, as taught by Kivela, for the purposes of providing a comprehensive assessment of the patient’s potential diagnoses (Kivela, [0010]).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Shrivastava, Miladin, Basu and Salafia in further view of Eshelman (US 2015/0006088 A1).
Regarding claim 6, Shrivastava, Miladin, Basu and Salafia teach the system of claim 1 as described above.
Shrivastava does not teach:
updating the classifier based on a reported outcome of treatment provided to the patient in response to the transmitted alert
Shrivastava, Miladin, Basu and Salafia do not teach:
updating the classifier based on a reported outcome of treatment provided to the patient in response to the transmitted alert
However, Eshelman in the analogous art of predicting physiological changes (e.g. see [0001]) teaches:
updating the classifier based on a reported outcome of treatment provided to the patient in response to the transmitted alert (generate and/or update the VIX models in response to an event such as a periodic time event, a user input event and the availability of new historical patient data, and so on, e.g. see [0039]; generate and/or update the VIX classifiers in response to an event such as a periodic time event, a user input event and the availability of new historical patient data, and so on, e.g. see [0042]; “Outcome data includes data indicating the outcome of a patient's medical treatments and/or stay in the medical institution, such as whether the patient's condition worsened or improved, whether the patient died, and so on. Typically, the outcome data is generated during the patient's stay at a medical institution after medical interventions are taken.”, e.g. see [0030]; the historical patient data used for updating the VIX models and VIX classifiers include outcome data indicating whether the patient was unstable (i.e. based on a reported outcome of treatment provided to the patient), e.g. see [0039], [0042]; the clinical decision support system (CDSS) monitors patients and generates an alert in response to determining the patient is deteriorating, e.g. see [0033], [0009]-[0010]; clinicians take corrective action or provide medical interventions; the outcome data is generated after medical interventions are taken (i.e. in response to the transmitted alert), e.g. see [0030], [0002]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Shrivastava, Miladin, Basu and Salafia to include updating the classifier based on a reported outcome of treatment provided to the patient in response to the transmitted alert, as taught by Eshelman, for the purposes of improving patient outcomes (Eshelman, [0002]).
Response to Arguments
Regarding the rejection under 35 U.S.C. § 101 of Claims 1-14 and 16-20, the Examiner has considered the Applicant’s arguments; however the arguments are not persuasive.
Applicant argues the claims do not fall under the abstract idea of “certain methods of organizing human activity”.
The Examiner respectfully disagrees. The collection, analysis and display of data, including medical data, constitute an abstract idea. The core focus of claim 1 is the process if gathering patient attributes, applying a classifier to evaluate risk based on known outcomes and outputting an alert. These are steps of a method of medical data analysis that a human could perform. Using an interface, processor and transmitter simply automates this abstract process.
Applicant argues the claims integrate the judicial exception into a practical application.
The Examiner respectfully disagrees. Applicant misapplies the Vanda guidance to argue that the claims integrate the judicial exception into practical application by "practically apply natural relationships" . The claims in Vanda explicitly recited a method of treatment that required a step of administering a specific dosage of a drug based on the outcome of a diagnostic test. In contrast, the instant claims lack any active step of treating the patient’s underlying condition. Merely alerting a remote device of a confirmed risk factor does not transform the data analysis into a method of treatment. In addition, the Applicant argues the claims provide a technical improvement to the field of data storage and retrieval by tailoring data measurements timing and type based on the risk profile. However, adjusting when and what data to collect based on an evaluated risk is merely applying a rule to the data collection process, not a technological improvement to the functioning of the computer. The claims rely on entirely generic computer components of an interface, processor, memory an transmitter to perform standard functions of measuring, parsing, storing and sending data. Hence, the computer is merely used as a tool to solve a problem in medical monitoring, and the claims fail to integrate the judicial exception into a practical application.
Regarding the rejection under 35 U.S.C. § 103 of Claims 1-14 and 16-20, the Examiner has considered the Applicant’s arguments; however, these arguments are moot given the new grounds of rejection as necessitated by amendment.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, 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 extension fee 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Aaisha Abdullah whose telephone number is (571)272-5668. The examiner can normally be reached on Monday through Friday 8:00 am - 5:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter H Choi can be reached on (469) 295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
/A.A./Examiner, Art Unit 3686
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681