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 1-27 are pending.
This is in response to documentation submitted on 09/21/2022.
Priority
This application claims the benefit of the U.S. Provisional Patent Application No. 63/247,044. The effective filing date for the present application is recognized as 09/22/2021.
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 1-27 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.
Step 1 Analysis:
Claims 1-27 are directed to a method, apparatus, or system for tracking and encouraging adherence to healthcare instructions and therefore falls into one of the four statutory categories.
(Step 1: Yes, the claim falls into one of the four statutory categories).
Step 2A analysis: Prong one:
The independent Claims 1, 10, and 19, taking Claim 1 as example, recite the following limitations:
…identifying electronic data that is longitudinal and includes a set of electronic records pertaining to a given subject or to a given object, wherein each electronic record of the set of electronic records includes a timestamp and identifies an observation made by, process performed by, or diagnosis made by a verified entity across a predefined time period of at least six months; generating a record-size metric that characterizes a size of the electronic data; determining a physical attribute of the given subject or the given object, wherein the physical attribute corresponds to a size, a dimension, weight or age; generating a physical-attribute metric based on the physical attribute; generating an input data set that includes the record-size metric and the physical-attribute metric; generating a failure probability across a given time period of at least one week and for the given subject or the given object by processing the input data set…; determining that an alert condition is satisfied based on the failure probability; and in response to determining that the alert condition is satisfied, outputting an alert representing the failure probability.
The above claim describes a method for identifying data, generating data, determining an alert condition is satisfied based on a probability, and outputting an alert.
Dependent claims 2-9, 11-18, and 20-27 are directed towards:
Identifying the total amount of electronic records in a set of electronic records
Identifying unique electronic records in a set of electronic records
Age being a physical attribute
Collecting one or more vital sign measurements using a sensor and generating a metric for that data
An alert output being on a device that includes a sensor
Collecting at least one movement measurements that use a sensor attached or worn by a subject or object and generating a movement metric based on that data
Collecting one or more vital signs using a sensor and determining that an additional alert condition is satisfied based on the vital sign measurements, and outputting an alert
Accordingly, the claims recite “certain methods of organizing human activity,” which falls within the judicial exception of an abstract idea. (Step 2A – Prong one: Yes, the claim is abstract).
Step 2A analysis: Prong two:
Claims 1, 5, 6, 7, 8, 9, 14, 15, 16, 17, 18, 23, 24, 25, 26, and 27 recite additional elements beyond the abstract idea. The judicial exception is not integrated into a practical application. Claim 1 recites a trained machine-learning model, Claims 6, 8, 15, 17, 24, and 26 recite “a device,” Claims 5, 7, 9, 14, 16, 18, 23, 25, and 27 recite “a sensor,” Claims 10 and 19 recite “one or more processors” and “a non-transitory computer readable storage medium.”
These additional elements are recited at a high level of generality (i.e., as a generic processor performing generic computer functions), such that it amounts to no more than mere instructions to apply the exceptions using a generic computer component.
The applicant’s specification indicates “a trained machine-learning model” as something that is used to “to process aggregate data based on electronic record data and potential sensor data” [0002]
The applicant’s specification indicates “a device” as something that is “associated with the object or subject to monitor electronic data” [0046]
The applicant’s specification indicates “a sensor” as something that is “attached to or worn by the given subject or the given object” [0011]
The applicant’s specification indicates “one or more data processors” as something that can “perform a set of actions” [0017]
The applicant’s specification indicates a non-transitory computer readable storage medium” as something that includes “instructions configured to cause one or more data processors to perform a set of actions” [0017]
Accordingly, these additional elements when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore Claims 1, 5, 6, 7, 8, 9, 14, 15, 16, 17, 18, 23, 24, 25, 26, and 27 are directed to an abstract idea without practical application. (Step 2A – Prong two: No, the additional elements are not integrated into a practical application).
Step 2B
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements: (1) do not improve the functioning of a computer or any other technology or technical field, and (2) merely use the additional elements for implementing the abstract idea.
A. Improvements to the Functioning of a Computer or To Any Other Technology or Technical
Field. MPEP 2106.05(a)
The additional elements of Claims 1, 5, 6, 7, 8, 9, 14, 15, 16, 17, 18, 23, 24, 25, 26, and 27 do not integrate the abstract idea into a practical application and only use those elements for performing the abstract idea and not more than the judicial exception itself. None of the claims recite an “inventive concept” because the additional elements fail to improve the functioning of a computer or any other technology or technical field (See MPEP 2106.05(a)).
The applicant’s specification indicates “a trained machine-learning model” as something that is used to “to process aggregate data based on electronic record data and potential sensor data” [0002]
The applicant’s specification indicates “a device” as something that is “associated with the object or subject to monitor electronic data” [0046]
The applicant’s specification indicates “a sensor” as something that is “attached to or worn by the given subject or the given object” [0011]
The applicant’s specification indicates “one or more data processors” as something that can “perform a set of actions” [0017]
The applicant’s specification indicates a non-transitory computer readable storage medium” as something that includes “instructions configured to cause one or more data processors to perform a set of actions” [0017]
The claimed additional elements are used in a conventional manner to aggregate data, collect data, monitor data, etc., and the specification does not disclose any specific technical improvement in how these functions are performed.
The use of a computer or processor to merely automate or implement the abstract idea cannot provide significantly more than the abstract idea itself. There is no indication that the additional limitations alone or in combination improves the functioning of a computer or any other technology, improves another technology or technical field, or effects a transformation or reduction of a particular article to a different state or thing.
There is no indication in the claims or specification that the invention improves the performance, efficiency, security, or functionality of a computer system beyond using generic components to perform a judicial exception. The claims do not improve how computers store, retrieve, transmit, or process data, nor do they enhance any other technical field.
Thus, because the claims fail to recite an improvement to the functioning of a computer or any other technology or technical field, they do not amount to significantly more than the judicial exception itself.
B. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
The additional elements of Claims 1, 5, 6, 7, 8, 9, 14, 15, 16, 17, 18, 23, 24, 25, 26, and 27 do not integrate the abstract idea into a practical application and only use those elements for performing the abstract idea and mere instructions to perform the abstract idea using a computer is not sufficient to amount to significantly more than the abstract idea (See MPEP 2106.05(f)).
The requirement to execute the claimed steps/functions using one or more processors, etc., are equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer.
Similarly, the limitations of using a machine-learning mode, a sensor, a device etc., (Claims 1-27) are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (See MPEP 2106.05(f)). (Step 2B: No, the claims do not provide significantly more).
In conclusion, the claims are directed to the abstract idea for managing data and to aggregate data, collect data, monitor data, etc., The claims do not provide an inventive concept, because the claims do not recite additional elements or a combination of elements that amount to significantly more than the judicial exception of the claims. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology or contains instructions to implement the judicial exception, and the collective functions merely provide conventional computer implementation. Therefore, whether taken individually or as an ordered combination, the claims are nonetheless rejected under 35 U.S.C. 101 as being directed to nonstatutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 1-6, 9-15, 18-25, and 27 are rejected under 35 U.S.C 103 as being unpatentable over US Patent Publication No. 20220068432 (“Ruderfer”) in view of US Patent Publication No. 20220199260 (Katsuki) in view of US Patent No. 11860834 (“Bassov”) in view of US Patent Publication No 20200069973 (“Lou”) in view of US Patent No. 12260370 (“Melancon”).
With respect to Claim 1,
Ruderfer teaches A computer-implemented method comprising: identifying electronic data that is longitudinal and includes a set of electronic records pertaining to a given subject or to a given object, (“A method of evaluating electronic health record data to identify genetic disorders, the method comprising: accessing, from a non-transitory computer-readable memory, electronic health record data for a patient…) (Ruderfer [Claim 1]) (Examiner note: Method to identify genetic disorders based on electronic health record data is interpreted as identifying electronic data that is longitudinal) and (“Systems and methods described in this disclosure identify patients for genetic testing based on longitudinal clinical data in their electronic health record (EHR).”) (Ruderfer [0005]) (Examiner note: Data is longitudinal)
Ruderfer does not teach the limitation taught by Katsuki
Katsuki teaches wherein each electronic record of the set of electronic records includes a timestamp and identifies an observation made by, process performed by, or diagnosis made by a verified entity… (“The method includes receiving an electronic health record (EHR) including a plurality of pairs of observation variables and corresponding timestamps.”) (Katsuki [Abstract])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as taught by Ruderfer to an electronic health record with timestamps and an observation made, process performed by, or diagnosis made by a verified entity as taught by Katsuki with the motivation to improve the modeling of patient health history (Katsuki [0002]). In the combination of elements, it would have been obvious to one of ordinary skill in the art to include an electronic health record with timestamps and an observation made, process performed by, or diagnosis made by a verified entity.
Ruderfer teaches …across a predefined time period of at least six months; (“…patients with a record length of at least four years.”) (Ruderfer [0028]) (Examiner note: A record length of at least four years is interpreted as a predefined time period of at least six months)
Ruderfer does not teach the following limitation taught by Bassov
Bassov teaches generating a record-size metric that characterizes a size of the electronic data; (“The said determination also comprises determining a total number of primary and replica files…) (Bassov [Col. 15, Lines 64-66]) (Examiner note: Determining a total number of primary and replica files is interpreted as generating a record-metric size)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as taught by Ruderfer to include the generation of a record-size metric as taught by Bassov with the motivation to improve storage efficiency and the techniques for reporting the space savings that result from data reduction techniques (Bassov [Background]). In the combination of elements, it would have been obvious to one of ordinary skill in the art to add the generation of a record-size metric.
Ruderfer teaches determining a physical attribute of the given subject or the given object, wherein the physical attribute corresponds to a size, a dimension, weight or age; (The HER data contains observable characteristics of the individual patient (e.g., a “phenotype”) (Ruderfer [0049]) and (“…the input data set for the AI model may be generated based on or including data items such as …demographic information (e.g., age, sex, race, markers of socio-economic status, etc.)”) (Ruderfer [0054])
generating a physical-attribute metric based on the physical attribute; (“…the input data set for the AI model may be generated based on or including data items such as …demographic information (e.g., age, sex, race, markers of socio-economic status, etc.)”) (Ruderfer [0054])
Ruderfer does not teach the limitation taught by Bassov
generating an input data set that includes the record-size metric and…. (“At 610, when a given allocation unit in a storage system matches one or more predefined patterns, then (i) setting a corresponding pattern flag for said given allocation unit, and (ii) incrementing at least one pattern counter.”) (Bassov [Col. 15, Lines 47-50]) (Examiner note: The incremental integer counter counting a number of records that are considered a duplicate is interpreted as an input data set for a record-size metric)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as taught by Ruderfer to include the generation of an input data set that includes a record-size metric as taught by Bassov with the motivation to improve storage efficiency and the techniques for reporting the space savings that result from data reduction techniques (Bassov [Background]). In the combination of elements, it would have been obvious to one of ordinary skill in the art to add the generation of an input data set that includes a record-size metric.
Ruderfer teaches Generating an input data set that includes …the physical-attribute metric; (“…the input data set for the AI model may be generated based on or including data items such as …demographic information (e.g., age, sex, race, markers of socio-economic status, etc.)”) (Ruderfer [0054])
Ruderfer does not teach the limitation taught by Lou
Lou teaches generating a failure probability across a given time period of at least one week (“The method of claim 1 wherein displaying the report comprises displaying the dose as a suggested dose with an estimated failure probability for the suggested dose and further comprises displaying a physician prescribed dose and an estimated failure probability for the prescribed dose.”) (Lou [Claim 10]) (Examiner note: The estimated failure probability for the suggested does is interpreted as the generated failure probability) and (“Calibration curves may be obtained by plotting the average predicted probability at 1, 2, or 3 years after radiation treatment.”) (Lou [0156]) (Examiner note: Failure probability is given over 1, 2, or 3 years is interpreted as a failure probability over a time period of at least one week) and (“The generator is trained to generate an output, such as an outcome prediction.”) (Lou [0050]) (Examiner note: The generated outcome prediction is interpreted as the failure probability)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as taught by Ruderfer to include the generation of a failure probability across a time period as taught by Lou with the motivation to aid in the selection of treatments based on specific characteristics of a patient and their disease to avoid treatment resistance and recurrence (Lou [0002]). In the combination of elements, it would have been obvious to one of ordinary skill in the art to add the generation of a failure probability across a time period.
Ruderfer teaches …and for the given subject or the given object by processing the input data set using a trained machine-learning model; (“A trained artificial intelligence model is then applied to the input data set…”) (Ruderfer [0006])
Ruderfer does not teach the limitation taught by Melancon
Melancon teaches determining that an alert condition is satisfied based on the failure probability; (“Assuming, for example, precision value is 0.8, and 50% of the sample having a precision value greater than 0.8 correctly predicted failures, the probability of failure may be measured as 50% when operating at a precision value of 0.8. The calculated probability of a failure occurring based on the values of the prediction features is a failure risk score, as described above. In addition, prediction system 110 may generate and display a visualization of the contribution of each prediction feature to the probability that an alert will result in a service level failure, which may be used to identify which supply chain systems need to be adjusted to avoid the service level failure.) (Melancon [Col. 17-18, Lines 60-4]) (Examiner note: The alert given if a supply chain system needs to be adjusted is interpreted as an alert condition based on the failure probability)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as taught by Ruderfer to include an alert condition satisfaction based on the failure probability as taught by Melancon with the motivation to achieve customer service level targets (Melancon [Background]). In the combination of elements, it would have been obvious to one of ordinary skill in the art to add an alert condition satisfaction based on failure probability.
Ruderfer does not teach the limitation taught by Melancon
Melancon teaches and in response to determining that the alert condition is satisfied, outputting an alert representing the failure probability. (“Assuming, for example, precision value is 0.8, and 50% of the sample having a precision value greater than 0.8 correctly predicted failures, the probability of failure may be measured as 50% when operating at a precision value of 0.8. The calculated probability of a failure occurring based on the values of the prediction features is a failure risk score, as described above. In addition, prediction system 110 may generate and display a visualization of the contribution of each prediction feature to the probability that an alert will result in a service level failure, which may be used to identify which supply chain systems need to be adjusted to avoid the service level failure.) (Melancon [Col. 17-18, Lines 60-4]) (Examiner note: The alert given to the display if a supply chain system needs to be adjusted is interpreted as outputting an alert representing the failure probability)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as taught by Ruderfer to include an alert representing the failure probability as taught by Melancon with the motivation to achieve customer service level targets (Melancon [Background]). In the combination of elements, it would have been obvious to one of ordinary skill in the art to add an alert representing the failure probability.
With respect to Claim 2,
Ruderfer, Katsuki, Bassov, Lou, and Melancon teach the limitations of Claim 1
Ruderfer does not teach the limitation taught by Bassov
Bassov teaches wherein the record-size metric identifies a total quantity of electronic records in the set of electronic records. (“The said determination also comprises determining a total number of primary and replica files…) (Bassov [Col. 15, Lines 64-66]) (Examiner note: Determining the total number of primary files and replica files is interpreted as identifying the total quantity of electronic records)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as taught by Ruderfer to include the determination of total quantity of electronic records as taught by Bassov with the motivation to improve storage efficiency and the techniques for reporting the space savings that result from data reduction techniques (Bassov [Background]). In the combination of elements, it would have been obvious to one of ordinary skill in the art to add the determination of the total quantity of electronic records.
With respect to Claim 3,
Ruderfer, Katsuki, Bassov, Lou, and Melancon teach the limitations of Claim 1
Ruderfer does not teach the limitation taught by Bassov
Bassov teaches wherein the record-size metric identifies a quantity of unique electronic records in the set of electronic records. (“The said determination also comprises determining a total number of primary and replica files…) (Bassov [Col. 15, Lines 64-66]) (Examiner note: Determining the total number of primary files and replica files is interpreted as identifying the quantity of unique electronic records because if the number of replica files is subtracted from the total number of files it outputs the number of unique files or files that do not have replicas)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as taught by Ruderfer to include the determination of total quantity of unique electronic records as taught by Bassov with the motivation to improve storage efficiency and the techniques for reporting the space savings that result from data reduction techniques (Bassov [Background]). In the combination of elements, it would have been obvious to one of ordinary skill in the art to add the determination of the total quantity of unique electronic records.
With respect to Claim 4,
Ruderfer, Katsuki, Bassov, Lou, and Melancon teach the limitations of Claim 1
Ruderfer teaches wherein the physical attribute identifies an age (“…identified four patients having identical age, sex, race, number of unique years in which the patient had visited VUMC, and the closest EHR record length in days (maximum of 100 days difference).”) (Ruderfer [0024])
With respect to Claim 5,
Ruderfer, Katsuki, Bassov, Lou, and Melancon teach the limitations of Claim 1
Ruderfer does not teach the limitation taught by Lou
Lou teaches collecting one or more vital-sign measurements using a sensor that is attached to or worn by the given subject or the given object; and generating a vital-sign metric based on the vital-sign measurements, wherein the input data set includes the vital-sign metric. (“In act 41, the image processor acquires non-image data. The non-image data is from sensors, the computerized patient medical record, manual input, pathology database, laboratory database, and/or other source. The non-image data represents one or more characteristics of the patient, such as family history, medications taken, temperature, body-mass index, and/or other information.) (Lou [0071]) (Examiner note: Non-image data acquired by a sensor such as temperature is interpreted as vital-sign measurements and metrics) and (“Non-image data may be input instead or in addition to scan data.”) (Lou [0070]) (Examiner note: Non-image data being used as input is interpreted as input data including vital-sign metric data)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as taught by Ruderfer to include the collection of vital-sign measurements, generating a vital-sign metric and input data including vital-sign metrics as taught by Lou with the motivation to aid in the selection of treatments based on specific characteristics of a patient and their disease to avoid treatment resistance and recurrence (Lou [0002]). In the combination of elements, it would have been obvious to one of ordinary skill in the art to add the collection of vital-sign measurements, the generation of a vital-sign metric and input data including vital-sign metrics.
With respect to Claim 6,
Ruderfer, Katsuki, Bassov, Lou, and Melancon teach the limitations of Claim 5
Ruderfer does not teach the limitation taught by Lou
Lou teaches wherein the alert is output on a device that includes the sensor. (FIG. 3 shows an example method for machine training decision support in a medical therapy system. The method is implemented by a machine (e.g., computer, processor, workstation, or server) using training data (e.g., samples and ground truths for the samples) in a memory.”) (Lou [0049]) and (“Examples provided for training of FIG. 3 may result in a trained generator for outcome usable in application of FIG. 4. Conversely, examples discussed in conjunction with the application of FIG. 4 may be trained as part of the training of FIG. 3.”) (Lou [0048]) (Examiner note: Figure 4 depicts acquiring non-image data i.e. vital sign data and displaying an output)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as taught by Ruderfer to include an output on a device that includes the sensor as taught by Lou with the motivation to aid in the selection of treatments based on specific characteristics of a patient and their disease to avoid treatment resistance and recurrence (Lou [0002]). In the combination of elements, it would have been obvious to one of ordinary skill in the art to add an output on a device that includes the sensor.
With respect to Claim 9,
Ruderfer, Katsuki, Bassov, Lou, and Melancon teach the limitations of Claim 1
collecting one or more vital-sign measurements using a sensor that is attached to or worn by the given subject or the given object; determining that an additional alert condition is satisfied based on the vital-sign measurements; (“In act 41, the image processor acquires non-image data. The non-image data is from sensors, the computerized patient medical record, manual input, pathology database, laboratory database, and/or other source. The non-image data represents one or more characteristics of the patient, such as family history, medications taken, temperature, body-mass index, and/or other information.) (Lou [0071]) (Examiner note: Non-image data acquired by a sensor such as temperature is interpreted as vital-sign measurements and metrics)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as taught by Ruderfer to include the collection of vital-sign measurements and generating a vital-sign metric as taught by Lou with the motivation to aid in the selection of treatments based on specific characteristics of a patient and their disease to avoid treatment resistance and recurrence (Lou [0002]). In the combination of elements, it would have been obvious to one of ordinary skill in the art to add the collection of vital-sign measurements and generation of a vital-sign metric.
and in response to determining that the additional alert condition is satisfied, outputting another alert. (“Distribution centers 156 may be any suitable entity that offers to store or otherwise distribute at least one product to one or more retailers 158 and/or customers. Distribution centers 156 may, for example, receive a product from a first one or more supply chain entities 150 in supply chain network 100 and store and transport the product for a second one or more supply chain entities 150. Distribution centers 156 may comprise automated warehousing systems 157 that automatically remove products from and place products into inventory based, at least in part, on one or more predicted supply chain failures, one or more corrective actions initiated to prevent a predicted failure, one or more alerts, and/or one or more other factors described herein.”) (Melancon [Col. 6, Lines 45-57]) (Examiner note: One or more corrective actions initiated to prevent a predicted failure and one or more alerts is interpreted an additional alert condition and outputting an additional alert)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as taught by Ruderfer to include an additional alert condition satisfaction based on the failure probability as taught by Melancon with the motivation to achieve customer service level targets (Melancon [Background]). In the combination of elements, it would have been obvious to one of ordinary skill in the art to add an additional alert condition satisfaction and output an additional alert.
With respect to Claim 10,
Ruderfer teaches A system comprising: one or more data processors; and a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform a set of actions including: (“ The electronic health record system includes an electronic processor 705 and a non-transitory computer-readable memory 707. In some implementations, the memory 707 stores computer-executable instructions that are accessed and executed by the electronic processor 705 to provide various functionality of the electronic health record system 703.”) (Ruderfer [0046])
identifying electronic data that is longitudinal and includes a set of electronic records pertaining to a given subject or to a given object, (“Systems and methods described in this disclosure identify patients for genetic testing based on longitudinal clinical data in their electronic health record (EHR).”) (Ruderfer [0005]) (Examiner note: Data is longitudinal)
wherein each electronic record of the set of electronic records includes a timestamp and identifies an observation made by, process performed by, or diagnosis made by a verified entity… (“The method includes receiving an electronic health record (EHR) including a plurality of pairs of observation variables and corresponding timestamps.”) (Katsuki [Abstract])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as taught by Ruderfer to an electronic health record with timestamps and an observation made, process performed by, or diagnosis made by a verified entity as taught by Katsuki with the motivation to improve the modeling of patient health history (Katsuki [0002]). In the combination of elements, it would have been obvious to one of ordinary skill in the art to include an electronic health record with timestamps and an observation made, process performed by, or diagnosis made by a verified entity.
Ruderfer teaches …across a predefined time period of at least six months; (“…patients with a record length of at least four years.”) (Ruderfer [0028]) (Examiner note: A record length of at least four years is interpreted as a predefined time period of at least six months)
Ruderfer does not teach the following limitation taught by Bassov
Bassov teaches generating a record-size metric that characterizes a size of the electronic data; (“The said determination also comprises determining a total number of primary and replica files…) (Bassov [Col. 15, Lines 64-66]) (Examiner note: Determining a total number of primary and replica files is interpreted as generating a record-metric size)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as taught by Ruderfer to include the generation of a record-size metric as taught by Bassov with the motivation to improve storage efficiency and the techniques for reporting the space savings that result from data reduction techniques (Bassov [Background]). In the combination of elements, it would have been obvious to one of ordinary skill in the art to add the generation of a record-size metric.
Ruderfer teaches determining a physical attribute of the given subject or the given object, wherein the physical attribute corresponds to a size, a dimension, weight or age; (The HER data contains observable characteristics of the individual patient (e.g., a “phenotype”) (Ruderfer [0049]) and (“…the input data set for the AI model may be generated based on or including data items such as …demographic information (e.g., age, sex, race, markers of socio-economic status, etc.)”) (Ruderfer [0054])
generating a physical-attribute metric based on the physical attribute; (“…the input data set for the AI model may be generated based on or including data items such as …demographic information (e.g., age, sex, race, markers of socio-economic status, etc.)”) (Ruderfer [0054])
Ruderfer does not teach the limitation taught by Bassov
generating an input data set that includes the record-size metric and…. (“At 610, when a given allocation unit in a storage system matches one or more predefined patterns, then (i) setting a corresponding pattern flag for said given allocation unit, and (ii) incrementing at least one pattern counter.”) (Bassov [Col. 15, Lines 47-50]) (Examiner note: The incremental integer counter counting a number of records that are considered a duplicate is interpreted as an input data set for a record-size metric)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as taught by Ruderfer to include the generation of an input data set that includes a record-size metric as taught by Bassov with the motivation to improve storage efficiency and the techniques for reporting the space savings that result from data reduction techniques (Bassov [Background]). In the combination of elements, it would have been obvious to one of ordinary skill in the art to add the generation of an input data set that includes a record-size metric.
Ruderfer teaches Generating an input data set that includes the physical-attribute metric; (“…the input data set for the AI model may be generated based on or including data items such as …demographic information (e.g., age, sex, race, markers of socio-economic status, etc.)”) (Ruderfer [0054])
Ruderfer does not teach the limitation taught by Lou
Lou teaches generating a failure probability across a given time period of at least one week (“The method of claim 1 wherein displaying the report comprises displaying the dose as a suggested dose with an estimated failure probability for the suggested dose and further comprises displaying a physician prescribed dose and an estimated failure probability for the prescribed dose.”) (Lou [Claim 10]) (Examiner note: The estimated failure probability for the suggested does is interpreted as the generated failure probability) and (“Calibration curves may be obtained by plotting the average predicted probability at 1, 2, or 3 years after radiation treatment.”) (Lou [0156]) (Examiner note: Failure probability is given over 1, 2, or 3 years is interpreted as a failure probability over a time period of at least one week) and (“The generator is trained to generate an output, such as an outcome prediction.”) (Lou [0050]) (Examiner note: The generated outcome prediction is interpreted as the failure probability)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as taught by Ruderfer to include the generation of a failure probability across a time period as taught by Lou with the motivation to aid in the selection of treatments based on specific characteristics of a patient and their disease to avoid treatment resistance and recurrence (Lou [0002]). In the combination of elements, it would have been obvious to one of ordinary skill in the art to add the generation of a failure probability across a time period.
Ruderfer teaches …and for the given subject or the given object by processing the input data set using a trained machine-learning model; (“A trained artificial intelligence model is then applied to the input data set…”) (Ruderfer [0006])
Ruderfer does not teach the limitation taught by Melancon
Melancon teaches determining that an alert condition is satisfied based on the failure probability; (“Assuming, for example, precision value is 0.8, and 50% of the sample having a precision value greater than 0.8 correctly predicted failures, the probability of failure may be measured as 50% when operating at a precision value of 0.8. The calculated probability of a failure occurring based on the values of the prediction features is a failure risk score, as described above. In addition, prediction system 110 may generate and display a visualization of the contribution of each prediction feature to the probability that an alert will result in a service level failure, which may be used to identify which supply chain systems need to be adjusted to avoid the service level failure.) (Melancon [Col. 17-18, Lines 60-4]) (Examiner note: The alert given if a supply chain system needs to be adjusted is interpreted as an alert condition based on the failure probability)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as taught by Ruderfer to include an alert condition satisfaction based on the failure probability as taught by Melancon with the motivation to achieve customer service level targets (Melancon [Background]). In the combination of elements, it would have been obvious to one of ordinary skill in the art to add an alert condition satisfaction based on failure probability.
Ruderfer does not teach the limitation taught by Melancon
Melancon teaches and in response to determining that the alert condition is satisfied, outputting an alert representing the failure probability. (“Assuming, for example, precision value is 0.8, and 50% of the sample having a precision value greater than 0.8 correctly predicted failures, the probability of failure may be measured as 50% when operating at a precision value of 0.8. The calculated probability of a failure occurring based on the values of the prediction features is a failure risk score, as described above. In addition, prediction system 110 may generate and display a visualization of the contribution of each prediction feature to the probability that an alert will result in a service level failure, which may be used to identify which supply chain systems need to be adjusted to avoid the service level failure.) (Melancon [Col. 17-18, Lines 60-4]) (Examiner note: The alert given to the display if a supply chain system needs to be adjusted is interpreted as outputting an alert representing the failure probability)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as taught by Ruderfer to include an alert representing the failure probability as taught by Melancon with the motivation to achieve customer service level targets (Melancon [Background]). In the combination of elements, it would have been obvious to one of ordinary skill in the art to add an alert representing the failure probability.
With respect to Claim 11,
Ruderfer, Katsuki, Bassov, Lou, and Melancon teach the limitations of Claim 10
Ruderfer does not teach the limitation taught by Bassov
Bassov teaches wherein the record-size metric identifies a total quantity of electronic records in the set of electronic records. (“The said determination also comprises determining a total number of primary and replica files…) (Bassov [Col. 15, Lines 64-66]) (Examiner note: Determining the total number of primary files and replica files is interpreted as identifying the total quantity of electronic records)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as taught by Ruderfer to include the determination of total quantity of electronic records as taught by Bassov with the motivation to improve storage efficiency and the techniques for reporting the space savings that result from data reduction techniques (Bassov [Background]). In the combination of elements, it would have been obvious to one of ordinary skill in the art to add the determination of the total quantity of electronic records.
With respect to Claim 12,
Ruderfer, Katsuki, Bassov, Lou, and Melancon teach the limitations of Claim 10
Ruderfer does not teach the limitation taught by Bassov
Bassov teaches wherein the record-size metric identifies a quantity of unique electronic records in the set of electronic records. (“The said determination also comprises determining a total number of primary and replica files…) (Bassov [Col. 15, Lines 64-66]) (Examiner note: Determining the total number of primary files and replica files is interpreted as identifying the quantity of unique electronic records because if the number of replica files is subtracted from the total number of files it outputs the number of unique files or files that do not have replicas)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as taught by Ruderfer to include the determination of total quantity of unique electronic records as taught by Bassov with the motivation to improve storage efficiency and the techniques for reporting the space savings that result from data reduction techniques (Bassov [Background]). In the combination of elements, it would have been obvious to one of ordinary skill in the art to add the determination of the total quantity of unique electronic records.
With respect to Claim 13,
Ruderfer, Katsuki, Bassov, Lou, and Melancon teach the limitations of Claim 10
Ruderfer teaches wherein the physical attribute identifies an age (“…identified four patients having identical age, sex, race, number of unique years in which the patient had visited VUMC, and the closest EHR record length in days (maximum of 100 days difference).”) (Ruderfer [0024])
With respect to Claim 14,
Ruderfer, Katsuki, Bassov, Lou, and Melancon teach the limitations of Claim 10
Ruderfer does not teach the limitation taught by Lou
Lou teaches collecting one or more vital-sign measurements using a sensor that is attached to or worn by the given subject or the given object; and generating a vital-sign metric based on the vital-sign measurements, wherein the input data set includes the vital-sign metric. (“In act 41, the image processor acquires non-image data. The non-image data is from sensors, the computerized patient medical record, manual input, pathology database, laboratory database, and/or other source. The non-image data represents one or more characteristics of the patient, such as family history, medications taken, temperature, body-mass index, and/or other information.) (Lou [0071]) (Examiner note: Non-image data acquired by a sensor such as temperature is interpreted as vital-sign measurements and metrics) and (“Non-image data may be input instead or in addition to scan data.”) (Lou [0070]) (Examiner note: Non-image data being used as input is interpreted as input data including vital-sign metric data)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as taught by Ruderfer to include the collection of vital-sign measurements, generating a vital-sign metric and input data including vital-sign metrics as taught by Lou with the motivation to aid in the selection of treatments based on specific characteristics of a patient and their disease to avoid treatment resistance and recurrence (Lou [0002]). In the combination of elements, it would have been obvious to one of ordinary skill in the art to add the collection of vital-sign measurements, the generation of a vital-sign metric and input data including vital-sign metrics.
With respect to Claim 15,
Ruderfer, Katsuki, Bassov, Lou, and Melancon teach the limitations of Claim 14
Ruderfer does not teach the limitation taught by Lou
Lou teaches wherein the alert is output on a device that includes the sensor. (FIG. 3 shows an example method for machine training decision support in a medical therapy system. The method is implemented by a machine (e.g., computer, processor, workstation, or server) using training data (e.g., samples and ground truths for the samples) in a memory.”) (Lou [0049]) and (“Examples provided for training of FIG. 3 may result in a trained generator for outcome usable in application of FIG. 4. Conversely, examples discussed in conjunction with the application of FIG. 4 may be trained as part of the training of FIG. 3.”) (Lou [0048]) (Examiner note: Figure 4 depicts acquiring non-image data i.e. vital sign data and displaying an output)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method as taught by Ruderfer to include an output on a device that includes the sensor as taught by Lou with the motivation to aid in the selection of treatments based on specific characteristics of a patient and their disease to avoid treatment resistance and recurrence (Lou [0002]). In the combination of elements, it would have been obvious to one of ordinary skill in the art to add an output on a device that includes the sensor.
With respect to Claim 18,
Ruderfer, Katsuki, Bassov, Lou, and Melancon teach the limitations of Claim 10
collecting one or more vital-sign measurements using a sensor that is attached to or worn by the given subject or the given object; determining that an additional alert condition is satisfied based on the vital-sign measurements; (“In act 41, the image processor acquires non-image data. The non-image data is from sensors, the computerized patient medical record, manual input, pathology database, laboratory database, and/or other source. The non-image data represents one or more characteristics of the patient, such as family history, medications taken, temperature, body-mass index, and/or other information.) (Lou [0071]) (Examiner note: Non-image data acquired by a sensor such as temperature is interpreted as vital-sign measurements and metrics)
It would have been obvious to one of ordinary sk