DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the present application filed on 9/08/2025.
Claims 1, 9, and 13 have been amended.
Claims 1-20 are currently pending and have been examined.
This action is made final.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1A Analysis:
Independent Claims 1, 9, and 13 are within the four statutory categories. Claims 1, 9, and 13 are directed toward methods (i.e. process) and a non-transitory medium (i.e. machine) respectively and therefore fall into one of the four statutory categories. Dependent Claims 2-8 and 10-12 are further directed to methods and Claims 14-20 are directed to a non-transitory computer-readable storage medium. Therefore the dependent claims also fall into one of the four statutory categories.
Step 2A Analysis – Prong One:
The substantially similar independent claims, taking Claim 1 as exemplary, recite the following:
A method comprising: collecting electronic health record (EHR) data for a plurality of patients associated with a first healthcare facility;
generating a set of facility features based on aggregating patient features, from the EHR data, corresponding to at least two of the plurality of patients;
generating one or more predicted scores for the first healthcare facility based on processing the set of facility features using a machine learning model, wherein the predicted scores are indicative of quality of the first healthcare facility;
and in response to determining that the one or more predicted scores fail to satisfy one or more thresholds, ranking the set of facility features, from the EHR data, based on their salience to the one or more predicted scores, comprising:
for at least one facility feature of the set of facility features, determining a distribution of values reflected in EHR data a plurality of healthcare facilities;
generating a plurality of perturbed facility features by probabilistically perturbing the at least one facility feature based on the distribution of values such that the perturbed at least one facility feature is within realistic bounds defined based on the distribution of values;
and generating a plurality of predicted scores based on processing the plurality of perturbed facility features using the machine learning model;
outputting the ranked set of facility features via a graphical user interface (GUI), comprising at least one of automatically filtering or automatically sorting information for each of the plurality of healthcare facilities based on corresponding predicted scores for each of the plurality of healthcare facilities;
and facilitating reconfiguration of the first healthcare facility based on the ranked set of facility features.
Independent Claim 9 recites the limitations seen in claim 1 and additionally recites:
generating one or more predicted scores for a first healthcare facility based on processing a new set of facility features using the machine learning model;
ranking the new set of facility features based on salience to the one or more predicted scores;
The series of steps in underline above, given the broadest reasonable interpretation, cover the abstract idea of certain methods of organizing human activity because they recite managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules and instructions- in this case, collecting data, generating features, generating predicted scores, ranking predicted scores, and outputting the ranked features), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements” and will be discussed further below.
Dependent Claims 2-8, 10-12, and 14-20 recite other limitations directed toward the abstract idea. For example, Claims 2 and 14 recite ranking the set of features comprises determining, for each respective facility feature, a respective contribution to one or more predicted scores in the plurality of predicted scores, Claims 4 and 16 recite identifying a subset of high-performing healthcare facilities, based on comparing a plurality of scores to one or more criteria, and ranking the set of features based on EHR data from healthcare facilities in the subset of high-performing healthcare facilities, Claims 5 and 17 recite determining a respective representative value with respect to the EHR data from the subset of high-performing healthcare facilities; and determining a respective difference between the respective representative value and a value of the respective feature for the first healthcare facility; Claims 6 and 18 recite receiving a proposed value for one or more features of the set of features, generating an updated predicted score for the first healthcare facility, and outputting the updated predicted score, Claims 7 and 19 recite the facility features comprise notes recorded for each patient, a number of times vitals are recorded for each patient, a frequency of medication administration issues, or a frequency of falls experienced by one or more patients of the plurality of patients, Claims 8 and 20 recite the first healthcare facility corresponds to a residential care facility, and the plurality of patients correspond to residents of the residential care facility, Claim 10 recites identifying a set of features in the plurality of data, selecting a subset of salient features, and using one or more feature selection operations, Claim 11 recites the EHR data comprise a respective number of notes recorded for each patient of the respective healthcare facility during a duration of time, a respective number of times one or more vitals were recorded for each patient of the respective healthcare facility during the duration of time, a respective frequency of medication administration issues, or a respective frequency of falls experienced by one or more patients of the plurality of patients of the healthcare facility, Claim 12 recites the plurality of healthcare facilities correspond to residential care facilities. These limitations only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g., see MPEP 2106.04. Additionally, any limitations in dependent Claims 2-8, 10-12, and 14-20 not addressed above are deemed additional elements to the abstract idea and will be further addressed below. Hence, dependent Claims 2-8, 10-12, and 14-20 are nonetheless directed towards fundamentally the same abstract idea as independent Claims 1, 9, and 13.
Step 2A Analysis – Prong Two:
Claims 1, 9, and 13 are not integrated into practical application because the additional elements (i.e. the non-underlined portions presented in prong one- in this case, the machine learning model, and GUI of Claim 1, machine learning model of Claim 9, and the non-transitory computer-readable storage medium, computer processor, machine learning model, and GUI of Claim 13) are recited at a high level of generality (i.e. as a generic processor performing generic computer functions) such that they amount to no more than mere instructions to apply the exceptions using a generic computer component. For example, Applicant’s specification explains that the GUI 600 may enable users to dynamically sort and/or filter the facilities based at least in part on their scores. For example, the GUI 600 may display the lowest-scoring facilities in a more prominent position (e.g., near the top) to allow for efficient identification of potentially problematic facilities that may need attention [0096]. In some embodiments, the system can generate and/or train predictive models (e.g., machine learning models) using the assigned scores as the label/target output, and the low-level EHR data as the input [0026]. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into practical application because they do not impose any meaningful limits on the abstract idea. Therefore, independent Claims 1, 9, and 13 are directed to an abstract idea without practical application.
Dependent Claims 3, 6, 10, 15, and 18 recite additional elements. Claims 6 and 18 recite the previously recited GUI and specify the GUI receives a proposed value for the facility features and outputs the updated predicted score on the GUI. Claims 3, 6, 9-10, 15, and 18 recite the previously recited machine learning model. Claims 3 and 15 recite the machine learning model comprises a trained machine learning model that was trained based on EHR data for the plurality of healthcare facilities. Claim 10 recites training the machine learning model based on the subset of salient features. However, these additional elements are used in their expected fashion, so they do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on the abstract idea. These limitations amount to no more than mere instructions to apply an exception, and hence, do not integrate the aforementioned abstract idea into practical application.
Step 2B Analysis:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of the machine learning model and GUI of Claim 1, the machine learning model and GUI of Claim 9, and the non-transitory computer-readable storage medium, computer processor, machine learning model, and GUI of Claim 13 amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). MPEP2106.05(I)(A) indicates that merely stating “apply it” or equivalent to the abstract idea cannot provide an inventive concept (“significantly more”). Accordingly, even in combination, this additional element does not provide significantly more. As such the independent Claims 1, 9, and 13 are not patent eligible.
Dependent Claims 4-5, 7-8, 11-12, 16-17, 19, and 20 do not recite any additional elements and solely narrow the abstract idea. Claims 4 and 16 narrow the abstract ideas of Claims 1 and 13 by specifying that the scores are related to multiple criteria and ranked based on high-performing healthcare facilities. Claims 5 and 17 narrow the abstract idea by specifying the determined facility values are compared to a high-scoring facility. Claims 7 and 19 narrow the abstract idea presented in Claims 1 and 13 by specifying what the features comprise. Claims 8 and 20 narrow the abstract idea presented in Claims 1 and 13 by specifying the relationships of the healthcare facility and the residential care facility as well as the patients and the residents in the care facility. Claim 11 narrows the abstract idea by specifying the type of data being examined. Claim 12 narrows the abstract idea of Claim 1 by specifying that the healthcare facilities correspond to residential care facilities.
Claims 2-3, 6, 9-10, 14-15, and 18 narrow the previously recited additional elements. Claims 2 and 14 recite the machine learning model and specify the model generates predicted scores. Dependent Claims 3 and 15 narrow the machine learning model by specifying it is trained on health record data. Claims 6 and 18 narrow the additional elements of the GUI and prediction model by specifying that the GUI receives an updated value for the feature and generates an updated prediction score via the prediction model. Claim 10 narrows the machine learning model by specifying that the model selects salient features from a subset of features. These additional elements are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Hence, Claims 2-8, 10-12, and 14-20 do not include any additional elements that amount to “significantly more” than the judicial exception.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination does not add anything that is already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 1-20 are nonetheless rejected under 35 U.S.C 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 4, 6, 13-14, 16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Krimsky et al. (US 20200026401 A1) in view of Vos et al. (US 20240105345A1) and Zonooz et al. (US 20210166123 A1).
Regarding Claim 1, Krimsky discloses the following:
method, comprising: collecting electronic health record (EHR) data for a plurality of patients associated with a first healthcare facility; (Krimsky discloses multiple patient records are accrued over a rolling period of remote evaluation sessions, are compiled in the CCC 110 database, analyzed on an institutional level, then made available in at the hospital 130 or doctor computer 120 on a dynamic TeleICU dashboard [0050].)
generating a set of facility features based on aggregating patient features, from the EHR data, corresponding to at least two of the plurality of patients, (Krimsky discloses four specific quantitative measures are scored and combined into a Patient Care Score. The four quantitative measures include: Total number of assessments (Antot)… Total number of skin integrity issues (Sntot) and number being addressed (Snadd);… Total number of delirium issues (Dntot) and number being addressed (Dnadd);… Number of patients mobilized (Mn);… Number of patients on appropriate prophylaxis (Pn) [0070-75]. The Patient Care Score is interpreted as facility features generated from EHR data of a plurality of patients.)
generating one or more predicted scores for the first healthcare facility, based on processing the set of facility features (Krimsky discloses the present system may refine the facility score over time to correlate with patient outcomes such as mortality, length of stay, number of transfers etc. The facility score can be correlated for a hospital ICU with patient outcomes, such that the score is a good proxy to predict that hospital's patient outcomes (e.g., when a hospital's Charlotte Score is X %, the mortality will likely be Y %). This makes the facility score both predictive (allowing prediction of current patient outcomes as well as future patient outcomes) and prescriptive (indicating what needs to be changed to improve outcomes) [0100]. FIGS. 11A and 11B are screens of an examine dashboard…illustrating how a…center can assess various treatment compliance indicators and calculate the composite facility score based on the treatment compliance indicators and the care score [0017, Figs 11A and 11B].)
…wherein the predicted scores are indicative of quality of the first healthcare facility; (Krimsky discloses an overall facility score, such as the “Charlotte Score” shown in FIGS. 11A and 11B. This Charlotte Score represents actionable data precisely because it can be used to assess different processes and outcomes as well as can be linked to a variety of different processes and outcomes such as patient outcomes, patient-related care processes, institutional goals for care, future patient outcomes, etc. It is predictive in the sense that it allows prediction of current patient outcomes as well as future patient outcomes [0052]. This ongoing documentation may also be used in facilities/programs with metrics for quality /safety/Joint Commission standards [0049].)
in response to determining that the one or more predicted scores fail to satisfy one or more thresholds,… (Krimsky discloses an extent of patient care risk for a particular facility may also correspond to a determination that the facility score for the particular facility is below or above a threshold value, such as a facility score benchmark. A facility score benchmark may be, for example the mean or median facility score or other selected statistic for all or a subset of facilities having records in the database maintained by CCC 110 computer [0114].)
…for at least one facility feature of the set of facility features, (Krimsky discloses systems…can use treatment status indicators, treatment compliance indicators, care scores, and facility scores from the set of facilities connected to the system to determine an extent of patient care risk for a particular facility… FIG. 15…showing an example procedure for providing a health care facility an indicator of a determined extent of risk, according to an embodiment of the invention. The system can determine an extent of patient care risk for a particular facility and notify the facility, such as by providing in indicator on the system's dashboard interface for the facility. [0102].)
…values reflected in EHR data for a plurality of healthcare facilities; (Krimsky discloses a facility may realize significant negative treatment outcomes caused by the occurrence of sepsis in patients. For example, the facility's incidence rate of sepsis may be significantly higher than other comparable facilities due to serving a patient population that is more susceptible to sepsis [0111]. The Examiner interprets the rate of sepsis being data coming directly from patient EHR data across multiple facilities.)
and generating a plurality of predicted scores based on processing the plurality of …facility features using the…model; (Krimsky discloses the present system may refine the facility score over time to correlate with patient outcomes such as mortality, length of stay, number of transfers etc. The facility score can be correlated for a hospital ICU with patient outcomes, such that the score is a good proxy to predict that hospital’s patient outcomes (e.g., when a hospital's Charlotte Score is X %, the mortality will likely be Y %). This makes the facility score both predictive (allowing prediction of current patient outcomes as well as future patient outcomes) and prescriptive (indicating what needs to be changed to improve outcomes) [0100]. A facility score benchmark may be, for example the mean or median facility score or other selected statistic for all or a subset of facilities having records in the database maintained by CCC 110 computer [0114].)
…for each of the plurality of healthcare facilities based on corresponding predicted scores for each of the plurality of healthcare facilities; (Krimsky discloses systems according to embodiments of the invention can … facility scores from the set of facilities connected to the system to determine an extent of patient care risk for a particular facility [0102].)
and facilitating reconfiguration of the first healthcare facility based on the…facility features (Krimsky discloses the present system may refine the facility score over time to correlate with patient outcomes such as mortality, length of stay, number of transfers etc. The facility score can be correlated for a hospital ICU with patient outcomes, such that the score is a good proxy to predict that hospital’s patient outcomes…This makes the facility score both predictive…and prescriptive (indicating what needs to be changed to improve outcomes) [0100]. The Examiner interprets an indication of what needs to be changed at a facility to improve patient outcomes as being what facilitates reconfiguration of the facility.)
Krimsky does not disclose ranking sets of features, the use of machine learning, or automatically filtering which is met by Vos:
…using a machine learning model, (Vos teaches by training the random forest classifier using the clinical records (e.g., clinical records 120), the random forest classifier may automatically provide a feature ranking representative of the correlation between the data corresponding to each input variable of the set of input variables and the data corresponding to the driving outcome [0053]. The Examiner interprets the random forest classifier as a machine learning model.)
ranking the set of … features, based on salience to the one or more predicted scores, comprising: (Vos teaches training of a random forest classifier, the random forest classifier may provide an internal ranking of a feature importance (e.g., a feature ranking). The internal ranking may identify the features (e.g., the input variables) that have the greatest effect on the output (e.g., the outcomes), as well as the features that have the least effect on the output (e.g., the outcomes). As an illustrative example, a number between 0 and 10 may be assigned to each feature input to the random forest, where 0 represents the lowest effect on an output and 10 represents the greatest effect on the output [0053]. The Examiner interprets the level of importance as a feature as the salience.)
and outputting the…set of… features via a graphical user interface (GUI). (Vos teaches FIG. 6 is a diagrammatic view of screen display 600 (e.g., a GUI) that may be provided at step 408 of the method 400. In particular, FIG. 6 provides an exemplary screen display 600 for the graphical representation of input variables automatically arranged based on a ranking determined…[0055].)
comprising at least one of automatically filtering or automatically sorting information… (Vos teaches the processor circuit may be configured to provide the graphical representation of the set of inputs automatically arranged based on the first ranking…[0006].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the systems and methods for collecting a plurality of patient data and determining a healthcare facility score as disclosed by Krimsky to incorporate the use of a machine learning model, ranking the features based on level of importance, and automatically filtering information as taught by Vos. This modification would improve patient care by improving upon the prediction of outcomes (see Vos, ¶ 0049).
Krimsky and Vos do not teach the use of perturbing the data which is met by Zonooz:
for at least one feature…determining a distribution of values… (Zonooz teaches forcing the model to completely match the feature distributions…[0006].)
generating a plurality of perturbed… features by probabilistically perturbing the at least one…feature based on the distribution of values such that the perturbed at least one…feature is within realistic bounds defined based on the distribution of values; (Zonooz teaches the supervision from the natural model acts as a noise-free reference for regularizing the robust model. This effectively adds a prior on the learned representations which encourages the model to learn semantically relevant features in the input space. Coupling this with the affinity of the robust model pushes the model towards features with stable behaviour within the perturbation bound [0016].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the systems and methods for collecting a plurality of patient data and determining a healthcare facility score as disclosed by Krimsky to incorporate determining a distribution of data and then generating perturbed data within realistic bounds as taught by Zonooz. This modification would create a system and method capable of training a robust machine learning model (see Zonooz, ¶ 0003-4).
Regarding Claim 13, this claim recites substantially similar limitations to those recited in Claim 1 above; thus the same rejection applies. Krimsky further discloses:
A non-transitory computer-readable storage medium comprising computer-readable program code that, when executed using one or more computer processors, performs an operation (Krimsky discloses a system, comprising: a database storing a plurality of records for a plurality of facilities; a first computer associated with a consult coordination center and comprising a processor in communication with the database; and a non-transitory, computer-readable medium in communication with the processor and storing instructions that, when executed by the processor, cause the processor to perform operations comprising handing off, by the first computer to a second computer associated with a remote health care professional and a third computer associated with a first facility (Claim 24).)
Regarding Claim 2, Krimsky, Vos, and Zonooz teach the limitations as shown in the rejection of Claim 1 above. Krimsky further discloses:
…facility features… (Krimsky discloses four specific quantitative measures are scored and combined into a Patient Care Score. The four quantitative measures include: Total number of assessments (Antot)… Total number of skin integrity issues (Sntot) and number being addressed (Snadd);… Total number of delirium issues (Dntot) and number being addressed (Dnadd);… Number of patients mobilized (Mn);… Number of patients on appropriate prophylaxis (Pn) [0070-75].)
… a respective contribution to one or more predicted scores in the plurality of predicted scores (Krimsky discloses weight the selected indicators such that positive or negative completion of the treatment status indicator or treatment compliance indicator will have a disproportionate impact on the calculation of the care score or facility score for the facility as compared to other contributing indicators [0111].)
Krimsky does not disclose the following limitations met by Vos:
wherein ranking the set of…features further comprises determining, for each respective… feature in the set of…features,…(Vos teaches… the random forest classifier may provide an internal ranking of a feature importance (e.g., a feature ranking). The internal ranking may identify the features (e.g., the input variables) that have the greatest effect on the output (e.g., the outcomes), as well as the features that have the least effect on the output (e.g., the outcomes). As an illustrative example, a number between 0 and 10 may be assigned to each feature input to the random forest, where 0 represents the lowest effect on an output and 10 represents the greatest effect on the output [0053].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the systems and methods for collecting a plurality of patient data and determining a healthcare facility score as disclosed by Krimsky to incorporate the ranking the features based on level of importance as taught by Vos. This modification would improve patient care by improving upon the prediction of outcomes (see Vos, ¶ 0049).
Regarding Claim 14, this claim recites substantially similar limitations to those recited in Claim 2 above; thus the same rejection applies.
Regarding Claim 4, Krimsky, Vos, and Zonooz teach the limitations as shown in the rejection of Claim 1 above. Krimsky further discloses:
identifying a subset of high-performing healthcare facilities, from the plurality of healthcare facilities, based on comparing a plurality of scores to one or more criteria;… based at least in part on EHR data from healthcare facilities in the subset of high-performing healthcare facilities (Krimsky discloses a facility may realize significant negative treatment outcomes caused by the occurrence of sepsis in patients…the facility's incidence rate of sepsis may be significantly higher than other comparable facilities …Thus, preventing the incidence of sepsis may be especially important to the facility. The a facility computer 130 may be presented with a user-interface by CCC 110 computer that provides options for weighting the contribution of certain treatment status indicators and treatment compliance indicators to the calculation of the care score or facility score for the facility. The facility computer can select a value to weight the selected indicators such that positive or negative completion of the treatment status indicator or treatment compliance indicator will have a disproportionate impact on the calculation of the…facility score…[0009]. The Examiner interprets a facility with low incidence rates of sepsis as a high-performing facility.)
Krimsky does not disclose the following limitations met by Vos:
and ranking the set of…features… (Vos teaches the random forest classifier may provide an internal ranking of a feature importance (e.g., a feature ranking). The internal ranking may identify the features (e.g., the input variables) that have the greatest effect on the output (e.g., the outcomes), as well as the features that have the least effect on the output (e.g., the outcomes) [0053].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the systems and methods for collecting a plurality of patient data and determining a healthcare facility score as disclosed by Krimsky to incorporate the ranking the features based on level of importance as taught by Vos. This modification would improve patient care by improving upon the prediction of outcomes (see Vos, ¶ 0049).
Regarding Claim 16, this claim recites substantially similar limitations to those recited in Claim 4 above; thus the same rejection applies.
Regarding Claim 6, Krimsky, Vos, and Zonooz teach the limitations as shown in the rejection of Claim 1 above. Krimsky further discloses:
receiving, via the GUI, a proposed value for one or more facility features of the set of features; (Krimsky discloses if there are new patients or discharges 325, the patient list may be updated 330 [0042]. The CCC 110 computer can update various metrics based on the received treatment status data and treatment compliance data at 1540. (para. 0106). CCC 110 computer can then provide the filtered and anonymized set of treatment status indicators and treatment compliance indicators to the facility computer, such as via the user-interface [0123].)
generating an updated predicted score for the first healthcare facility, using the… model, based on the proposed value; (Krimsky discloses the CCC 110 computer can also update a facility score for the facility at 1540, such as a Charlotte Score. For example, treatment compliance indicators for the facility can be updated based on received treatment compliance data in accordance with any of the techniques described herein. The updated treatment compliance indicators can serve as the basis for updating the facility score. The CCC 110 computer can then update the facility score by changing an indicator of the facility score to reflect the newly calculated facility score [0109].)
and outputting the updated predicted score via the GUI. (discloses teaches both the Patient Care Score and Charlotte Score are compiled in real time over a rolling time period, preferably six months and displayed on a novel dashboard that provides user-defined filters to filter both the Patient Care Score, Charlotte Score and individual metrics [0052].)
Krimsky does not disclose the model being a machine learning model which is met by Vos:
using a machine learning model, (Vos teaches by training the random forest classifier using the clinical records (e.g., clinical records 120), the random forest classifier may automatically provide a feature ranking representative of the correlation between the data corresponding to each input variable of the set of input variables and the data corresponding to the driving outcome [0053]. The Examiner interprets the random forest classifier as a machine learning model.)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the systems and methods for collecting a plurality of patient data and determining a healthcare facility score as disclosed by Krimsky to incorporate the use of a machine learning model as taught by Vos. This modification would improve patient care by improving upon the prediction of outcomes (see Vos, ¶ 0049).
Regarding Claim 18, this claim recites substantially similar limitations to those recited in Claim 6 above; thus the same rejection applies.
Claims 3 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Krimsky, Vos, and Zonooz in view of Granson et al. (US 11816539 B1).
Regarding Claim 3, Krimsky, Vos, and Zonooz teach the limitations as shown in the rejection of Claim 1 above. Krimsky, Vos, and Zonooz do not teach the following limitations met by Granson:
the prediction model comprises a trained machine learning model that was trained based on EHR data for the plurality of healthcare facilities. (Granson teaches a neural network processor includes a plurality of input nodes…The input nodes each accept one data metric from the database and apply at least one weighting factor learned through a training set of input metric data and health care provider and health care facility output ratings to connections to succeeding hidden nodes in the neural network. Each output node provides a rating target value for the medical procedure based on the neural processing of the data metrics (col. 3, lines 4-14).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the systems and methods for collecting a plurality of patient data and determining a healthcare facility score as disclosed by Krimsky to training a machine learning model based off of medical record data as taught by Granson. This modification would create a system and methods which provides objective data to patients on the quality of physicians and their facilities (see Granson, col. 2, lines 48-61).
Regarding Claim 15, this claim recites substantially similar limitations to those recited in Claim 3 above; thus the same rejection applies.
Claims 5 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Krimsky in view of Vos and Zonooz, further in view of Ridgeway et al. (US 20190287039 A1).
Regarding Claim 5, Krimsky, Vos, and Zonooz teach the limitations as shown in the rejection of Claim 4 above. Krimsky further discloses:
… the set of facility features… (Krimsky discloses four specific quantitative measures are scored and combined into a Patient Care Score. The four quantitative measures include: Total number of assessments… Total number of skin integrity issues … Total number of delirium issues (Dntot) and number being addressed… Number of patients mobilized (Mn);… Number of patients on appropriate prophylaxis [0070-75]. The Patient Care Score is interpreted as facility features generated from EHR data of a plurality of patients.)
Krimsky does not disclose the ranking the features which is met by Vos:
ranking the set of…features comprises, for each respective…feature in the set of …features: (Vos teaches training of a random forest classifier, the random forest classifier may provide an internal ranking of a feature importance (e.g., a feature ranking). The internal ranking may identify the features (e.g., the input variables) that have the greatest effect on the output (e.g., the outcomes), as well as the features that have the least effect on the output (e.g., the outcomes). As an illustrative example, a number between 0 and 10 may be assigned to each feature input to the random forest, where 0 represents the lowest effect on an output and 10 represents the greatest effect on the output [0053]. The Examiner interprets the level of importance as a feature as the salience.)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the systems and methods for collecting a plurality of patient data and determining a healthcare facility score as disclosed by Krimsky to incorporate ranking features of a set of features based on their salience as taught by Vos. This modification would improve patient care by improving upon the prediction of outcomes (see Vos, ¶ 0049).
Krimsky, Vos, and Zonooz do not teach the following limitations met by Ridgeway:
determining a respective representative value with respect to the EHR data from the subset of high-performing healthcare facilities; (Ridgeway teaches the systems and methods provide reporting on the results for each service provider, including a report card listing observed outcomes, its benchmark outcomes, and an outlier probability (abstract, Fig. 5). There is provided a software component … for the statistical analysis of patient medical records for the purpose of dynamically benchmarking the performance of medical providers. The system provides a high quality benchmark that matches patient data of a given medical provider with a collection of patient data from other medical providers involving similar diagnoses, medical records, and prescription drug histories [0006].)
and determining a respective difference between the respective representative value and a value of the respective facility feature for the first healthcare facility. (Ridgeway teaches underperforming and/or overperforming service providers may be compared relative to each other, based on their performance relative to their individual benchmarks (abstract). The Z-statistic is extracted from the weighted regression model as a measure of the difference between Hospital A's outcomes and the benchmark outcomes [0089]. FIG.5 is a table showing the number of patients for each hospital, the effective sample size of the benchmark set of patients, and the largest difference in the patient features between the hospital and its benchmark (¶ 0092, Fig. 5).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the systems and methods for collecting a plurality of patient data and determining a healthcare facility score as disclosed by Krimsky to incorporate comparing the values of a facility with a high-performing or benchmark facility as taught by Ridgeway. This modification would create a system and methods which can provide controlled mechanism for benchmarking service providers, which can help determine how/why a low performance score occurs (see Ridgeway, ¶ 0003-0004).
Regarding Claim 17, this claim recites substantially similar limitations to those recited in Claim 5 above; thus the same rejection applies.
Claims 7 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Krimsky, Vos, and Zonooz in view of Gilham et al. (US 20100298718 A1).
Regarding Claim 7, Krimsky and Phillips teach the limitations as shown in the rejection of Claim 1 above. Krimsky further discloses:
the set of facility features… (Krimsky discloses four specific quantitative measures are scored and combined into a Patient Care Score. The four quantitative measures include: Total number of assessments… Total number of skin integrity issues…Total number of delirium issues… Number of patients mobilized (Mn);… Number of patients on appropriate prophylaxis [0070-75]. The Patient Care Score is interpreted as facility features generated from EHR data of a plurality of patients.)
Krimsky, Vos, and Zonooz do not teach the following limitations met by Gilham:
…comprise at least one of: a number of notes recorded for each patient, of the plurality of patients, during a duration of time; a number of times one or more vitals are recorded for each patient, of the plurality of patients, during the duration of time; a frequency of medication administration issues; or a frequency of falls experienced by one or more patients of the plurality of patients. (Gilham teaches an input device for receiving a plurality of physical inputs from a user and communicating to a memory data indicative of the frequency at which said plurality of physical inputs are received from the user within a time period [0010]. The physiological parameter is at least one of pulse rate or respiratory rate [0011]. The monitor provides a visual indication of status, including the number of completed readings for each specified interval. The input device is a touch screen display. The monitor functions as an adjunct to the clinical staff for collection and documentation of vital sign data on multiple patients [0051].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the systems and methods for collecting a plurality of patient data and determining a healthcare facility score as disclosed by Krimsky to incorporate the features of the EHR of tracking the number of times vital signs are measured for a plurality of patients taught by Gilham. This modification would create a system and method which is able to analyze the quality of vital sign data by the number of times it is collected (see Gilham, ¶ 0008-9).)
Regarding Claim 19, this claim recites substantially similar limitations to those recited in Claim 7; thus the same rejection applies.
Claims 8 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Krimsky, Vos, and Zonooz in view of Bua et al. (US 20030167187 A1).
Regarding Claim 8, Krimsky, Vos, and Zonooz teach the limitations as shown in the rejection of Claim 1 above. Krimsky, Vos, and Zonooz do not teach the following limitations met by Bua:
the first healthcare facility corresponds to a residential care facility, (Bua teaches determining a performance rating of a health care facility, such as a nursing home or a long term care facility (abstract).)
and the plurality of patients correspond to residents of the residential care facility. (Bua teaches the performance ratings of a health care facility can be determined …using…federal and state inspection data to base the performance ratings on objective and resident-focused criteria and inspections that monitor a quality of life and a quality of care of the facility's residents and patients [0013].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the systems and methods for collecting a plurality of patient data and determining a healthcare facility score as disclosed by Krimsky to incorporate the medical facility being a residential care facility as taught by Bua. This modification would create a system and method with provides people with a resource for identifying a safe, quality nursing or residential home (see Bua, ¶ 0005).
Regarding Claim 20, this claim recites substantially similar limitations to those recited in Claim 8 above; thus, the same rejection applies.
Claims 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Krimsky et al. (US 20200026401 A1) in view of Granson et al. (US 11816539 B1), Zonooz et al (US 20210166123 A1), and Vos et al. (US 20240105345 A1).
Regarding Claim 9, Krimsky discloses the following:
A method comprising: collecting a plurality of electronic health record (EHR) data for a plurality of healthcare facilities; (Krimsky discloses multiple patient records are accrued over a rolling period of remote evaluation sessions, are compiled in the CCC 110 database, analyzed on an institutional level, then made available in at the hospital 130 or doctor computer 120 on a dynamic TeleICU dashboard [0050]. Doctors and hospitals are shown in this example, although …facilities 130 may be present in the network 100 [0033]. Systems according to embodiments of the invention can use treatment status indicators, treatment compliance indicators, care scores, and facility scores from the set of facilities connected to the system to determine an extent of patient care risk for a particular facility [0102].)
generating a set of facility features based on aggregating patient features, from the EHR data, corresponding to at least two of the plurality of patients; (Krimsky discloses four specific quantitative measures are scored and combined into a Patient Care Score. The four quantitative measures include: Total number of assessments (Antot)… Total number of skin integrity issues (Sntot) and number being addressed (Snadd);… Total number of delirium issues (Dntot) and number being addressed (Dnadd);… Number of patients mobilized (Mn);… Number of patients on appropriate prophylaxis (Pn) [0070-75]. The Patient Care Score is interpreted as facility features generated from EHR data of a plurality of patients.)
determining a plurality of facility scores for the plurality of healthcare facilities; …a model to generate predicted…scores based on the set of…features and the plurality of…scores, (Krimsky discloses the system may refine the facility score over time to correlate with patient outcomes such as mortality, length of stay, number of transfers etc. The facility score can be correlated for a hospital ICU with patient outcomes, such that the score is a good proxy to predict that hospital's patient outcomes (e.g., when a hospital's Charlotte Score is X %, the mortality will likely be Y %). This makes the facility score both predictive (allowing prediction of current patient outcomes as well as future patient outcomes) and prescriptive (indicating what needs to be changed to improve outcomes) [0100]. FIGS. 11A and 11B are screens of an examine dashboard…illustrating how a…center can assess various treatment compliance indicators and calculate the composite facility score based on the treatment compliance indicators and the care score [0017, Figs 11A and 11B]. Doctors and hospitals are shown in this example, although …facilities 130 may be present in the network 100 [0033]. Systems according to embodiments of the invention can use treatment status indicators, treatment compliance indicators, care scores, and facility scores from the set of facilities connected to the system to determine an extent of patient care risk for a particular facility [0102].)
the…scores indicative of quality of healthcare facilities; (Krimsky discloses the facility score can be correlated for a hospital ICU with patient outcomes, such that the score is a good proxy to predict that hospital's patient outcomes…This makes the facility score both predictive (allowing prediction of current patient outcomes as well as future patient outcomes) and prescriptive (indicating what needs to be changed to improve outcomes) [0100].)
generating one of more predicted scores for a first healthcare facility based on processing a new set of facility features using the…model; (Krimsky discloses the present system may refine the facility score over time to correlate with patient outcomes such as mortality, length of stay, number of transfers etc. The facility score can be correlated for a hospital ICU with patient outcomes, such that the score is a good proxy to predict that hospital's patient outcomes (e.g., when a hospital's Charlotte Score is X %, the mortality will likely be Y %). This makes the facility score both predictive (allowing prediction of current patient outcomes as well as future patient outcomes) and prescriptive (indicating what needs to be changed to improve outcomes) [0100]. FIGS. 11A and 11B are screens of an examine dashboard …illustrating how a…center can assess various treatment compliance indicators and calculate the composite facility score based on the treatment compliance indicators and the care score [0017, Figs 11A and 11B].)
in response to determining that the one or more predicted scores fail to satisfy one or more thresholds,… (Krimsky discloses an extent of patient care risk for a particular facility may also correspond to a determination that the facility score for the particular facility is below or above a threshold value, such as a facility score benchmark. A facility score benchmark may be, for example the mean or median facility score or other selected statistic for all or a subset of facilities having records in the database maintained by CCC 110 computer [0114].)
…values reflected in EHR data for a plurality of healthcare facilities; (Krimsky discloses a facility may realize significant negative treatment outcomes caused by the occurrence of sepsis in patients. For example, the facility's incidence rate of sepsis may be significantly higher than other comparable facilities due to serving a patient population that is more susceptible to sepsis [0111]. The Examiner interprets the rate of sepsis being data coming directly from patient EHR data across multiple facilities.)
and generating a plurality of predicted scores based on processing the plurality of … facility features using the…model; (Krimsky discloses the…system may refine the facility score over time to correlate with patient outcomes such as mortality, length of stay, number of transfers etc. The facility score can be correlated for a hospital ICU with patient outcomes, such that the score is a good proxy to predict that hospital’s patient outcomes (e.g., when a hospital's Charlotte Score is X %, the mortality will likely be Y %). This makes the facility score both predictive (allowing prediction of current patient outcomes as well as future patient outcomes) and prescriptive (indicating what needs to be changed to improve outcomes) [0100]. A facility score benchmark may be, for example the mean or median facility score or other selected statistic for all or a subset of facilities having records in the database maintained by CCC 110 computer [0114].)
…for each of the plurality of healthcare facilities based on corresponding predicted scores for each of the plurality of healthcare facilities; (Krimsky discloses systems according to embodiments of the invention can … facility scores from the set of facilities connected to the system to determine an extent of patient care risk for a particular facility [0102].)
and facilitating reconfiguration of the first healthcare facility based on the ranked set of facility features. (Krimsky discloses the Charlotte Score… is predictive in the sense that it allows prediction of current patient outcomes as well as future patient outcomes. The Charlotte Score is prescriptive in the sense that it indicates what needs to change to improve those outcomes. If a hospital has consistent issues (e.g., devices inappropriately left in place, prophylaxis problems, etc.), these may be identified… the data may reveal that a hospital's clinical staff consistently do not seek help despite a remote doctor's frequent indication of need for help. As a result, hospital troubleshooting may be performed, and hospital efficiency and effectiveness improved [0052-53]. The Examiner interprets providing an indication of necessary changes to a facility is interpreted as facilitating reconfiguration of the facility.)
Krimsky does not teach the following limitations met by Granson:
ranking the new set of…features based on salience to the one or more predicted scores, comprising: (Granson teaches the analytical engines of the Surgeon Check application 140 determine the target ranges and importance of each of the relevant input metrics. The application 140 then returns an overall composite score based on the input metric values and the calculated weights for each metric (col. 9, lines 21-29).)
training a machine learning model to generate predicted…scores based on the set of…features and the plurality of…scores, (Granson teaches a database includes input data metrics each related to…health care provider quality metrics, health care provider cost metrics, health care facility quality metrics, and health care facility quality metrics (abstract). The resulting evaluation scores and ratings are combined into a composite rating… This may be broken down into both an overall quality rating and an overall cost rating (col. 10, lines 12-17, see also Fig. 2). The selection of an objectively best physician and facility for an identified procedure such as the lung cancer resection procedure may be made by determining the scores of each available physician and facility from the associated input metrics (col. 12, lines 41-46). The algorithms all are trained using a model training set 1030. The model training set 1030 is a selected set of values from the database of all metric values relating to different surgical procedures (col. 21, lines 43-46, see also Fig. 5).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the systems and methods for collecting a plurality of patient data and determining a healthcare facility score as disclosed by Krimsky to incorporate training a machine learning model based on the health data and scores taught by Granson. This modification would create a system which can provide patients with scores regarding the quality of care of a healthcare facility so they can make an informed decision on their care (see Granson, col. 5, lines 1-5).
Krimsky and Granson do not teach the following limitations met by Zonooz:
for at least one feature…determining a distribution of values… (Zonooz teaches forcing the model to completely match the feature distributions…[0006].)
generating a plurality of perturbed… features by probabilistically perturbing the at least one…feature based on the distribution of values such that the perturbed at least one…feature is within realistic bounds defined based on the distribution of values; (Zonooz teaches the supervision from the natural model acts as a noise-free reference for regularizing the robust model. This effectively adds a prior on the learned representations which encourages the model to learn semantically relevant features in the input space. Coupling this with the affinity of the robust model pushes the model towards features with stable behaviour within the perturbation bound [0016].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the systems and methods for collecting a plurality of patient data and determining a healthcare facility score as disclosed by Krimsky to incorporate determining a distribution of data and then generating perturbed data within realistic bounds as taught by Zonooz. This modification would create a system and method capable of training a robust machine learning model (see Zonooz, ¶ 0003-4).
Krimsky, Granson, and Zonooz do not teach automatically filtering data which is met by Vos:
comprising at least one of automatically filtering or automatically sorting information… (Vos teaches the processor circuit may be configured to provide the graphical representation of the set of inputs automatically arranged based on the first ranking…[0006].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the systems and methods for collecting a plurality of patient data and determining a healthcare facility score as disclosed by Krimsky to incorporate automatic filtering of information as taught by Vos. This modification would create a system which filters out unnecessary data and focuses on the most impactful data to provide improved accuracy to scores (see Vos, ¶ 0003).
Regarding Claim 10, Krimsky, Granson, Zonooz, and Vos teach the limitations as shown in the rejection of Claim 9 above. Krimsky, Granson, and Zonooz do not teach the following limitations met by Vos:
identifying a set of features in the plurality of EHR data; (Vos teaches for each of the plurality of patients, the clinical records may include data corresponding to a set of inputs associated with a medical condition of the patient and data corresponding to a set of outcomes associated with the medical condition of the patient [0006].)
selecting a subset of salient features, from the set of features, using one or more feature selection operations; (Vos teaches the filter criterion corresponds to at least one of: a selection of a subset of the clinical records based on data corresponding to an input of the set of inputs, or a selection of a subset of the set of inputs [0007].)
and training the machine learning model based on the subset of salient features. (Vos teaches by training the random forest classifier using the clinical records (e.g., clinical records 120), the random forest classifier may automatically provide a feature ranking representative of the correlation between the data corresponding to each input variable of the set of input variables and the data corresponding to the driving outcome [0053].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the systems and methods for collecting a plurality of patient data and determining a healthcare facility score as disclosed by Krimsky to incorporate identifying a subset of features from the first set of features for training a model as taught by Vos. This modification would create a system which filters out unnecessary data and focuses on the most impactful data to provide improved accuracy to scores (see Vos, ¶ 0003).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Krimsky, Granson, Zonooz, and Vos in view of Gilham et al. (US 20100298718 A1).
Regarding Claim 11, Krimsky and Granson teach the limitations as shown in the rejection of Claim 9 above. Krimsky further discloses:
the plurality of EHR data comprise, for each respective healthcare facility of the plurality of healthcare facilities, at least one of:… by one or more patients of the healthcare facility. (Krimsky discloses multiple patient records are accrued over a rolling period of remote evaluation sessions, are compiled in the CCC 110 database, analyzed on an institutional level, then made available in at the hospital 130 or doctor computer 120 on a dynamic TeleICU dashboard [0050]. Doctors and hospitals are shown in this example, although …facilities 130 may be present in the network 100 [0033]. Systems according to embodiments of the invention can use treatment status indicators, treatment compliance indicators, care scores, and facility scores from the set of facilities connected to the system to determine an extent of patient care risk for a particular facility [0102].)
Krimsky, Granson, Zonooz, and Vos do not teach the following limitations met by Gilham:
a respective number of notes recorded for each patient of the respective healthcare facility during a duration of time; a respective number of times one or more vitals were recorded for each patient of the respective healthcare facility during the duration of time; a respective frequency of medication administration issues; or a respective frequency of falls experienced (Gilham teaches an input device for receiving a plurality of physical inputs from a user and communicating to a memory data indicative of the frequency at which said plurality of physical inputs are received from the user within a time period [0010]. The physiological parameter is at least one of pulse rate or respiratory rate [0011]. The monitor provides a visual indication of status, including the number of completed readings for each specified interval. The input device is a touch screen display. The monitor functions as an adjunct to the clinical staff for collection and documentation of vital sign data on multiple patients [0051].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the systems and methods for collecting a plurality of patient data and determining a healthcare facility score as disclosed by Krimsky to incorporate the features of the EHR of tracking the number of times vital signs are measured for a plurality of patients taught by Gilham. This modification would create a system and method which is able to analyze the quality of vital sign data by the number of times it is collected (see Gilham, ¶ 0008-9).)
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Krimsky, Granson, Zonooz, and Vos in view of Bua et al. (US 20030167187 A1).
Regarding Claim 12, Krimsky and Granson teach the limitations as shown in the rejection of Claim 9 above. Krimsky and Granson do not teach the following limitations met by Bua:
wherein the plurality of healthcare facilities correspond to residential care facilities. (Bua teaches the performance ratings of a health care facility can be based on data and information regarding all facilities certified by the United States government to include even those facilities with the worst or lowest ratings [0013]. The computer or server system can provide to the client system the detailed comparative report of nursing homes and long term care facilities that have characteristics that are in accord with or "match" the criteria identified in the consumer queries or formatted search requests [0017].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the systems and methods for collecting a plurality of patient data and determining a healthcare facility score as disclosed by Krimsky to incorporate the medical facility being a residential care facility as taught by Bua. This modification would create a system and method with provides people with a resource for identifying a safe, quality nursing or residential home (see Bua, ¶ 0005).
Response to Arguments
Regarding rejections under 35 U.S.C. § 101 to Claims 1-20, Applicant’s arguments have been fully considered but are not persuasive. The rejection has been updated in light of the latest amendments.
Applicant argues that with regards to Step 2A Prong One, the amended claims are not directed to an abstract idea as they are not directed toward certain methods of organizing human activity. Applicant submits that the present claims, which relate to techniques for training and using machine learning models to predict facility quality, do not recite or relate to any method of organizing human activity (see Applicant’s Remarks, p. 9-10).
Regarding (a), Examiner respectfully disagrees and maintains that the claims are directed toward certain methods of organizing human activity. Examiner notes that the training of a machine learning is not an abstract idea, which is why it is not identified as an abstract idea in the 101 analysis and rejection and is instead an additional element (which is considered in Step 2A Prong Two and Step 2B of the analysis). The claimed steps of collecting data, generating features, generating scores, ranking features based on scores, and outputting the ranking are reasonably interpreted as steps which can be followed by a health worker or person of the like. The Examiner submits that the identified claim elements represent a series of rules or instructions that a person or persons, with or without the aid of a computer, would follow to generate facility assessments (see October 2019 Update: Subject Matter Eligibility on p. 5 which states certain activity between a person and a computer may fall within the “certain methods of organizing human activity” grouping). Applicant has not pointed to anything in the claims that fall outside of this characterization. Because the claim elements fall under a series of rules or instruction that a person would follow to assess facilities, the claimed invention is directed to an abstract idea.
Applicant argues recent guidance titled “Reminders on evaluating subject matter eligibility of claims under 35 USC 101” explains prong two “considers the claim as a whole”, and “[t]he way in which the additional elements use or interact with the exception may integrate the judicial exception into a practical application”. Accordingly, “the additional limitations should not be evaluated in a vacuum, completely separate from the recited judicial exception”. Instead “the analysis should take into consideration all the claim limitations and how these limitations interact and impact each other when evaluating whether the exception is integrated into a practical application” (p. 10).
Regarding (b), Examiner agrees. However, absent of evidence in the disclosure, Examiner does not find the combination of additional elements in light of their use in the claim to amount to a practical application via (1) an improvement in the functioning of a computer or other technological field, (2) applying the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, (3) effecting a transformation or reduction of a particular article to a different state or thing, or (4) applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment (MPEP § 2106.04(d)(I)).
Applicant argues in Prong Two Step 2A the claims are eligible because they reflect an improvement in the functioning of a computer and therefore integrate the alleged judicial exception into a practical application. An important consideration in determining whether a claim improves technology or a technical field is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution of outcome”. To do so, one must “consult the specification to determine whether the disclosed invention improves technology”. Examiners are cautioned not to oversimplify claim limitations and expand the application of the ‘apply it’ consideration. Here, the amended claims are eligible because they reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field and integrate the alleged judicial exception into a practical application. For example, the computing system may “generate… the ranked features based on determining that the predicted score and/or the actual score of the facility satisfies one or more criteria (Applicant’s specification, ¶ 0118). The system may rank the features “in response to determining that the predicted score and/or actual score fall below a threshold (e.g., indicating that changes may be useful, and that outputting the ranked features may be beneficial). By only generating rankings when the initial evaluation indicates that doing so would be helpful, the system is able to substantially reduce the computational expense of the process (p. 11-12).
Regarding (c), Examiner respectfully disagrees. The claims recite a machine learning model and a GUI, both recited at a high level of generality such that they equate to stating the term “apply it”. The recited machine learning model does not provide any specific details regarding how the model is run or what improvements it brings to the computer, and the GUI is used for the general purpose of displaying. As the claims are stated, the steps the machine learning takes could be carried out on any general purpose computer using conventional technology. MPEP 2106.05(a) states that the courts have found that the following does not show an improvement in computer-functionality: Arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly, Trading Technologies v. IBG LLC, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019). There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. The additional elements recited in the claims were evaluated under the “significantly more” analysis and were determined to be insufficient to provide significantly more. MPEP 2106.05(I) states mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
Examiner notes that the stated problem of assessing medical facilities is interpreted as not being rooted in technology. The problem is not caused by nor related to computer technology and the claims do not provide any limitations that may be interpreted as technical improvements to computer technology. Therefore, there is no technological problem presented.
Applicant argues the claims are similar to Example 40 of the USPTO’s Subject Matter Eligibility Examples, which involves adaptive monitoring of network traffic. As the guidance explains, by only performing additional analysis when abnormal conditions are detected (based on an initial analysis), the system avoids excess traffic volume and computational expense. The present claims, which have been amended to recite ranking the facility features “in response to determining that the one or more predicted scores fail to satisfy one or more thresholds” similarly reduce the computational burden on the computing system (p. 12).
Regarding (d), Examiner respectfully disagrees because the instant claims are not analogous to Claim 1 of Example 40. Claim 1 of Example 40 integrated the abstract idea into a practical application because the combination of additional elements provided a specific improvement over prior systems and resulted in improved network monitoring. Conversely, the limitation of presenting a ranking of features upon determining that a score does not satisfy a threshold provides no apparent improvement to the technical field of to the functioning of a computer. Ranking values in response to an analysis does not improve upon the functioning of the computer because it is merely automating a manual process. Further, the ranked values do not affect the functioning of the computer’s functioning and operation as they are independent from one another. Language such as concurrently, automatically, instantly, or simultaneously to describe the automation of a manual process is not enough to overcome a subject matter eligibility rejection (MPEP § 2106.05(a)(I). Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality no. (iii) mere automation of manual processes). MPEP 2106.04(d)(1) and MPEP 2106.05(a) indicate that a practical application may be present where the claimed invention provides a technical solution to a technical problem. See, e.g., DDR Holdings, LLC. V. Hotels.com, L.P., 773 F.3d 1245, 1259 (Fed. Cir. 2014) (finding that claiming a website that retained the “look and feel” of a host webpage provided a technological solution to the technological problem of retention of website visitors by utilizing a website descriptor that emulated the “look and feel” of the host webpage, where the problem arose out of the internet and was thus a technical problem). The additional elements, whether alone or in combination, do not amount to more than a generic computer component or a generic system architecture (i.e., generic machine learning model).
Applicant argues the claims have been amended to recite performing at least one of “automatically filtering or automatically sorting information for each of the plurality of healthcare facilities based on corresponding predicted scores for each of the plurality of healthcare facilities.” Applicant submits that this dynamic GUI ordering and filtering based on machine learning-generated scores improves the functioning of the computer itself, in a similar manner to Claim 1 of Example 38 (p. 12).
Regarding (e), Examiner respectfully disagrees. As stated in (d) above, the courts have indicated that the mere automation of a manual process cannot provide a technical solution to a technical problem. Further, the instant claims are not analogous to Claim 1 of Example 38 because in this example, the claim was found to be eligible due to it not reciting a judicial exception. The instant claims do recite a judicial exception (abstract idea) which is identified in the 101 rejection above.
Examiner notes that a claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (see MPEP § 2106.04(d) - Integration of a Judicial Exception Into A Practical Application). The court has provided limitations that are indicative that an additional element (or combination of elements) may have integrated the exception into a practical application and limitations that did not integrate a judicial exception into a practical application (see MPEP § 2106.04(d)(I) – Relevant Considerations for Evaluating Whether Additional Elements integrate a Judicial Exception into a Practical Application) wherein the claims may amount to (1) improvements to the functioning of a computer, (2) improvements to a technological field, (3) applying the judicial exception to a particular machine (as evaluated above in ¶ ), (4) transforming or reducing a particular article to a different state or thing, (5) unconventional activity or steps that confine the claim to a particular useful application, or (6) other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment. Here the instant claims seem more analogous to "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
Applicant argues as the August Memorandum reminds, “if it is a ‘close call’ as to whether a claim is eligible…should only make a rejection when it is more likely than not that the claim is ineligible under 35 USC 101”. That is, “a rejection of a claim should not be made simply because an examiner is uncertain as to the claim’s eligibility,” (p. 12).
Regarding (f), Examiner has considered Applicant’s statements and notes claims are not a situation where this applies because it is not more likely than not.
Regarding rejections under 35 U.S.C. § 103 to Claims 1-20, Applicant’s arguments have been fully considered and are persuasive in light of the amendments. Therefore, the rejection has been withdrawn. However, upon further consideration, a new rejection has been made, rejecting Claim 1 over Krimsky in view of Vos and Zonooz.
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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OLIVIA R GEDRA whose telephone number is (571)270-0944. The examiner can normally be reached Monday - Friday 8:00am-5:00pm.
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.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/OLIVIA R. GEDRA/Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681