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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 USC 101 because the claimed invention is directed to an abstract idea without significantly more.
It is appropriate for the Examiner to determine whether a claim satisfies the criteria for subject matter eligibility by evaluating the claim in accordance to the Subject Matter Eligibility Test as recited in the following Steps: 1, 2A, and 2B, see MPEP 2106(III.).
Patent Subject Matter Eligibility Test: Step 1:
First, the Examiner is to establish whether the claim falls within any statutory category including a process, a machine, manufacture, or composition of matter, see MPEP 2106.03(II.) and MPEP 2106.03(I).
Claims 1-10 are related to a system, and claims 11-18 are also related to a method (i.e., a process). Claims 19-20 are related to a “non-transitory” processor readable media storing instructions. Accordingly, these claims are all within at least one of the four statutory categories.
Patent Subject Matter Eligibility Test: Step 2A- Prong One:
Step 2A of the Subject Matter Eligibility Test demonstrates whether a clam is directed to a judicial exception, see MPEP 2106.04(I.). Step 2A is a two-prong inquiry, where Prong One establishes the judicial exception. Regarding Prong One of Step 2A, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes, see MPEP 2106.04(II.)(A.)(1.) and 2106.04(a)(2).
Representative independent claim 1 includes limitations that recite at least one abstract idea as underlined in the following limitations. Specifically, independent claim 1 recites:
A system, comprising:
a memory that stores computer executable components; and a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise:
a training component that performs one or more machine learning processes to learn patterns in historical census data and historical patient flow data related to a flow of patients in and out of respective beds in a group at a medical facility, wherein the training component generates one or more census forecasting models based on the patterns, wherein the historical patient flow data comprises patient journey data tracked for a plurality of patients regarding their journeys at the medical facility, the patient journey data comprising information regarding workflow events, condition of the patients, operations of the medical facility and temporal relationships between the workflow events, the conditions of the patients and the operations, or a combination thereof;
a patient census component that applies the one or more census forecasting models to current patient flow data for the medical facility to forecast an expected occupancy level for the group during one or more future periods of time, wherein a forecasting optimization component tunes one or more parameters of the one or more census forecasting models for the group prior to application by the patient census component to the current patient flow data to forecast the expected occupancy level for the group; and
an alert component that provides an alert to a device associated with the medical facility in response to the expected occupancy level meeting a threshold value.
The Examiner submits that the foregoing underlined limitations constitute “certain methods of organizing human activity”, more specifically managing interactions between people as the following abstract limitations recite providing an alert in response to the expected occupancy level meeting a threshold value:
“learn” patterns in historical census data and historical patient flow data related to a flow of patients in and out of respective beds in a group at a medical facility, wherein the historical patient flow data comprises patient journey data tracked for a plurality of patients regarding their journeys at the medical facility, the patient journey data comprising information regarding workflow events, condition of the patients, operations of the medical facility and temporal relationships between the workflow events, the conditions of the patients and the operations, or a combination thereof, which are abstract limitations of analysis and management of the flow of other people, the patients, in and out of beds in a hospital as described,
Use of current patient flow data for the medical facility for the “forecast” of an expected occupancy level for the group during future period of times, which are abstract limitations related to analysis and management of the patient flow and occupancy level of patients,
“providing” an alert in response to the expected occupancy level meeting a threshold value, which is an abstract limitation of a communication of an alert of the expected occupancy
The claim limitations as a whole recite steps for providing an alert in response to the expected occupancy level meeting a threshold value, which recites a communication of the expected occupancy level to be interpreted as managing interactions between people.
The abstract ideas recited in claims 11 and 19 are similar to that of claim 1.
Any limitations not identified above as part of the abstract idea are deemed “additional elements” (i.e., processor) and will be discussed in further detail below.
Accordingly, the claim as a whole recites at least one abstract idea.
Furthermore, dependent claims further define the at least one abstract idea, and thus fails to make the abstract idea any less abstract as noted below:
Claim 8 recites further abstract limitations of defining the group of beds at the medical facility based on at least one grouping factor, wherein the at least one grouping factor comprises a unit grouping factor identifying two or more inpatient units of the medical facility, and wherein the grouping component groups the bed based on association of the beds with the two or more inpatient units, further describing the abstract idea. Claim 9 recites further abstract limitations of defining the group of beds at the medical facility based on at least one grouping factor, wherein the at least one grouping factor comprise a service line grouping factor identifying at least one service line at the medical facility and wherein the grouping component groups the bed based on association of the beds with the at least one service line, further describing the abstract idea. Claim 10 recites further abstract limitations of the grouping component that defines the group of beds at the medical facility based on at least one grouping factor, wherein the at least one grouping factor comprises a bed attribute shared by the beds in the group and a time period of the one or more future periods of time, and wherein a group stability component determines the measure of occupancy variability for the time period, further describing the abstract idea.
Patent Subject Matter Eligibility Test: Step 2A- Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. It must be determined whether any additional elements in the claim beyond the abstract idea integrates the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exceptions into a “practical application,” see MPEP 2106.04(II.)(A.)(2.) and 2106.04(d)(I.).
In the present case, the additional limitations beyond the above-noted at least one abstract idea are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
A system, comprising:
a memory that stores computer executable components; and a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)):
a training component that performs one or more machine learning processes to (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)) learn patterns in historical census data and historical patient flow data related to a flow of patients in and out of respective beds in a group at a medical facility, wherein the training component generates one or more census forecasting models based on the patterns (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)), wherein the historical patient flow data comprises patient journey data tracked for a plurality of patients regarding their journeys at the medical facility, the patient journey data comprising information regarding workflow events, condition of the patients, operations of the medical facility and temporal relationships between the workflow events, the conditions of the patients and the operations, or a combination thereof;
a patient census component that applies the one or more census forecasting models to (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)) current patient flow data for the medical facility to forecast an expected occupancy level for the group during one or more future periods of time, wherein a forecasting optimization component tunes one or more parameters of the one or more census forecasting models for the group prior to application by the patient census component (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)) to the current patient flow data to forecast the expected occupancy level for the group; and
an alert component that provides an alert to a device associated with the medical facility (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)) in response to the expected occupancy level meeting a threshold value.
For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitations of:
the overall computing system comprising memory and processor,
a training component that performs a machine learning process, the training component generating one or more census forecasting models based on patterns,
use of a patient census component that applies the one or more census forecasting models to perform steps,
use of a forecasting optimization component to tune one or more parameters of the one or more census forecasting models for the group prior to application of the patient census component, and
use of an alert component with a device associated with the medical facility,
the Examiner submits that these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components (see MPEP § 2106.05(f)). [0141] of the Applicant’s Specification recites the overall generic computing system with generic processors and memory. [0109] recites the use of a generic training component with generic implementation of a generic machine learning process. [0109] recites the generic generation of a census forecasting models based on the patterns, using basic machine learning to generate the other model. [0085] recites the use of a generic patient census component that uses the previously generated model and generic machine learning. [0117] recites the use of a generically made forecasting optimization model to tune the parameters of the forecast model in a non-specific implementation. [0064] recites the use of a generic device with an alert component that demonstrates generic implementation using a computing device with a user interface. The additional elements recite the use of generic computing components with a non-specific implementation to carry out steps of the abstract idea without showing an improvement to technology, computers or other technical fields, and thus recites mere instructions to implement the abstract idea on a computer.
In addition to the elements of claim 1, claims 11 and 19 further recite the element of selecting, by the system, the one or more census forecasting models for the group based on a measure of occupancy variability. However, the Examiner submits that these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components (see MPEP § 2106.05(f)). [0106] of the Applicant’s Specification recites the generic implementation of the trained machine learning with the occupancy variability. The additional elements recite the use of generic computing components with a non-specific implementation to carry out steps of the abstract idea without showing an improvement to technology, computers or other technical fields, and thus recites mere instructions to implement the abstract idea on a computer.
Taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to provide an alert, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception, see MPEP 2106.04(d), 2106.05(a), 2106.05(b).
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set below:
Claims 2, 12, and 20 recite further additional elements of a number and type of the one or more census forecasting models selected varies as a function of the measure of occupancy variability, however this amounts to nothing more than an instruction to apply the abstract idea using generic computing components. Claims 3, 13 recite further additional elements of using different forecasting models using weights and combines outputs of the different models to perform the step of the abstract idea related to forecasting, however this amounts to nothing more than an instruction to apply the abstract idea using generic computing components. Claims 4,5 and 14 recite further elements of the estimating for the models using non-convex optimization techniques comprising quadratic programming, however this amounts to nothing more than an instruction to apply the abstract idea using generic computing components. Claims 6, 7 and 17, 18 recites the use of heterogenous graphs and label propagation for inferring bed placement patterns and to extract features, however this amounts to nothing more than an instruction to apply the abstract idea using a generic computer. Claim 8 recites the use of a grouping component, however this amounts to nothing more than an instruction to apply the abstract idea using a generic computer. Claims 15 and 16 recite further of training the census forecasting models based on historical data as claimed and tailoring the models based on the patterns, however this amounts to nothing more than an instruction to apply the abstract idea using generic computing components. All of the additional elements provided in the dependent claims are claimed without showing an improvement to technology, computers or other technical fields, and thus recites mere instructions to implement the abstract idea on a computer.
Thus, taken alone and in ordered combination, the additional elements do not integrate the at least one abstract idea into a practical application.
Patent Subject Matter Eligibility Test: Step 2B:
Regarding Step 2B of the Subject Matter Eligibility Test, the independent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application, see MPEP 2106.05(II.). Further, it may need to be established, when determining whether a claim recites significantly more than a judicial exception, that the additional elements recite well understood, routine, and conventional activities, see MPEP 2106.05(d).
Regarding the additional limitations of:
the overall computing system comprising memory and processor,
a training component that performs a machine learning process, the training component generating one or more census forecasting models based on patterns,
use of a patient census component that applies the one or more census forecasting models to perform steps,
use of a forecasting optimization component to tune one or more parameters of the one or more census forecasting models for the group prior to application of the patient census component, and
use of an alert component with a device associated with the medical facility,
the Examiner submits that these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components (see MPEP § 2106.05(f)). [0141] of the Applicant’s Specification recites the overall generic computing system with generic processors and memory. [0109] recites the use of a generic training component with generic implementation of a generic machine learning process. [0109] recites the generic generation of a census forecasting models based on the patterns, using basic machine learning to generate the other model. [0085] recites the use of a generic patient census component that uses the previously generated model and generic machine learning. [0117] recites the use of a generically made forecasting optimization model to tune the parameters of the forecast model in a non-specific implementation. [0064] recites the use of a generic device with an alert component that demonstrates generic implementation using a computing device with a user interface. The additional elements recite the use of generic computing components with a non-specific implementation to carry out steps of the abstract idea without showing an improvement to technology, computers or other technical fields, and thus recites mere instructions to implement the abstract idea on a computer and does not recite significantly more than the judicial exception.
In addition to the elements of claim 1, claims 11 and 19 further recite the element of selecting, by the system, the one or more census forecasting models for the group based on a measure of occupancy variability. However, the Examiner submits that these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components (see MPEP § 2106.05(f)). [0106] of the Applicant’s Specification recites the generic implementation of the trained machine learning with the occupancy variability. The additional elements recite the use of generic computing components with a non-specific implementation to carry out steps of the abstract idea without showing an improvement to technology, computers or other technical fields, and thus recites mere instructions to implement the abstract idea on a computer and does not recite significantly more than the judicial exception.
The dependent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exceptions for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
For the reasons stated, the claims fail the Subject Matter Eligibility Test and therefore claims 1-20 are rejected under 35 USC 101 as being directed to non-statutory subject matter.
The following references have been considered as relevant, however have not been used in the above rejection:
US-20110087502-A1 to Yelton et al. teaches of a system for predicting availability of beds based on analyzing patient data in a database and predicting time frames of availability.
US-20100228565-A1 to Rosow et al. teaches of a system for maximizing bed resources in a hospital facility using real time patient data.
WO-2018112185-A1 to Gravenor et al. teaches of a system for predicting the capacity of medical facilities using models for patient volume and flow.
NPL “Data-driven decision-support for process improvement through predictions of bed occupancy rates” to Tran et al. teaches of a system for determining bed occupation rates for a hospital using linear regression.
These references do not teach aspects of the current invention including but not limited to: “wherein the executing comprises generating one or more census forecasting models based on the patterns, wherein the historical patient flow data comprises patient journey data tracked for a plurality of patients regarding their journeys at the medical facility, the patient journey data comprising information regarding workflow events, condition of the patients, operations of the medical facility and temporal relationships between the workflow events, the conditions of the patients and the operations, or a combination thereof; selecting, by the system, the one or more census forecasting models for the group based on a measure of occupancy variability; applying, by the system, the one or more census forecasting models to current patient flow data for the medical facility to forecast an expected occupancy level for the group during one or more future periods of time; tuning one or more parameters of the one or more census forecasting models for the group prior to application to the current patient flow data to forecast the expected occupancy level for the group”
Conclusion
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/CONSTANTINE SIOZOPOULOS/
Examiner
Art Unit 3686