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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/05/2026 has been entered.
Response to Arguments
Regarding the arguments against the rejection of claims under 35 USC 101, the Examiner respectfully disagrees. Applicant argues that the claims do not recite a judicial exception. Examiner asserts that the use of the trained machine learning models recites the use of generic computing devices to carry out the abstract idea as described in the Step 2A Prong 2 analysis below of this Office Action. The steps of predicting and refining a fall score for an individual and initiating appropriate intervention as claimed recites organizing human activity, as these steps are for the managing of human interactions as analyzed in Step 2A Prong One recited below. Examiner did not show that the claimed invention recites a mental process. Further, the use of AI in the claims is used in a generic manner and amounts to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components. Example 39 of the Subject Matter Eligibility Examples does not recite an abstract idea; however, the instant application does recite an abstract idea as described below in Step 2A Prong One. Example 47 recites some claims where the training recites mere computer implementation and does not recite a technology improvement, however the instant application recites the use of generic computing components to carry out the abstract idea.
Applicant further argues that under Step 2A Prong 2, the claims recite a technical improvement in that the claims allows for the re-evaluation of only a subset of the plurality of individuals rather than the entire monitored population based on a threshold, therefore reducing computational expenses associated with re-computing the scores and receiving the data necessary to perform said re-computations. Examiner further asserts that there is no indication of a technical improvement as the claims are clearly using generic computing components such as processors and machine learning models. As claimed, using less steps in the method by considering the risk criteria to then not compute a second score while still using the high-level-of-generality hybrid MLM does not demonstrate a clear technical improvement that fundamentally improves the functioning of the computer and does meaningfully limit the claim, see MPEP 2106.05(f). Further, the use of the “adjustment factor” for the fall prediction score is part of the abstract idea as analyzed below, and does not demonstrate a technology improvement related to the use of the hybrid MLM. Improving the accuracy and efficiency of the output of the algorithm, where the output is the abstract score, and then “enhancing the prophylactic effect of allocating resources to individuals demonstrates part of the abstract idea and does not recite a technical improvement, See MPEP 2106.05(f), specifically” "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).” Again, use of the trained hybrid machine learning model recites generic computer implementation, and the steps for “initiating” intervention are abstract as shown in Step 2A Prong One below.
Examiner further asserts that the Examiner is not uncertain as to the claim’s ineligibility, see rejection below.
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 1-7, 9, 11-25, 27-30 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 1-7, 9, 11-13, and 24, 25 are related to a system, and claims 14-15 and 27-30 are also related to a method (i.e., a process). Claims 16-23 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).
Independent claim 1 includes limitations that recite at least one abstract idea as underlined in the following limitations. Specifically, independent claim 1 recites:
An apparatus, the apparatus comprising:
a memory to store instructions; and
processing circuitry, coupled with the memory, operable to execute the instructions, that when executed, cause the processing circuitry to:
apply a hybrid machine learning model (MLM) to a plurality of data associated with one or more individuals;
compute a fall prediction score for the one or more individuals based in whole or in part on application of the hybrid MLM;
generate, for each of the one or more individuals, an adjustment factor for the fall prediction score based on previous fall history associated with the individual;
adjust each fall prediction score based on the respective adjustment factor;
based on the adjusted fall prediction score for the one or more individuals, initiate prophylactic intervention for an individual of the one or more individuals, wherein the prophylactic intervention includes at least one prophylactic media component, the processing circuitry configured to:
select the prophylactic media component based on the individual; and
output the prophylactic media component to the individual;
in response to determining that the adjusted fall prediction score of the individual satisfies a risk criteria, compute a second fall prediction score for the individual using the hybrid MLM; and
in response to determining that the adjusted fall prediction score of a second individual does not satisfy the risk criteria, refrain from computing a second fall prediction score for the individual using the hybrid MLM.
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 are for outputting a prophylactic media component to an individual and further analyzing the risk of falling for the individual:
“compute” a fall prediction score for the one or more individuals, which is an abstract limitation related to an analysis of the data from the individual,
“generate”, for each of the one or more individuals, an adjustment factor for the fall prediction score based on previous fall history associated with the individual and “adjust” each fall prediction score based on the respective adjustment factor, which are abstract limitations of analysis of the fall history to be used for a determination of an adjustment factor for the prediction score and then adjust the abstract fall prediction score,
“initiate” prophylactic intervention for an individual of the one or more individuals based on the adjust fall prediction score, wherein the prophylactic intervention includes at least one prophylactic media component where the component is selected based on the individual and then “outputted”, which are abstract limitations of an interaction with the individual to give prophylactic media component to the individual based on the previous abstract analysis of the score,
“compute” a second fall prediction score for the individual in response to determining that the adjusted fall prediction score satisfies a risk criteria, which recites further abstract limitation of analysis of risk and the previously generated adjusted fall score for further consideration of predicting a fall for the individual,
“refrain from computing” a second fall prediction score if the adjusted prediction score does not satisfy the risk criteria, which recites further abstract limitation of analysis of risk and the previously generated adjusted fall score for further consideration of predicting a fall for the individual.
Accordingly, the claim as a whole recites outputting a prophylactic media component to an individual and further analyzing the risk of falling for the individual, which recites an interaction with the individual by giving them the appropriate media components and further analysis of a prediction for a fall; these steps recite the management of the care for the individual related to them falling, and thus constitutes certain methods of organizing human activity, more specifically managing interactions between people.
Regarding claim 14:
A computer-implemented method, the method comprising:
receiving, by one or more computer processors, a first plurality of medical data associated with a first plurality of individuals;
training, by the one or more computer processors, a machine learning model (MLM) based on the received first plurality of medical data, such that the trained MLM is able to process a second plurality of medical data associated with a second plurality of individuals and output a fall prediction score associated with each of the second plurality of individuals, wherein the fall prediction score is an estimation of a likelihood, respectively, that each of the second plurality of individuals will suffer a fall, and wherein at least a portion of the first plurality of data and a portion of the second plurality of data are based on one or more pre-determined fillable forms associated with a value range;
generating, by the one or more computer processors and for each of the second plurality of individuals, an adjustment factor for the fall prediction score based on previous fall history associated with the respective individual;
adjusting, by the one or more computer processors, each of the fall prediction scores based on the respective adjustment factor;
based on the adjusted fall prediction score for one or more individuals of the second plurality of individuals, initiating, by the one or more computer processors, prophylactic intervention for one or more individuals, wherein the prophylactic intervention includes at least one prophylactic media component, wherein initiating the prophylactic intervention comprises:
for each of the one or more individuals, selecting the prophylactic media component based on the individual; and
for each of the one or more individuals, outputting the prophylactic media component;
in response to determining that the adjusted fall prediction score of one or more individuals of the second plurality of individuals satisfies a risk criteria, computing, by the one or more computer processors, a second fall prediction score for the one or more individuals using the trained MLM; and
in response to determining that the adjusted fall prediction score of a second one or more individuals of the second plurality of individuals does not satisfy the risk criteria, refrain from computing a second fall prediction score for the second one or more individuals using the trained MLM.
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 are for outputting a prophylactic media component to individuals and further analyzing the risk of falling for the individuals:
“processing” a second plurality of medical data associated with a second plurality of individuals and “output” a fall prediction score estimating the likelihood of the induvial falling associated with each of the second plurality of individuals, which is an abstract limitation related to an analysis of information from the one or more individuals,
where the second at least a portion of the second plurality of data is based on pre-determined fillable forms associated with a range value, which indicates an observation of data of the one or more individuals to be used for the analysis,
“generate”, for each of the second plurality of individuals, an adjustment factor for the fall prediction score based on previous fall history associated with the respective individual and “adjust” each fall prediction score based on the respective adjustment factor, which are abstract limitations of analysis of the fall history to be used for a determination of an adjustment factor for the prediction score and then adjust the abstract fall prediction score,
“initiate” prophylactic intervention for one or more individuals based on the adjust fall prediction scores, wherein the prophylactic intervention includes at least one prophylactic media component where the component is selected based on the individual and then “outputted” for each individual, which are abstract limitations of an interaction with the individual to give prophylactic media component to the individuals based on the previous abstract analysis of the score,
“compute” a second fall prediction score for the individuals of the second plurality of individuals in response to determining that the adjusted fall prediction score satisfies a risk criteria, which recites further abstract limitation of analysis of risk and the previously generated adjusted fall score for further consideration of predicting a fall for the individual,
“refrain from computing” a second fall prediction score if the adjusted prediction score does not satisfy the risk criteria for each of the individuals of the second plurality of individuals, which recites further abstract limitation of analysis of risk and the previously generated adjusted fall score for further consideration of predicting a fall for the individual.
Accordingly, the claim as a whole recites outputting a prophylactic media component to individuals and further analyzing the risk of falling for the individuals, which recites an interaction with the individuals by giving them the appropriate media components and further analysis of a prediction for a fall; these steps recite the management of the care for the individuals related to them falling, and thus constitutes certain methods of organizing human activity, more specifically managing interactions between people.
Regarding claim 16:
A non-transitory computer-readable storage medium storing computer-readable program code executable by a processor to:
apply a machine learning model (MLM) to a plurality of data associated with at least one individual;
compute an initial fall risk score for the at least one individual based on the application of the MLM;
generate an adjustment factor for the initial fall risk score based on a previous fall history associated with the individual;
adjust the initial fall risk score based on the adjustment factor;
based on the adjusted fall risk score for the individual, initiate prophylactic intervention for the individual, wherein the prophylactic intervention includes at least one prophylactic media component, the processor configured to:
select the prophylactic media component based on the individual; and
output the prophylactic media component to the individual;
in response to determining that the adjusted fall risk score of the individual satisfies a risk criteria, compute a second fall risk score for the individual using the MLM; and
in response to determining that the adjusted fall risk score of a second individual does not satisfy the risk criteria, refraining from computing a second fall risk score for the individual using the MLM.
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 are for outputting a prophylactic media component to an individual and further analyzing the risk of falling for the individual:
“compute” an initial fall prediction score for the individual, which is an abstract limitation related to an analysis of the data from the individual,
“generate”, for the individual, an adjustment factor for the fall prediction score based on previous fall history associated with the individual and “adjust” each fall prediction score based on the respective adjustment factor, which are abstract limitations of analysis of the fall history to be used for a determination of an adjustment factor for the prediction score and then adjust the abstract fall prediction score,
“initiate” prophylactic intervention for the individual of the one or more individuals based on the adjust fall prediction score, wherein the prophylactic intervention includes at least one prophylactic media component where the component is selected based on the individual and then “outputted”, which are abstract limitations of an interaction with the individual to give prophylactic media component to the individual based on the previous abstract analysis of the score,
“compute” a second fall prediction score for the individual in response to determining that the adjusted fall prediction score satisfies a risk criteria, which recites further abstract limitation of analysis of risk and the previously generated adjusted fall score for further consideration of predicting a fall for the individual,
“refrain from computing” a second fall prediction score if the adjusted prediction score does not satisfy the risk criteria, which recites further abstract limitation of analysis of risk and the previously generated adjusted fall score for further consideration of predicting a fall for the individual.
Accordingly, the claim as a whole recites outputting a prophylactic media component to an individual and further analyzing the risk of falling for the individual, which recites an interaction with the individual by giving them the appropriate media components and further analysis of a prediction for a fall; these steps recite the management of the care for the individual related to them falling, and thus constitutes certain methods of organizing human activity, more specifically managing interactions between people.
Regarding claim 24:
An apparatus, the apparatus comprising:
a memory to store instructions; and
processing circuitry, coupled with the memory, operable to execute the instructions, that when executed, cause the processing circuitry to:
receive a plurality of medical data associated with a plurality of individuals;
compute a fall prediction score associated with each of the plurality of individuals based on the received plurality of medical data; and
generate, for each of the plurality of individuals, an adjustment factor for the fall prediction score based on previous fall history associated the individual;
adjust each fall prediction score based on the respective adjustment factor;
based on each of the adjusted fall prediction score and a medical profile of each of the plurality of individuals, initiate prophylactic intervention for each of the plurality of individuals, wherein the prophylactic intervention includes at least one prophylactic media component, the processing circuity configured to:
select the prophylactic media component based on the individual; and
output the prophylactic media component to the individual;
in response to determining that the adjusted fall prediction score of an individual of the plurality of individuals satisfies a risk criteria, compute a second fall prediction score for the individual; and
in response to determining that the adjusted fall prediction score of a second individual of the plurality of individuals does not satisfy the risk criteria, refrain from computing a second fall prediction score for the individual.
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 are for outputting a prophylactic media component to an individual and further analyzing the risk of falling for the individual:
“compute” a fall prediction score for the one or more individuals based on the received medical data, which is an abstract limitation related to an analysis of the data from the individual,
“generate”, for each of the one or more individuals, an adjustment factor for the fall prediction score based on previous fall history associated with the individual and “adjust” each fall prediction score based on the respective adjustment factor, which are abstract limitations of analysis of the fall history to be used for a determination of an adjustment factor for the prediction score and then adjust the abstract fall prediction score,
“initiate” prophylactic intervention for an individual of the one or more individuals based on the adjust fall prediction score and a medical profile for each individual, wherein the prophylactic intervention includes at least one prophylactic media component where the component is selected based on the individual and then “outputted”, which are abstract limitations of an interaction with the individual to give prophylactic media component to the individual based on the previous abstract analysis of the score,
“compute” a second fall prediction score for the individual in response to determining that the adjusted fall prediction score satisfies a risk criteria, which recites further abstract limitation of analysis of risk and the previously generated adjusted fall score for further consideration of predicting a fall for the individual,
“refrain from computing” a second fall prediction score if the adjusted prediction score does not satisfy the risk criteria, which recites further abstract limitation of analysis of risk and the previously generated adjusted fall score for further consideration of predicting a fall for the individual.
Accordingly, the claim as a whole recites outputting a prophylactic media component to an individual and further analyzing the risk of falling for the individual, which recites an interaction with the individual by giving them the appropriate media components and further analysis of a prediction for a fall; these steps recite the management of the care for the individual related to them falling, and thus constitutes certain methods of organizing human activity, more specifically managing interactions between people.
Regarding claim 27:
A method comprising:
receiving a first plurality of data associated with a plurality of individuals;
applying a hybrid machine learning algorithm to the first plurality of data;
generating an initial score based on an application of the hybrid machine learning algorithm to the first plurality of data;
generating an adjustment factor for the initial score based on previous fall history associated with the respective individual;
generating a second score by applying the adjustment factor to the initial score, wherein the adjustment factor is based on a fall history data of the plurality of individuals;
receiving a second plurality of data associated with the one or more individuals;
applying a second algorithm to the second plurality of data associated with the plurality of individuals;
generating a third score based on application of the second algorithm;
generating a final score based on the second score and the third score, where the final score is a prediction as to whether or not the plurality of individuals will sustain a fall, and
based on the final score for the plurality of individuals, initiating, prophylactic intervention for the plurality of individuals, wherein the prophylactic intervention is tailored to one or more profiles of the plurality of individuals, wherein the prophylactic intervention includes at least one prophylactic media component, and wherein initiating the prophylactic intervention comprises:
selecting the prophylactic media component based on the individual; and
outputting the prophylactic media component;
in response to determining that the final score of an individual of the plurality of individuals satisfies a risk criteria, computing a second fall prediction score for the individual using the hybrid machine learning algorithm; and
in response to determining that the final score of a second individual of the plurality of individuals does not satisfy the risk criteria, refraining from computing a second fall prediction score for the second individual using the hybrid machine learning algorithm.
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 are for outputting a prophylactic media component to an individual and further analyzing the risk of falling for the individual:
“generating” an initial score, which is an abstract limitation related to an evaluation for an individual,
“generate” an adjustment factor for the fall prediction score based on previous fall history associated with the individual, which are abstract limitations of analysis of the fall history to be used for a determination of an adjustment factor for the prediction score,
“generating” a second score by applying the adjustment factor, where the adjustment factor is based on fall history, which is an abstract limitation related to an evaluation of generating a score based on the observation of the fall history data and the adjustment factor for the individual,
“generating” a third score, which is an abstract limitation related to an evaluation for the individual,
“generating” a final score based on the second and third scores, where the score is a prediction if the individuals will sustain a fall, which is an abstract limitation of an evaluation of the previous scores for the individual,
“initiate” prophylactic intervention for an individual of the one or more individuals based on the final score and a medical profile for each individual, wherein the prophylactic intervention includes at least one prophylactic media component where the component is selected based on the individual and tailored to the medical profile of the individual and then “outputted”, which are abstract limitations of an interaction with the individual to give prophylactic media component to the individual based on the previous abstract analysis of the score,
“compute” a second fall prediction score for the individual in response to determining that the adjusted fall prediction score satisfies a risk criteria, which recites further abstract limitation of analysis of risk and the previously generated adjusted fall score for further consideration of predicting a fall for the individual,
“refrain from computing” a second fall prediction score if the adjusted prediction score does not satisfy the risk criteria, which recites further abstract limitation of analysis of risk and the previously generated adjusted fall score for further consideration of predicting a fall for the individual.
Accordingly, the claim as a whole recites outputting a prophylactic media component to an individual and further analyzing the risk of falling for the individual, which recites an interaction with the individual by giving them the appropriate media components and further analysis of a prediction for a fall; these steps recite the management of the care for the individual related to them falling, and thus constitutes certain methods of organizing human activity, more specifically managing interactions between people.
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.
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 3 recites the abstract limitation of “determining” weights associated with the first plurality of data and “determining” weights for the second plurality of data where the data is used for generating the score, thus further describing the abstract idea. Claim 11 recites abstract limitations of further describing the assigning of resources as based on medical profile of the individual, thus further describing the abstract idea. Claim 16 recites abstract limitations of “computing” an initial fall risk score for the individual and “generating” a final risk score by applying an adjustment factor, thus further describing the abstract idea. Claim 18 recites abstract limitations further describing the data as either diagnostic or medication data, further describing the abstract idea. Claim 21 recites abstract limitations for “determining” first and second set of weights related to the data, thus further describing the abstract idea. Claim 28 recites abstract limitation of “assigning” a resource to prevent a fall for the individual based on the final score, 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”):
Regarding claim 1:
An apparatus, the apparatus comprising:
a memory to store instructions; and
processing circuitry, coupled with the memory, operable to execute the instructions, that when executed, cause the processing circuitry 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)):
apply a hybrid machine learning model (MLM) to a plurality of data associated with one or more individuals (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f));
compute a fall prediction score for the one or more individuals based in whole or in part on application of the hybrid MLM (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f));
generate, for each of the on or more individuals, an adjustment factor for the fall prediction score based on previous fall history associated with the individual;
adjust each fall prediction score based on the respective adjustment factor;
based on the adjusted fall prediction score for the one or more individuals, initiate prophylactic intervention for an individual of the one or more individuals, wherein the prophylactic intervention includes at least one prophylactic media component, the processing circuitry configured to:
select the prophylactic media component based on the individual; and
output the prophylactic media component to the individual;
in response to determining that the adjusted fall prediction score of the individual satisfies a risk criteria, compute a second fall prediction score for the individual using the hybrid MLM; and
in response to determining that the adjusted fall prediction score of a second individual does not satisfy the risk criteria, refrain from computing a second fall prediction score for the individual using the hybrid MLM.
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 limitation of the overall apparatus with the memory and processing circuitry, applying a hybrid MLM to a plurality of data, and the application of the hybrid MLM, 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)). [0057-0058] of the Applicant’s Specification recites the use of generic computing components for the apparatus system. [0146] recites the use of generic functions of “applying” data into the generically recited hybrid MLM. [0146] recites the use of the generic hybrid MLM to carry out the abstract idea. 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.
Regarding claim 14:
A computer-implemented method, the method comprising (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)):
receiving, by one or more computer processors, a first plurality of medical data associated with a first plurality of individuals;
training, by the one or more computer processors, a machine learning model (MLM) based on the received first plurality of medical data, such that the trained MLM is able 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)) process a second plurality of medical data associated with a second plurality of individuals and output a fall prediction score associated with each of the second plurality of individuals, wherein the fall prediction score is an estimation of a likelihood, respectively, that each of the second plurality of individuals will suffer a fall, and wherein at least a portion of the first plurality of data and a portion of the second plurality of data are based on one or more pre-determined fillable forms associated with a value range;
generating, by the one or more computer processors and for each of the second plurality of individuals, an adjustment factor for the fall prediction score based on previous fall history associated with the respective individual;
adjusting, by the one or more computer processors, each of the fall prediction scores based on the respective adjustment factor;
based on the adjusted fall prediction score for one or more individuals of the second plurality of individuals, initiating, by the one or more computer processors, prophylactic intervention for one or more individuals, wherein the prophylactic intervention includes at least one prophylactic media component, wherein initiating the prophylactic intervention comprises:
for each of the one or more individuals, selecting the prophylactic media component based on the individual; and
for each of the one or more individuals, outputting the prophylactic media component;
in response to determining that the adjusted fall prediction score of one or more individuals of the second plurality of individuals satisfies a risk criteria, computing, by the one or more computer processors, a second fall prediction score for the one or more individuals using the trained MLM (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)); and
in response to determining that the adjusted fall prediction score of a second one or more individuals of the second plurality of individuals does not satisfy the risk criteria, refrain from computing a second fall prediction score for the second one or more individuals using the trained MLM.
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 limitation of use of processors for receiving a first plurality of medical data associated with individuals that is used for training a MLM to be used to perform steps, 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)). [0103] of Applicant’s Specification recites the use of a generic processor for the training of a MLM. [0104, 0184] recites the generic training of the MLM and use of the model and where the first plurality of data is derived from. 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.
Regarding claim 16:
A non-transitory computer-readable storage medium storing computer-readable program code executable by a processor 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)):
apply a machine learning model (MLM) to a plurality of data associated with at least one individual (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f));
compute an initial fall risk score for the at least one individual based on the application of the MLM (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f));
generate an adjustment factor for the initial fall risk score based on a previous fall history associated with the individual;
adjust the initial fall risk score based on the adjustment factor;
based on the adjusted fall risk score for the individual, initiate prophylactic intervention for the individual, wherein the prophylactic intervention includes at least one prophylactic media component, the processor configured to:
select the prophylactic media component based on the individual; and
output the prophylactic media component to the individual;
in response to determining that the adjusted fall risk score of the individual satisfies a risk criteria, compute a second fall risk score for the individual using the MLM; and
in response to determining that the adjusted fall risk score of a second individual does not satisfy the risk criteria, refraining from computing a second fall risk score for the individual using the MLM.
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 limitation of a non-transitory computer-readable storage medium storing computer-readable program code executable by a processor, applying the MLM to data and the application of the MLM, 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)). [0057-0058] of Applicant’s Specification recites the overall generic computing with the medium and processor. [0091] recites the use of generic functions of “applying” data into the generically recited MLM. [0091] recites the use of the generic MLM to carry out the abstract idea. 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.
Regarding claim 24:
An apparatus, the apparatus comprising:
a memory to store instructions; and
processing circuitry, coupled with the memory, operable to execute the instructions, that when executed, cause the processing circuitry 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)):
receive a plurality of medical data associated with a plurality of individuals (merely data gathering steps as noted below, see MPEP 2106.05(g) and Versata Dev. Group, Inc. v. SAP Am., Inc.);
compute a fall prediction score associated with each of the plurality of individuals based on the received plurality of medical data; and
generate, for each of the plurality of individuals, an adjustment factor for the fall prediction score based on previous fall history associated the individual;
adjust each fall prediction score based on the respective adjustment factor;
based on each of the adjusted fall prediction score and a medical profile of each of the plurality of individuals, initiate prophylactic intervention for each of the plurality of individuals, wherein the prophylactic intervention includes at least one prophylactic media component, the processing circuity configured to:
select the prophylactic media component based on the individual; and
output the prophylactic media component to the individual;
in response to determining that the adjusted fall prediction score of an individual of the plurality of individuals satisfies a risk criteria, compute a second fall prediction score for the individual; and
in response to determining that the adjusted fall prediction score of a second individual of the plurality of individuals does not satisfy the risk criteria, refrain from computing a second fall prediction score for the individual.
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 limitation of the overall apparatus with the memory and processing circuitry to perform steps, 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)). [0057-0058] of the Applicant’s Specification recites the overall generic apparatus and computing with the processor and memory. 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.
Regarding the additional limitation of receive a plurality of medical data associated with a plurality of individuals, this is merely pre-solution activity. The Examiner submits that this additional limitation merely adds insignificant extra-solution activity of collecting data to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)). [0191] of Applicant’s Specification recites the step of receiving the medical data. “Receiving” the medical data to be used to perform steps of the abstract idea is an action for data gathering, and thus recites insignificant pre-solution activity.
Regarding claim 27:
A method comprising (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)):
receiving a first plurality of data associated with a plurality of individuals;
applying a hybrid machine learning algorithm to the first plurality of data (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f));
generating an initial score based on an application of the hybrid machine learning algorithm to the first plurality of data (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f));
generating an adjustment factor for the initial score based on previous fall history associated with the respective individual;
generating a second score by applying the adjustment factor to the initial score, wherein the adjustment factor is based on a fall history data of the plurality of individuals;
receiving a second plurality of data associated with the one or more individuals;
applying a second algorithm to the second plurality of data associated with the plurality of individuals (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f));
generating a third score based on application of the second algorithm (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f));
generating a final score based on the second score and the third score, where the final score is a prediction as to whether or not the plurality of individuals will sustain a fall, and
based on the final score for the plurality of individuals, initiating, prophylactic intervention for the plurality of individuals, wherein the prophylactic intervention is tailored to one or more profiles of the plurality of individuals, wherein the prophylactic intervention includes at least one prophylactic media component, and wherein initiating the prophylactic intervention comprises:
selecting the prophylactic media component based on the individual; and
outputting the prophylactic media component;
in response to determining that the final score of an individual of the plurality of individuals satisfies a risk criteria, computing a second fall prediction score for the individual using the hybrid machine learning algorithm; and
in response to determining that the final score of a second individual of the plurality of individuals does not satisfy the risk criteria, refraining from computing a second fall prediction score for the second individual using the hybrid machine learning algorithm.
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 limitation of receiving first plurality of data being applied to a hybrid ML algorithm, use of that hybrid LM algorithm, receiving a second plurality of data being applied to another algorithm, and use of that another algorithm, 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)). [0191] of Applicant’s Specification recites the use of the data being applied generically to the algorithms. [0184] recites generically “applying” the algorithms to perform steps of the abstract idea. 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 output medial components for individuals, 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, 4, 15, 17 recite additional elements of the machine learning as having a static and/or dynamic component and how the static output is inputted into the dynamic component, 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 as there is no specific configuration of the machine learning models. Claims 3, 5-7, 12, 13 recites additional elements further describing the data that is being inputted in the MLM models, 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 as there is no specific configuration of how the data is used in the components. Claim 9 recites additional element of using a component for generating the adjust factor, 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. Claim 16 recites additional elements of the use of a MLM to data, 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 as there is no specific configuration of the machine learning model. Claim 19 recites additional elements further describing the dynamic model including a neural network, 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 as there is no specific configuration of the neural network. Claim 20 recites further additional elements of the output of the static model is the input for the NN, 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 as there is no specific configuration of how the outputs are used as inputs for the NN. Claim 22 recites additional element of static model that is distinct from the MLLM, 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 as there is no specific configuration of the MLM. Claim 23 recites the additional element of the fall history being used for another static model, 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 as there is no specific configuration of the static model. Claim 25 recites additional element of further describing the received data, where this is merely describing the insignificant pre-solution activity. Claim 29 recites additional element with further detail of the data that is being applied to the hybrid model, 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 as there is no specific configuration of the machine learning models and input of data. Claim 30 recites additional elements describing the hybrid model as including static model and dynamic models and the algorithm including a static model, 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 as there is no specific configuration of the machine learning models
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 claim 1:
Regarding the additional limitation of the overall apparatus with the memory and processing circuitry, applying a hybrid MLM to a plurality of data, and the application of the hybrid MLM, 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)). [0057-0058] of the Applicant’s Specification recites the use of generic computing components for the apparatus system. [0146] recites the use of generic functions of “applying” data into the generically recited hybrid MLM. [0146] recites the use of the generic hybrid MLM to carry out the abstract idea. 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.
Regarding claim 14:
Regarding the additional limitation of use of processors for receiving a first plurality of medical data associated with individuals that is used for training a MLM to be used to perform steps, 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)). [0103] of Applicant’s Specification recites the use of a generic processor for the training of a MLM. [0104, 0184] recites the generic training of the MLM and use of the model and where the first plurality of data is derived from. 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.
Regarding claim 16:
Regarding the additional limitation of a non-transitory computer-readable storage medium storing computer-readable program code executable by a processor, applying the MLM to data and the application of the MLM, 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)). [0057-0058] of Applicant’s Specification recites the overall generic computing with the medium and processor. [0091] recites the use of generic functions of “applying” data into the generically recited MLM. [0091] recites the use of the generic MLM to carry out the abstract idea. 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.
Regarding claim 24:
Regarding the additional limitation of the overall apparatus with the memory and processing circuitry to perform steps, 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)). [0057-0058] of the Applicant’s Specification recites the overall generic apparatus and computing with the processor and memory. 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
Regarding the additional limitation of receive a plurality of medical data associated with a plurality of individuals, this is merely pre-solution activity. The Examiner submits that this additional limitation merely adds insignificant extra-solution activity of collecting data to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g) and MPEP § 2106.05(d)(II), specifically “storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93”). [0191] of Applicant’s Specification recites the step of receiving the medical data. “Receiving” the medical data to be used to perform steps of the abstract idea is an action for data gathering, and thus recites insignificant pre-solution activity. The receiving of information can come from data sources as described in [0191], where the data is retrieved from memory for the insignificant pre-solution activities and recites well understood, routine, and conventional activities and does not recite significantly more than the judicial exception.
Regarding claim 27:
Regarding the additional limitation of receiving first plurality of data being applied to a hybrid ML algorithm, use of that hybrid LM algorithm, receiving a second plurality of data being applied to another algorithm, and use of that another algorithm, 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)). [0191] of Applicant’s Specification recites the use of the data being applied generically to the algorithms. [0184] recites generically “applying” the algorithms to perform steps of the abstract idea. 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 1-7, 9, 11-25, 27-30 are rejected under 35 USC 101 as being directed to non-statutory subject matter.
Conclusion
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/CONSTANTINE SIOZOPOULOS/
Examiner
Art Unit 3686
Prosecution Insights