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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 February 13, 2026 has been entered.
Status of Claims
This office action for the 18/134189 application is in response to the communications filed February 13, 2026.
Claim 1 was amended February 13, 2026.
Claims 1-20 are currently pending and considered below.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a risk based patient monitoring engine” and “a performance assessor” in claim 11 and “a notification module” in claim 13.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
As per claim 1,
Step 1: The claim recites subject matter within a statutory category as a process.
Step 2A is a two-prong inquiry, in which Prong 1 determines whether a claim recites a judicial exception. Prong 2 determines if the additional limitations of the claim integrates the recited judicial exception into a practical application. If the additional elements of the claim fail to integrate the judicial exception into a practical application, claim is directed to the recited judicial exception, see MPEP 2106.04(II)(A).
Step 2A Prong 1: The claim contains subject matter that recites an abstract idea, with the steps of a method of calculating an internal state variable, the method comprising: receiving patient data and a model of an internal state variable; determining the internal state variable using the patient data and the model of the internal state variable; receiving gold standard data corresponding to the internal state variable; performing a statistical performance assessment of the model of the internal state variable based on a comparison of model outputs and the gold standard data; determining whether a performance of the model of the internal state variable is above a prescribed performance threshold; and determining a source of inconsistent data negatively impacting the performance of the model of the internal state variable by identifying one or more data sources whose outputs deviate from the gold standard beyond a statistical threshold. These steps, as drafted, under the broadest reasonable interpretation recite:
certain methods of organizing human activity (e.g., fundamental economic principles or practices including: hedging; insurance; mitigating risk; etc., commercial or legal interactions including: agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations; etc., managing personal behavior or relationships or interactions between people including: social activities; teaching; following rules or instructions; etc.) but for recitation of generic computer components. That is nothing in the claim element precludes the step from being directed to certain methods of organizing human activity. For example the identified abstract idea, law of nature, or natural phenomenon identified above, in the context of this claim, encompasses a certain method of organizing human activity, namely managing personal behavior or relationships or interactions between people. This is because each of the limitations of the abstract idea recites a list of rules or instructions that a human person can follow in the course of their personal behavior. If a claim limitation, under its broadest reasonable interpretation, covers at least the recited methods of organizing human activity above, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. See MPEP 2106.04(a).
Step 2A Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
add insignificant extra-solution activity to the abstract idea, see MPEP 2106.05(g), such as:
“stored in a database” which corresponds to mere data gathering and/or output.
Accordingly, this claim is directed to an abstract idea.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, identified as insignificant extra-solution activity to the abstract idea, amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields such as:
computer functions that have been identified by the courts as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity, see MPEP 2106.05(d)(II), such as:
“stored in a database” which corresponds to storing and retrieving information in memory.
As per claim 2,
Claim 2 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 2 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“generating a list of potential associated error conditions causing the inconsistent data from the source” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 3,
Claim 3 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 3 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“taking corrective action to reduce an error condition causing the inconsistent data from the source” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 4,
Claim 4 depends from claim 3 and inherits all the limitations of the claim from which it depends. Claim 4 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“repeating the steps of claim 1 after taking the corrective action” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 5,
Claim 5 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 5 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the model of the internal state variable is based on retrospective data” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 6,
Claim 6 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 6 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the internal state variable is a particular health event” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 7,
Claim 7 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 7 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the internal state variable is a particular patient variable” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 8,
Claim 8 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 8 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the internal state variable is a hidden internal state variable” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 9,
Claim 9 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 9 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the source of error is a patient characteristic” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 10,
Claim 10 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 10 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the source of error is a patient characteristic when used with a particular sensor” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 11,
Step 1: The claim recites subject matter within a statutory category as a machine.
Step 2A is a two-prong inquiry, in which Prong 1 determines whether a claim recites a judicial exception. Prong 2 determines if the additional limitations of the claim integrates the recited judicial exception into a practical application. If the additional elements of the claim fail to integrate the judicial exception into a practical application, claim is directed to the recited judicial exception, see MPEP 2106.04(II)(A).
Step 2A Prong 1: The claim contains subject matter that recites an abstract idea, with the steps of determining an internal state variable, calculate an internal state variable using patient data and a model of the internal state variable for a patient; having gold standard data corresponding to the internal state variable; perform a statistical performance assessment of the model of the internal state variable; determine whether a performance of the model of the internal state variable is above a prescribed threshold; and determine a source of inconsistent data negatively impacting the performance of the model of the internal state variable. These steps, as drafted, under the broadest reasonable interpretation recite:
certain methods of organizing human activity (e.g., fundamental economic principles or practices including: hedging; insurance; mitigating risk; etc., commercial or legal interactions including: agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations; etc., managing personal behavior or relationships or interactions between people including: social activities; teaching; following rules or instructions; etc.) but for recitation of generic computer components. That is, other than reciting steps as performed by the generic computer components, nothing in the claim element precludes the step from being directed to certain methods of organizing human activity. For example, but for the additional element(s) of “A system for”, “the system comprising”, “a risk based patient monitoring engine configured to”, “a retrospective database” and “a performance assessor configured to”, the identified abstract idea, law of nature, or natural phenomenon identified above, in the context of this claim, encompasses a certain method of organizing human activity, namely managing personal behavior or relationships or interactions between people. This is because each of the limitations of the abstract idea recites a list of rules or instructions that a human person can follow in the course of their personal behavior. If a claim limitation, under its broadest reasonable interpretation, covers at least the recited methods of organizing human activity above, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. See MPEP 2106.04(a).
Step 2A Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception, see MPEP 2106.05(f), such as:
“A system for”, “the system comprising”, “a risk based patient monitoring engine configured to”, “a retrospective database” and “a performance assessor configured to” which corresponds to merely using a computer as a tool to perform an abstract idea. Page 17, Lines 20-27 – Page 18, Lines 1-4 of the as-filed specification describe that the hardware that implements the steps of the abstract idea amounts to little more than a generic computer. Implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
Accordingly, this claim is directed to an abstract idea.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 12,
Claim 12 depends from claim 11 and inherits all the limitations of the claim from which it depends. Claim 12 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“an error source identifier configured to generate a list of potential associated error conditions causing the inconsistent data from the source” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 13,
Claim 13 depends from claim 11 and inherits all the limitations of the claim from which it depends. Claim 13 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“report that the performance of the model is below the prescribed threshold…report corrective actions that reduce an error condition causing the inconsistent data from the source” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
“a notification module configured to” and “the notification module further configured to” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to merely using a computer as a tool to perform an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 14,
Claim 14 depends from claim 11 and inherits all the limitations of the claim from which it depends. Claim 14 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the internal state variable is a particular health event” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 15,
Claim 15 depends from claim 11 and inherits all the limitations of the claim from which it depends. Claim 15 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the internal state variable is a particular patient biomarker” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 16,
Claim 16 depends from claim 11 and inherits all the limitations of the claim from which it depends. Claim 16 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the internal state variable is a hidden internal state variable” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 17,
Claim 17 depends from claim 13 and inherits all the limitations of the claim from which it depends. Claim 17 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“further comprising a display that displays the notification” introduces additional elements that is insufficient to provide a practical application or significantly more:
Step 2A Prong 2: In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
add insignificant extra-solution activity to the abstract idea, see MPEP 2106.05(g), such as:
“further comprising a display that displays the notification” which corresponds to mere data gathering and/or output.
Step 2B: As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, identified as insignificant extra-solution activity to the abstract idea, amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields such as:
computer functions that have been identified by the courts as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity, see MPEP 2106.05(d)(II), such as:
“further comprising a display that displays the notification” which corresponds to receiving or transmitting data over a network.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 18,
Step 1: The claim recites subject matter within a statutory category as a manufacture.
Step 2A is a two-prong inquiry, in which Prong 1 determines whether a claim recites a judicial exception. Prong 2 determines if the additional limitations of the claim integrates the recited judicial exception into a practical application. If the additional elements of the claim fail to integrate the judicial exception into a practical application, claim is directed to the recited judicial exception, see MPEP 2106.04(II)(A).
Step 2A Prong 1: The claim contains subject matter that recites an abstract idea, with the steps of determining an internal state variable, receiving patient data and a model of an internal state variable; calculating the internal state variable using the patient data and the model of the internal state variable; receiving gold standard data corresponding to the internal state variable; performing a statistical performance assessment of the model of the internal state variable; determining whether a performance of the model of the internal state variable is above a prescribed threshold; and determining a source of inconsistent data negatively impacting the performance of the model of the internal state variable. These steps, as drafted, under the broadest reasonable interpretation recite:
certain methods of organizing human activity (e.g., fundamental economic principles or practices including: hedging; insurance; mitigating risk; etc., commercial or legal interactions including: agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations; etc., managing personal behavior or relationships or interactions between people including: social activities; teaching; following rules or instructions; etc.) but for recitation of generic computer components. That is, other than reciting steps as performed by the generic computer components, nothing in the claim element precludes the step from being directed to certain methods of organizing human activity. For example, but for the additional element(s) of “A computer program product for use on a computer system for”, “the computer program product comprising a tangible, non-transient computer usable medium having computer readable program code thereon, the computer readable program code comprising” and “program code for”, the identified abstract idea, law of nature, or natural phenomenon identified above, in the context of this claim, encompasses a certain method of organizing human activity, namely managing personal behavior or relationships or interactions between people. This is because each of the limitations of the abstract idea recites a list of rules or instructions that a human person can follow in the course of their personal behavior. If a claim limitation, under its broadest reasonable interpretation, covers at least the recited methods of organizing human activity above, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. See MPEP 2106.04(a).
Step 2A Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception, see MPEP 2106.05(f), such as:
“A computer program product for use on a computer system for”, “the computer program product comprising a tangible, non-transient computer usable medium having computer readable program code thereon, the computer readable program code comprising” and “program code for” which corresponds to merely using a computer as a tool to perform an abstract idea. Page 17, Lines 20-27 – Page 18, Lines 1-4 of the as-filed specification describe that the hardware that implements the steps of the abstract idea amounts to little more than a generic computer. Implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
Accordingly, this claim is directed to an abstract idea.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 19,
Claim 19 depends from claim 18 and inherits all the limitations of the claim from which it depends. Claim 19 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“generating a list of potential associated error conditions causing the inconsistent data from the source” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
“program code for” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to merely using a computer as a tool to perform an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 20,
Claim 20 depends from claim 18 and inherits all the limitations of the claim from which it depends. Claim 20 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“taking corrective action to reduce an error condition causing the inconsistent data from the source” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
“program code for” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to merely using a computer as a tool to perform an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Gupta et al. (US 2019/0192768; herein referred to as Gupta).
As per claim 1,
Gupta discloses method of determining an internal state variable:
(Paragraph [0218] of Gupta. The teaching describes data can comprise both internal OScal data and external OScal data (and/or manually entered external data).)
Gupta further discloses receiving patient data and a model of an internal state variable:
(Paragraph [0238] of Gupta. The teaching describes other data (e.g., internal OScal data, external OScal data, or other user input data) are received and/or collected in addition to the BAcal data during training mode operation, variables or parameters can additionally include temperature, acceleration, orientation, pressure, pulse rate, one or more other non-target blood analyte concentrations, intake of medication, intake of food, designated resting period of the user, designated active period the user, and/or other determinable parameters that can be extracted from the other sensor data and/or the user input data such as user state or context data.)
Gupta further discloses calculating the internal state variable using the patient data and the model of the internal state variable:
(Paragraph [0061] of Gupta. The teaching describes that operation in the training mode enables generation of one or more user-specific operational models (via e.g., "machine learning"), which can be used to at least partially correct for systemic or other errors during analyte detection and medicant delivery in subsequent modes.)
Gupta further discloses receiving gold standard data corresponding to the internal state variable:
(Paragraph [0282] of Gupta. The teaching describes an approach ("evaluation mode"), the user-specific (training mode derived) sensor operational model can be run in parallel with the predesignated population-based model on the same in vivo user data to generate corrected BG level outputs for each; these can then be compared to e.g., an external calibration data source or other "gold standard" for the actual BG level in that user at that time, to assess model performance.)
Gupta further discloses performing a statistical performance assessment of the model of the internal state variable based on a comparison of model outputs and the gold standard data stored in a database:
(Paragraphs [0281], [0282] and [0365] of Gupta. The teaching describes that if the user specific sensor models developed for numerous individual users show significant statistical divergence or variation from the relevant population-based model(s) selected for that user, then the strength of the statistical correlation or confidence in the population-based models is necessarily low, and their structure (e.g., in terms of errors modeled) and/or selection criteria may need further refinement. In a simple example, assessments of the divergence of the error corrections or corrected BG values produced by the user-specific sensor model could be utilized to de-select a population-based sensor model for a specific user. The teaching describes an approach ("evaluation mode"), the user-specific (training mode derived) sensor operational model can be run in parallel with the predesignated population-based model on the same in vivo user data to generate corrected BG level outputs for each; these can then be compared to e.g., an external calibration data source or other "gold standard" for the actual BG level in that user at that time, to assess model performance. It will be appreciated from the foregoing that various data storage and/or representation schemes can be utilized consistent with the various aspects of the present disclosure, depending on the particular application. For instance, data intended for human cognizance (e.g., a graphical display of response over time) may be represented as a graph, such as that of FIGS. 14A-14B. Tabular data such as that of FIG. 14C may be useful for e.g., data export, such as to a user-maintained database or spreadsheet program or the like.)
Gupta further discloses determining whether a performance of the model of the internal state variable is above a prescribed performance threshold:
(Paragraph [0047] of Gupta. The teaching describes in another implementation, the operation of the first processing apparatus in the initial pump training mode includes a determination that a training data collection threshold has been met; and in response to the determination, termination of the pump training mode operation. For example, the training data collection threshold may comprise a pre-determined number of data points, and/or a pre-determined duration of time.)
Gupta further discloses determining a source of inconsistent data negatively impacting the performance of the model of the internal state variable by identifying one or more data sources whose outputs deviate from the gold standard data beyond the statistical threshold:
(Paragraphs [0259] and [0361] of Gupta. The teaching describes that for example, one or more of the detector pairs of the sensor 200 may become less sensitive or more sensitive or fail over time, and/or the host's physiological responses (including FBR or other such factors) may vary as a function of time. Ancillary sensors on the sensor 200 (e.g., pressure, temperature, etc.) may also fail or their response characteristics may change, thereby necessitating their re-evaluation or removal from the algorithmic modeling process. However, certain embodiments of the disclosure may implement the "machine learning" aspects indigenously on the implanted pump apparatus itself, thereby effectively obviating the need for communication with the corresponding receiver/processor apparatus, at least for functions relating to systemic or other error modeling and correction)
(Paragraphs [0281], [0282] and [0365] of Gupta. The teaching describes that if the user specific sensor models developed for numerous individual users show significant statistical divergence or variation from the relevant population-based model(s) selected for that user, then the strength of the statistical correlation or confidence in the population-based models is necessarily low, and their structure (e.g., in terms of errors modeled) and/or selection criteria may need further refinement. In a simple example, assessments of the divergence of the error corrections or corrected BG values produced by the user-specific sensor model could be utilized to de-select a population-based sensor model for a specific user. The teaching describes an approach ("evaluation mode"), the user-specific (training mode derived) sensor operational model can be run in parallel with the predesignated population-based model on the same in vivo user data to generate corrected BG level outputs for each; these can then be compared to e.g., an external calibration data source or other "gold standard" for the actual BG level in that user at that time, to assess model performance. It will be appreciated from the foregoing that various data storage and/or representation schemes can be utilized consistent with the various aspects of the present disclosure, depending on the particular application. For instance, data intended for human cognizance (e.g., a graphical display of response over time) may be represented as a graph, such as that of FIGS. 14A-14B. Tabular data such as that of FIG. 14C may be useful for e.g., data export, such as to a user-maintained database or spreadsheet program or the like.)
As per claim 2,
Gupta discloses the limitations of claim 1.
Gupta further discloses generating a list of potential associated error conditions causing the inconsistent data from the source:
(Paragraphs [0060] and [0438] of Gupta. The teaching describes that based at least in part on the operating of the sensor apparatus in the initial training mode, generating a sensor error correction operational model. Per step 1364, test data sets are generated and compared for, inter alia, evaluation purposes. Per step 1365, the evaluation may include identification of one or more parameters having a highest (or statistically significant) correlation to BAp _ error. Thus, the final list of one or more parameters is selected based on a predictor importance/relevance to BAp _ error criterion, such as for example based on a pre-defined threshold of predictor importance. Alternatively, a simple criterion to select a prescribed number (e.g., top 'n') of predictors can be employed. It will be appreciated that the parameter identification process may be conducted algorithmically (e.g., by an application computer program or other software) based on provided data sets, heuristically by a human, or combinations thereof. Moreover, if the relevant model parameters are known a priori, such model parameter identification methodology may be completely obviated.)
As per claim 3,
Gupta discloses the limitations of claim 1.
Gupta further discloses taking corrective action to reduce an error condition causing the inconsistent data from the source:
(Paragraph [0202] of Gupta. The teaching describes that it is also appreciated that user notification and/or input may be obviated in favor of direct communication between the sensor system and the source of BAf, such as where the sensor system generates and transmits a datagram to an API (application programming interface) of the reference data source, requesting the reference data. Upon receiving the datagram, the reference data source generates and transmits a responsive datagram containing the requested reference data and any other appropriate data such as temporal reference, source ID, CRC or other error correction data, etc.")
As per claim 4,
Gupta discloses the limitations of claim 3.
Gupta further discloses repeating the steps of claim 1 after taking the corrective action:
(Paragraphs [0130] and [0271] of Gupta. The teaching describes that the foregoing pump training mode can be repeated (as necessary, on a prescribed schedule, or according to yet other bases), even as disease presentation or other physiological or lifestyle characteristics of the user change over that same time. If a selection for sensor re-training is received, a repeated operation of the sensor system in the training mode is initiated (step 554).).
As per claim 5,
Gupta discloses the limitations of claim 1.
Gupta further discloses wherein the model of the internal state variable is based on retrospective data:
(Paragraph [0260] of Gupta. The teaching describes that new and previously un-modeled (within that individual) physiological or non-physiological error sources may arise over time, or other previously modeled sources (i.e., accounted for in the model developed after initial training) may wane over time. New algorithms may also be developed, and it may be desired to retro-fit them into an already implanted device.)
As per claim 6,
Gupta discloses the limitations of claim 1.
Gupta further discloses wherein the internal state variable is a particular health event:
(Paragraph [0437] of Gupta. The teaching describes that in another variant where other data (e.g., internal OScal data, external OScal data, or other user input data) are received and/or collected in addition to the BAref data or corrected BAcal data and pump data during pump training mode operation, variables or parameters can additionally include temperature, acceleration, altitude, orientation, pressure, pulse rate, one or more other non-target blood analyte concentrations, intake of medication, intake of food, designated resting period of the user, designated active period the user, and/or other determinable parameters that can be extracted from the other sensor data and/or the user input data such as user state or context data.)
As per claim 7,
Gupta discloses the limitations of claim 1.
Gupta further discloses wherein the internal state variable is a particular patient variable:
(Paragraph [0127] of Gupta. The teaching describes in another exemplary embodiment, the foregoing implanted sensor system and sensor operational model can be utilized in generation of a patient-specific medicant dosing model. In some examples, the patient-specific medicant dosing model is applicable to delivery of medicants without use of a pump (i.e., manual medicant delivery via e.g., oral, ocular, subcutaneous, cutaneous, nasal or other mechanisms) and/or manual or semi-manual medicant pump mechanisms. In such examples, unmodeled variables related to user-specific medicant absorption or affect (based on e.g., physiology, lifestyle, and/or disease presentation of a specific individual) can be modeled via operation of the implanted sensor system and monitoring of analyte levels before, during and after medicant delivery to the user. Based on the generated patient-specific medicant dosing model, a processor associated with the sensor system (or a separate processor which receives sensor data) may communicate to the user an appropriate dosage or time for delivery of the medicant via manual administration or semi-manual administration.)
As per claim 8,
Gupta discloses the limitations of claim 1.
Gupta further discloses wherein the internal state variable is a hidden internal state variable:
(Paragraph [0241] of Gupta. The teaching describes that supervised learning solutions may be useful to quickly adapt the known input BAcal to the expected output BAref. Unsupervised learning may be used to find hidden correlations between the various sensor inputs; for example, unsupervised learning may be able to infer complex interrelationships between e.g., heart rate, oxygenation, and blood glucose which would be otherwise too complex to generically model, or the bases for which are unknown. Reinforcement techniques may be used by doctors, or other trained personnel to fine tune and or further tailor measurements. Still other applications of the foregoing will be readily appreciated by those of ordinary skill in the related arts.)
As per claim 9,
Gupta discloses the limitations of claim 1.
Gupta further discloses wherein the source of error is a patient characteristic:
(Paragraph [0280] of Gupta. The teaching describes that after such model development, characteristics of other users (i.e., non-test subject users, such as new patients) can be identified, and a specific population-based operational model can be implemented based on the identified characteristics within the implanted sensor system, without the need for operation of the implanted sensor in the training mode (or rather a confirmatory training process). For example, it may be found that persons of a certain gender, age, race or ethnicity, physiologic profile-which may include the presence or absence of certain genetic markers, blood constituents (e.g., proteins, antibodies, antigens, etc.), BMI, or other parameter, exhibit certain types of systematic or other error sources relative to the BG measurement, with a high degree of statistical confidence. This correlation can be used in picking an operational model for a user falling within such class prior to or in place of user-specific operational models.)
As per claim 10,
Gupta discloses the limitations of claim 1.
Gupta further discloses wherein the source of error is a patient characteristic when used with a particular sensor:
(Paragraph [0388] of Gupta. The teaching describes that is appreciated that while the generalized methodologies set forth above with respect to FIGS.13-138 utilize implant of the sensor 200 and implant/partial implant of the pump (as applicable) as preconditions for training of the machine learning algorithms (so as to ostensibly provide the best training environment for that particular sensor, pump, and patient combination), there may be instances where such implantation is not required for sensor and/or pump training. For example, the present disclosure contemplates conditions where the sensor and pump system (or the sensor and pump individually) may be "pre-trained" prior to implantation, such as based on data previously acquired for that _same individual (e.g., as part of a prior training session, prior sensor implantation, prior pump implantation, etc.), or even data derived from one or more similarly situated individuals (e.g., family member, similar physiologic characteristics, similar disease expression, etc.). Moreover, training based on pump "class" data (as opposed to that specifically derived from the pump to be implanted) can be used as the basis of modeling, at least for initial training estimates.)
As per claim 11,
Gupta discloses system for determining an internal state variable, the system comprising:
(Paragraph [0218] of Gupta. The teaching describes that in another implementation, the other data can comprise both internal OScal data and external OScal data (and/or manually entered external data).)
Gupta further discloses a risk based patient monitoring engine configured to calculate an internal state variable using patient data and a model of the internal state variable for a patient:
(Paragraph [0238] and [0303] of Gupta. The teaching describes that in another variant where other data (e.g., internal OScal data, external OScal data, or other user input data) are received and/or collected in addition to the BAcal data during training mode operation, variables or parameters can additionally include temperature, acceleration, orientation, pressure, pulse rate, one or more other non-target blood analyte concentrations, intake of medication, intake of food, designated resting period of the user, designated active period the user, and/or other determinable parameters that can be extracted from the other sensor data and/or the user input data such as user state or context data. Referring now to FIG. 11A, one embodiment of a system architecture for, inter alia, monitoring blood analyte levels and automatically administering medicant within a user, useful with the machine learning-based methods and apparatus of present disclosure is described in detail.)
Gupta further discloses a retrospective database having gold standard data corresponding to the internal state variable:
(Paragraph [0061] and [0282] of Gupta. The teaching describes that operation in the training mode enables generation of one or more user-specific operational models (via e.g., "machine learning"), which can be used to at least partially correct for systemic or other errors during analyte detection and medicant delivery in subsequent modes. Hence, in one approach ("evaluation mode"), the user-specific (training mode derived) sensor operational model can be run in parallel with the pre-designated population-based model on the same in vivo user data to generate corrected BG level outputs for each; these can then be compared to e.g., an external calibration data source or other "gold standard" for the actual BG level in that user at that time, to assess model performance.)
Gupta further discloses a performance assessor configured to: perform a statistical performance assessment of the model of the internal state variable:
(Para [0281] of Gupta. The teaching describes that if the user-specific sensor models developed for numerous individual users show significant statistical divergence or variation from the relevant population-based model(s) selected for that user, then the strength of the statistical correlation or confidence in the population-based models is necessarily low, and their structure (e.g., in terms of errors modeled) and/or selection criteria may need further refinement. In a simple example, assessments of the divergence of the error corrections or corrected BG values produced by the user-specific sensor model could be utilized to de-select a population-based sensor model for a specific user.)
Gupta further discloses determine whether a performance of the model of the internal state variable is above a prescribed threshold:
(Paragraph [0047] of Gupta. The teaching describes that in another implementation, the operation of the first processing apparatus in the initial pump training mode includes a determination that a training data collection threshold has been met; and in response to the determination, termination of the pump training mode operation. For example, the training data collection threshold may comprise a pre-determined number of data points, and/or a pre-determined duration of time.)
Gupta further discloses determine a source of inconsistent data negatively impacting the performance of the model of the internal state variable:
(Paragraphs [0259] and [0361] of Gupta. The teaching describes that for example, one or more of the detector pairs of the sensor 200 may become less sensitive or more sensitive or fail over time, and/or the host's physiological responses (including FBR or other such factors) may vary as a function of time. Ancillary sensors on the sensor 200 (e.g., pressure, temperature, etc.) may also fail or their response characteristics may change, thereby necessitating their reevaluation or removal from the algorithmic modeling process. Certain embodiments of the disclosure may implement the "machine learning" aspects indigenously on the implanted pump apparatus itself, thereby effectively obviating the need for communication with the corresponding receiver/processor apparatus, at least for functions relating to systemic or other error modeling and correction.)
As per claim 12,
Gupta discloses the limitations of claim 11.
Gupta further discloses an error source identifier configured to generate a list of potential associated error conditions causing the inconsistent data from the source:
(Paragraphs [0042], [0060] and [0438] of Gupta. The teaching describes that based at least in part on the operating of the sensor apparatus in the initial training mode, generating a sensor error correction operational model. Per step 1364, test data sets are generated and compared for, inter alia, evaluation purposes. Per step 1365, the evaluation may include identification of one or more parameters having a highest (or statistically significant) correlation to BAp _ error. Thus, the final list of one or more parameters is selected based on a predictor importance/relevance to BAp _ error criterion, such as for example based on a pre-defined threshold of predictor importance. Alternatively, a simple criterion to select a prescribed number (e.g., top 'n') of predictors can be employed. It will be appreciated that the parameter identification process may be conducted algorithmically (e.g., by an application computer program or other software) based on provided data sets, heuristically by a human, or combinations thereof. Moreover, if the relevant model parameters are known a priori, such model parameter identification methodology may be completely obviated.)
As per claim 13,
Gupta discloses the limitations of claim 11.
Gupta further discloses a notification module configured to report that the performance of the model is below the prescribed threshold, the notification module further configured to report corrective actions that reduce an error condition causing the inconsistent data from the source:
(Paragraphs [0199] and [0202] of Gupta. The teaching describes that after the sensor training mode is initialized, a notification is generated and transmitted to the GUI requesting input of external blood analyte reference data (BAref) per step 518. For example, during sensor training mode operation, the sensor system can periodically transmit notifications to a user to enter a manual blood analyte reading such as e.g., a blood glucose level determined via the aforementioned "fingersticking" method and/or laboratory-type analyzers (e.g., YSI analyzers). For example, notifications may be sent to the user hourly, every two hours, every three hours, daily, weekly, or according to other desired notification schedules. It is also appreciated that user notification and/or input may be obviated in favor of direct communication between the sensor system and the source of BAf, such as where the sensor system generates and transmits a datagram to an API (application programming interface) of the reference data source, requesting the reference data. Upon receiving the datagram, the reference data source generates and transmits a responsive datagram containing the requested reference data and any other appropriate data such as temporal reference, source ID, CRC or other error correction data, etc.)
As per claim 14,
Gupta discloses the limitations of claim 11.
Gupta further discloses wherein the internal state variable is a particular health event:
(Paragraph [0437] of Gupta. The teaching describes that in another variant where other data (e.g., internal OScal data, external OScal data, or other user input data) are received and/or collected in addition to the BAref data or corrected BAcal data and pump data during pump training mode operation, variables or parameters can additionally include temperature, acceleration, altitude, orientation, pressure, pulse rate, one or more other non-target blood analyte concentrations, intake of medication, intake of food, designated resting period of the user, designated active period the user, and/or other determinable parameters that can be extracted from the other sensor data and/or the user input data such as user state or context data.)
As per claim 15,
Gupta discloses the limitations of claim 11.
Gupta further discloses wherein the internal state variable is a particular patient biomarker:
(Paragraph [0280] of Gupta. The teaching describes that after such model development, characteristics of other users (i.e., non-test subject users, such as new patients) can be identified, and a specific population-based operational model can be implemented based on the identified characteristics within the implanted sensor system, without the need for operation of the implanted sensor in the training mode (or rather a confirmatory training process). For example, it may be found that persons of a certain gender, age, race or ethnicity, physiologic profile-which may include the presence or absence of certain genetic markers, blood constituents (e.g., proteins, antibodies, antigens, etc.), BMI, or other parameter, exhibit certain types of systematic or other error sources relative to the BG measurement, with a high degree of statistical confidence. This correlation can be used in picking an operational model for a user falling within such class prior to or in place of user-specific operational models ).
As per claim 16,
Gupta discloses the limitations of claim 11.
Gupta further discloses wherein the internal state variable is a hidden internal state variable:
(Paragraph [0241] of Gupta. The teaching describes that supervised learning solutions may be useful to quickly adapt the known input BAcal to the expected output BAref. Unsupervised learning may be used to find hidden correlations between the various sensor inputs; for example, unsupervised learning may be able to infer complex interrelationships between e.g., heart rate, oxygenation, and blood glucose which would be otherwise too complex to generically model, or tt:ie bases for which are unknown. Reinforcement techniques may be used by doctors, or other trained personnel to fine tune and or further tailor measurements. Still other applications of the foregoing will be readily appreciated by those of ordinary skill in the related arts.)
As per claim 17,
Gupta discloses the limitations of claim 13.
Gupta further discloses further comprising a display that displays the notification:
(Paragraph [0296] of Gupta. The teaching describes that the injection tool 956 further includes a digital user interface 964 for display of information (e.g., time and current dosage units) and notifications to the user.)
As per claim 18,
Gupta discloses a computer program product for use on a computer system for determining an internal state variable:
(Paragraph [0218] of Gupta. The teaching describes that in another implementation, the other data can comprise both internal OScal data and external OScal data (and/or manually entered external data).)
Gupta further discloses the computer program product comprising a tangible, non-transient computer usable medium having computer readable program code thereon, the computer readable program code comprising:
(Paragraph [0061] of Gupta. The teaching describes that in another aspect, a computer readable apparatus is disclosed. In one embodiment, the computer readable apparatus comprises a storage medium (e.g., magnetic, solid state, optical, or other storage medium) having at least one computer program disposed thereon and readable by a computerized apparatus.)
Gupta further discloses program code for receiving patient data and a model of an internal state variable:
(Paragraph [0238] of Gupta. The teaching describes that in another variant where other data (e.g., internal OScal data, external OScal data, or other user input data) are received and/or collected in addition to the BAcal data during training mode operation, variables or parameters can additionally include temperature, acceleration, orientation, pressure, pulse rate, one or more other non-target blood analyte concentrations, intake of medication, intake of food, designated resting period of the user, designated active period the user, and/or other determinable parameters that can be extracted from the other sensor data and/or the user input data such as user state or context data.)
Gupta further discloses program code for calculating the internal state variable using the patient data and the model of the internal state variable:
(Paragraph [0061] of Gupta. The teaching describes that operation in the training mode enables generation of one or more user-specific operational models (via e.g., "machine learning"), which can be used to at least partially correct for systemic or other errors during analyte detection and medicant delivery in subsequent modes.)
Gupta further discloses program code for receiving gold standard data corresponding to the internal state variable:
(Paragraph [0282] of Gupta. The teaching describes that in one approach ("evaluation mode"), the user-specific (training mode derived) sensor operational model can be run in parallel with the pre-designated population-based model on the same in vivo user data to generate corrected BG level outputs for each; these can then be compared to e.g., an external calibration data source or other "gold standard" for the actual BG level in that user at that time, to assess model performance.)
Gupta further discloses program code for performing a statistical performance assessment of the model of the internal state variable:
(Paragraph [0281] of Gupta. The teaching describes that if the user-specific sensor models developed for numerous individual users show significant statistical divergence or variation from the relevant population-based model(s) selected for that user, then the strength of the statistical correlation or confidence in the population-based models is necessarily low, and their structure (e.g., in terms of errors modeled) and/or selection criteria may need further refinement. In a simple example, assessments of the divergence of the error corrections or corrected BG values produced by the user-specific sensor model could be utilized to de-select a population-based sensor model for a specific user.)
Gupta further discloses program code for determining whether a performance of the model of the internal state variable is above a prescribed threshold:
(Paragraph [0047] of Gupta. The teaching describes that in another implementation, the operation of the first processing apparatus in the initial pump training mode includes a determination that a training data collection threshold has been met; and in response to the determination, termination of the pump training mode operation. For example, the training data collection threshold may comprise a pre-determined number of data points, and/or a pre-determined duration of time.)
Gupta further discloses program code for determining a source of inconsistent data negatively impacting the performance of the model of the internal state variable:
(Paragraphs [0259] and [0361] of Gupta. The teaching describes that for example, one or more of the detector pairs of the sensor 200 may become less sensitive or more sensitive or fail over time, and/or the host's physiological responses (including FBR or other such factors) may vary as a function of time. Ancillary sensors on the sensor 200 (e.g., pressure, temperature, etc.) may also fail or their response characteristics may change, thereby necessitating their re-evaluation or removal from the algorithmic modeling process. Certain embodiments of the disclosure may implement the "machine learning" aspects indigenously on the implanted pump apparatus itself, thereby effectively obviating the need for communication with the corresponding receiver/processor apparatus, at least for functions relating to systemic or other error modeling and correction.)
As per claim 19,
Gupta discloses the limitations of claim 18.
Gupta further discloses program code for generating a list of potential associated error conditions causing the inconsistent data from the source:
(Paragraphs [0042], [0060] and [0438] of Gupta. The teaching describes that based at least in part on the operating of the sensor apparatus in the initial training mode, generating a sensor error correction operational model. Per step 1364, test data sets are generated and compared for, inter alia, evaluation purposes. Per step 1365, the evaluation may include identification of one or more parameters having a highest (or statistically significant) correlation to BAp _ error. Thus, the final list of one or more parameters is selected based on a predictor importance/relevance to BAp _ error criterion, such as for example based on a pre-defined threshold of predictor importance. Alternatively, a simple criterion to select a prescribed number (e.g., top 'n') of predictors can be employed. It will be appreciated that the parameter identification process may be conducted algorithmically (e.g., by an application computer program or other software) based on provided data sets, heuristically by a human, or combinations thereof. Moreover, if the relevant model parameters are known a priori, such model parameter identification methodology may be completely obviated.)
As per claim 20,
Gupta discloses the limitations of claim 18.
Gupta further discloses program code for taking corrective action to reduce an error condition causing the inconsistent data from the source:
(Paragraph [0202] of Gupta. The teaching describes that it is also appreciated that user notification and/or input may be obviated in favor of direct communication between the sensor system and the source of BAf, such as where the sensor system generates and transmits a datagram to an API (application programming interface) of the reference data source, requesting the reference data. Upon receiving the datagram, the reference data source generates and transmits a responsive datagram containing the requested reference data and any other appropriate data such as temporal reference, source ID, CRC or other error correction data, etc.")
Response to Arguments
Applicant's arguments filed February 13, 2026 have been fully considered.
Applicant’s arguments pertaining to Claim Interpretation under 35 U.S.C. 112(f) are not persuasive:
The Applicant argues that the interpretation of “risk based patient monitoring engine”, “a performance assessor” and “notification module” under 35 U.S.C. 112(f) is in error. The pending claims do not recite “means” language and they convey sufficient structure.
The Examiner respectfully disagrees. The following is a list of non-structural generic placeholders that may invoke 35 U.S.C. 112(f): "mechanism for," "module for," "device for," "unit for," "component for," "element for," "member for," "apparatus for," "machine for," or "system for." Welker Bearing Co., v. PHD, Inc., 550 F.3d 1090, 1096, 89 USPQ2d 1289, 1293-94 (Fed. Cir. 2008) (See MPEP 2181 (I)(A)). Both “risk based patient monitoring engine” and “notification module” provide no structure in the claim because the MPEP clearly states that “module” is not a structure. As for “a performance assessor”, the Applicant argued that the structure was informed by the specification which is exactly how the Examiner stated that the term would be interpreted. These terms need to have structure claimed or they will be interpreted by the corresponding structure in the specification. Please see the relevant statements above. With regard to the pending claims not reciting “means” language, a claim need not specifically claim “means” for 112(f) interpretation to apply. The claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. The terms “risk based patient monitoring engine”, “notification module” and “a performance assessor” are not terms with inherent structure.
Applicant’s arguments pertaining to rejections made under 35 U.S.C. 101 are not persuasive:
The Applicant argues that claim 1 merely involves but does not recite a judicial exception. The alleged abstract idea is implicated only within the broader operation of a computer-implemented workflow that improves the functioning of a clinical modeling system.
The Examiner respectfully disagrees. The entirety of claim 1, save for “stored in a database”, is a recitation of “Certain Methods of Organizing Human Activity”. There is absolutely no basis to stand on when no other technology is being claimed. The steps of claim 1 are not even being performed within a computer environment. On what grounds can we conclude that technology is being improved when technology is not involved? The only mention of technology with the database merely identifies the source of data for the gold standard. This database is otherwise unused and remains unchanged. Accordingly, it is not possible for claim 1 to improve technology. If the aim is that these steps would be executed in a computer environment, this feature is not claimed. Even if this feature were claimed, it is merely outputting an abstract idea to apply it to a computer. It is manifestly apparent that the limitations of claim 1 recite an abstract idea.
The Applicant further argues that claim 1 integrates any judicial exception into a practical application. The claimed workflow represents a technical solution to a technical problem, improving the accuracy and robustness of computer-implemented inferential models.
The Examiner respectfully disagrees. Again, the steps of claim 1 are not even being performed within a computer environment. On what grounds can we conclude that technology is being improved when technology is not involved? The only mention of technology with the database merely identifies the source of data for the gold standard. This database is otherwise unused and remains unchanged. Accordingly, it is not possible for claim 1 to improve technology. However, let us assume that a computer were executing the steps of the abstract idea, there is no evidence that the claimed invention would improve accuracy and robustness. This is merely a bare assertion of improvement. The as-field specification appears silent with regard to what technological deficit is present and how the invention provides a technological solution for this problem.
The Applicant further argues that claim 1 improves technology in a technical field.
The Examiner respectfully disagrees. Similar to the above, claim 1 does not involve technology in any meaningful way. The only recitation of technology is the database which is used in a tangential manner, identifying a source of data. Accordingly, it is not possible for claim 1 to improve technology.
The Applicant further argues that the previous response from the Examiner with regard to the steps being mental processes is unfounded. The claimed process requires statistical computation over multi-dimensional, high-frequency physiological streams. The steps of claim 1 cannot be performed in the human mind in any practical sense.
The Examiner respectfully disagrees. Claim 1 does not require statistical computation over multi-dimensional, high-frequency physiological streams at all. The Applicant has argued for features that are not claimed. However, even if it were claimed, there is no indication that a human mind would be incapable of processing this data. Regardless, this capacity of the human mind detracts from the basis of rejection which characterizes these steps as “Certain Methods of Organizing Human Activity”. A claim need not specifically claim a human performing the steps to be “Certain Methods of Organizing Human Activity”. See BASCOM. If a human person is capable of performing the claimed steps, the relevant limitations recite “Certain Methods of Organizing Human Activity”.
The Applicant further argues that determining a source of inconsistent data improves the functioning of the model. Accordingly, this addresses a technical performance issue.
The Examiner respectfully disagrees. gain, the steps of claim 1 are not even being performed within a computer environment. On what grounds can we conclude that technology is being improved when technology is not involved? The only mention of technology with the database merely identifies the source of data for the gold standard. This database is otherwise unused and remains unchanged. Accordingly, it is not possible for claim 1 to improve technology. A “model” is not technology per se. It is merely an algorithm especially when divorced from a technical environment such as a computer. Algorithms such as these amount to nothing more than math, which is abstract.
Addressing the concern of whether this case is a close question of eligibility, the Examiner can confidently state that these claims are not on the fence for patent eligibility for the reasons indicated above. There is no doubt in the Examiner’s opinion that the pending claims are clearly ineligible.
Applicant’s arguments pertaining to rejections made under 35 U.S.C. 102 are not persuasive:
The Applicant argues that Gupta fails to disclose “receiving patient data and a model of an internal state variable” as recited by claim 1. The model in Gupta is generated/used as a model of sensor/pump error, not a model of a physiological state variable.
The Examiner respectfully disagrees. This is not a proper characterization of what Gupta teaches or what the word “model” means. The term “model” in this context is a mathematical characterization of an internal state variable. This term is incredibly broad especially considering that the function of the model is not specifically claimed. Gupta clearly and explicitly takes in internal state physiological data into a predictive model to determine an outcome. Accordingly, this model can reasonably be classified as an internal state variable model. The ability of this model to have other functions does not preclude this characterization. Application of Gupta here falls under the BRI of the claim language and the reference. The Applicant is trying to narrow the term “model” to cover features that are not claimed.
The Applicant further argues that Gupta fails to disclose “calculating the internal state variable using the patient data and the model of the internal state variable” as recited by claim 1. Gupta remains focused on the error and calibration of analyte values, not the calculation of a separate internal physiological state variable using the model of that state.
The Examiner respectfully disagrees. Gupta clearly calculates when an input analyte measurement is in error. This necessarily requires a calculation of an internal state variable to determine whether an error is present. The claim language for “calculating” is incredibly broad. If the Applicant wishes a particular type of “calculating” to be protected, the Examiner suggests claiming the features that are argued.
The Applicant further argues that Gupta fails to disclose “receiving gold standard data corresponding to the internal state variable” as recited by claim 1. Because Gupta does not calculate an internal state variable in the first place, none of the cited passages can disclose receiving gold-standard data “corresponding to the internal state variable”.
The Examiner respectfully disagrees. This argument is completely predicated on the previous argument being true. That argument has been refuted. Please refer above.
The Applicant further argues that Gupta fails to disclose “performing a statistical performance assessment of the model of the internal state variable”.
The Examiner respectfully disagrees. Comparing calibration models against analyte references is a statistical performance assessment. The term “statistical performance assessment” is an incredibly broad term that encompasses any sort of statistical or mathematical function. Determining a deviation in function as taught by Gupta satisfies this limitation. If there is a particular statistical measurement that the Applicant would like protection for, the Examiner encourages the Applicant to claim that measurement specifically.
The Applicant further argues that Gupta fails to disclose “determining whether performance of the model of the internal state variable is above a prescribed performance threshold”.
The Examiner respectfully disagrees. Determining when enough training data has been accumulated is a determination of whether performance of the model of the internal state variable is above a prescribed performance threshold. In a similar manner as what is seen above, the Applicant has used incredibly broad language and argued for a narrower interpretation of that the claims actually say.
The Applicant further argues that Gupta fails to disclose “determining a source of inconsistent data negatively impacting the performance of the model of the internal state variable”. There is no internal state model to diagnose in Gupta.
The Examiner respectfully disagrees. The Applicant’s argument are predicated on previous arguments made holding true. Those arguments have been refuted. Accordingly, this argument is without weight. In Gupta, the model is dependent on the performance of sensors to measure analyte levels. Analyte levels are an internal state variable which is incorporated into this internal state variable model. Accordingly evaluation of the function of sensors determines a source of inconsistent data negatively impacting the performance of the model.
The Applicant further argues that Gupta merely receives analyte data. It does not calculate using the model it even if it were considered to be an internal state variable.
The Examiner respectfully disagrees. Gupta clearly calculates when an input analyte measurement is in error. This necessarily requires a calculation of an internal state variable to determine whether an error is present.
The Applicant further argues that Gupta does not identify sources of data in consistency in relation to the internal state model’s performance or why the inconsistency occurs.
The Examiner respectfully disagrees. The claim does not require that a “why” be determined. The Applicant has argued for features that are not claimed. Furthermore, it would appear that the Applicant is trying to claim a determination of a problem with the model itself as opposed to factors impacting the performance such as input data. Sensors in Gupta are a source of data for the model that impacts its performance. Accordingly, Gupta reads on this limitation.
The Applicant further argues that the determination of a source requires an identification step linking data inconsistency to model-performance degradation for the internal state model.
The Examiner respectfully disagrees. Similar to the above response, the Applicant’s broad claim language does not necessitate determining problem with a specific feature within the internal state variable model itself. The BRI includes any factor that impacts its performance, such as input data.
Conclusion
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/CHAD A NEWTON/Primary Examiner, Art Unit 3681