Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This office action for the 19/170547 application is in response to the communications filed April 04, 2025.
Claims 1-43 were initially submitted April 04, 2025.
Claims 1-43 were cancelled April 04, 2025.
Claims 44-63 were added as new April 04, 2025.
Claims 44-63 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: “predictive analytics module” and “prescriptive analytics module” in claim 60.
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 44-63 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 44,
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 receive, input data of a patient, wherein the input data comprises one or more of a first patient data, a first prescription data, a first drug data, a first dispenser data, and a first prescriber data; derive, one or more attribute variables, based on the input data, wherein the attribute variables comprise one or more of a second patient data, a second prescription data, a second drug data, a second dispenser data, and a second prescriber data; predict a risk score for the patient based on the attribute variables, wherein the risk score is a probability of the patient developing an opioid use disorder, and determine, a list of the attribute variables that contribute to the risk score, wherein the list comprises a percentage contribution of each of the attribute variables in the list to the risk score, wherein the list comprises a subset of the attribute variables; provide a treatment recommendation based on the risk score and the attribute variables, wherein the treatment recommendation comprises one or more of a dosage, an alternate medication, a craving- suppression medication, a behavioral intervention, a therapy, a withdrawal reduction treatment, and educational materials to overcome the opioid use disorder; and identifying and preventing the opioid use disorder in the patient, wherein the patient is being treated for pain with a prescription drug, and wherein the prescription drug is an opioid. 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. 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 comprising: a processor storing instructions in a non-transitory memory that, when executed, cause the processor to:”, “using a predictive analytics module comprising a first artificial intelligence and machine learning model,”, “wherein the first artificial intelligence and machine learning model that provides explainable outcome and is pre-trained on a training database;”, “using a prescriptive analytics module comprising a second artificial intelligence and machine learning model,”, “wherein the second artificial intelligence and machine learning model is pre-trained on the training database,”, and “wherein the system is configured to one or more of” which corresponds to merely using a computer as a tool to perform an abstract idea. Paragraph [0091] of the as-filed specification describes that the hardware that implements the steps of the abstract idea amount to nothing 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 45,
Claim 45 depends from claim 44 and inherits all the limitations of the claim from which it depends. Claim 45 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 input data is considered from one or more databases, wherein the databases comprise Prescription Drug Monitoring Program (PDMP) and Electronic Health Record (EHR).” 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 46,
Claim 46 depends from claim 44 and inherits all the limitations of the claim from which it depends. Claim 46 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 first patient data comprises at least one or more of patient identification details, patient date of birth, and patient location.” 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 47,
Claim 47 depends from claim 44 and inherits all the limitations of the claim from which it depends. Claim 47 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 first prescription data comprises at least one or more of prescription filled data, prescription date, and prescription number.” 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 48,
Claim 48 depends from claim 44 and inherits all the limitations of the claim from which it depends. Claim 48 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 first drug data comprises at least one or more of drug name, drug strength, drug form, drug quantity, and number of days of supply.” 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 49,
Claim 49 depends from claim 44 and inherits all the limitations of the claim from which it depends. Claim 49 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 first dispenser data comprises one or more of dispenser identification details and dispenser location.” 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 50,
Claim 50 depends from claim 44 and inherits all the limitations of the claim from which it depends. Claim 50 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 first prescriber data comprises one or more of prescriber identification details and prescriber location.” 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 51,
Claim 51 depends from claim 44 and inherits all the limitations of the claim from which it depends. Claim 51 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 second patient data comprises one or more of patient unique identification, patient age, and patient location.” 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 52,
Claim 52 depends from claim 44 and inherits all the limitations of the claim from which it depends. Claim 52 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 second prescription data comprises one or more of number of prescriptions in a time period, days since last prescription, and average duration between prescriptions.” 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 53,
Claim 53 depends from claim 44 and inherits all the limitations of the claim from which it depends. Claim 53 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 second drug data comprises one or more of average drug quantity, highest drug quantity, average days of supply, highest days of supply, highest drug strength, lowest drug strength as per the first prescription data and the first drug 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 54,
Claim 54 depends from claim 44 and inherits all the limitations of the claim from which it depends. Claim 54 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 second dispenser data comprises one or more of dispenser identification details, dispenser location, highest number of prescriptions filled by a dispenser, identification details of most frequently used dispenser for filling the prescription drug.” 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 55,
Claim 55 depends from claim 44 and inherits all the limitations of the claim from which it depends. Claim 55 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 second prescriber data comprises one or more of prescriber identification details, prescriber location, highest number of prescriptions prescribed, and most frequent prescriber identification details.” 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 56,
Claim 56 depends from claim 44 and inherits all the limitations of the claim from which it depends. Claim 56 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“a logistic regression model configured to explain why a patient is at risk” 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.
“wherein the first artificial intelligence and machine learning model comprises” 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 57,
Claim 57 depends from claim 56 and inherits all the limitations of the claim from which it depends. Claim 57 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 logistic regression model applies a linear and weighted contribution of the input data; and wherein the logistic regression model comprises a multivariate logistic regression.” 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 58,
Claim 58 depends from claim 44 and inherits all the limitations of the claim from which it depends. Claim 58 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“use by a physician as a decision support system for prescribing one or more of the prescription drug and the treatment recommendation” 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.
“wherein the system is configured 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 59,
Claim 59 depends from claim 44 and inherits all the limitations of the claim from which it depends. Claim 59 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“a clustering model, wherein the clustering model comprises one of recursive partitioning and random forest clustering” 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.
“wherein the second artificial intelligence and machine learning model comprises” 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 60,
Claim 60 is substantially similar to claim 43. Accordingly claim 60 is rejected for the same reasons as claim 43.
As per claim 61,
Claim 61 depends from claim 60 and inherits all the limitations of the claim from which it depends. Claim 61 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 risk score is a value between 0 and 1;” 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.
“wherein the risk score along with the list of attributes is one or more of displayed on a display, stored to a database, and generates an alarm.” 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:
“wherein the risk score along with the list of attributes is one or more of displayed on a display, stored to a database, and generates an alarm.” 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:
“wherein the risk score along with the list of attributes is one or more of displayed on a display, …, and generates an alarm.” which corresponds to receiving or transmitting data over a network.
“stored to a database” which corresponds to storing and retrieving information in memory.
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 62,
Claim 62 depends from claim 60 and inherits all the limitations of the claim from which it depends. Claim 62 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 first artificial intelligence and machine learning model and the second artificial intelligence and machine learning model are retrained based on false positive and false negative results during a period of use of the first artificial intelligence and machine learning model and the second artificial intelligence and machine learning model.” 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 63,
Claim 63 is substantially similar to claim 43. Accordingly claim 63 is rejected for the same reasons as claim 43.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 44-55, 58, and 60-63 are rejected under 35 U.S.C. 103 as being unpatentable over Whitworth et al. (US 2024/358597; herein referred to as Whitworth) in view of Bostic et al. (US 2020/0303047; herein referred to as Bostic)
As per claim 44,
Whitworth teaches a system comprising: a processor storing instructions in a non-transitory memory:
(Paragraph [0052] of Whitworth. The teaching describes the electronic circuit 178 used to control the dispensing device 10. The electronic circuit 178 is comprised of a microcontroller 218, a cellular module 224, a recharging port 112, a timer power supply 226, a timer 228, an antenna 230, a power supply connector 232, a sim card 234, a solenoid control 236, and a sensor 238. The microcontroller 218 has inferior components, processor 220 and memory 222, which store and execute computer language instructions.)
Whitworth further teaches receive, input data of a patient, wherein the input data comprises one or more of a first patient data, a first prescription data, a first drug data, a first dispenser data, and a first prescriber data:
(Paragraph [0052] of Whitworth. The teaching describes that the memory 222 is additionally used to store prescription specific information, patient information, and database information. Prescription-specific information may include the date, time, number of pills taken, iterations, and the number of pills initially prescribed, etc. Patient information such as a uniquely generated identification number, date of the prescription on the dispensing device, and the name of the prescription may also be stored.)
Whitworth further teaches derive, one or more attribute variables, based on the input data, wherein the attribute variables comprise one or more of a second patient data, a second prescription data, a second drug data, a second dispenser data, and a second prescriber data:
(Paragraph [0061] of Whitworth. The teaching describes that three options share the same basic prescription information such as the type, name, start date, total number of pills, pills taken so far, and the pills left in the prescription. What can vary are the diagnostics of the PillSafe Device. For example, FIG. 5B shows what a patient would see if the dispensing button 104 was restricted in the bottom position. The information shown details the exact time, date, and elapsed time from current time until the alarm will trigger and the dispensing button 104 will be released.)
Whitworth further teaches predict, using a predictive analytics module comprising a first artificial intelligence and machine learning model, a risk score for the patient based on the attribute variables, wherein the risk score is a probability of the patient developing an opioid use disorder, wherein the first artificial intelligence and machine learning model that provides explainable outcome and is pre-trained on a training database, determine, a list of the attribute variables that contribute to the risk score, wherein the list comprises a percentage contribution of each of the attribute variables in the list to the risk score, wherein the list comprises a subset of the attribute variables, and wherein the system is configured to one or more of identifying and preventing the opioid use disorder in the patient, wherein the patient is being treated for pain with a prescription drug, and wherein the prescription drug is an opioid:
(Paragraph [0062] and Figure 5O of Whitworth. The teaching describes that where the medical professional successfully logged in and searched the patient, they can now search for the various prescriptions under the selected patient as illustrated in FIG. 5K. Step 430 is an example of one prescription for this patient and this section shows where each prescribed prescription would populate showing the type and date of the prescription. Selecting the check patient button (step 418), the medical profession gets a quick snapshot of the selected patient's overall prescription score and risk assessment as illustrated in FIG. 50 . The overall prescription score may be established by taking average statistics [percent contribution] over all prescriptions under the selected patient. The risk assessment addresses how risky this patient is with respect to being prescribed opioids and it can be used to predict or assesses the likelihood of future abuse. Such risk assessment may be calculated based on certain parameters, minor calculations and/or human input, but it is contemplated that future applications may also utilise artificial intelligence and machine learning to make a similar assessment. The following is one example of how artificial intelligence and machine learning may be used to calculate the risk assessment of a patient. Other predictions of patient adherence can be made but the process will be the same. All that changes for other risk predictions is that a new machine learning model would need to be trained for each desired risk prediction output. One risk prediction output based on the PillSafe devices captured data is to determine whether or not a patient is going to finish the prescription. Input data such as the number of pills taken, number of pills in the prescription, and amount of time between the alarm and taken time for each pill may be formatted into column tabular data format. The output data will be a “yes” or “no” response based on if the prescription is finished. The finishing of a prescription is measured by the successful unlocking of the disposable capsule from the dispensing device. Using a select number of patients' past data regarding their relationship with finishing their prescriptions, a neural network regression model may be trained. This model will then be used to assess whether new or active prescriptions run the risk of the output being a “no”. This means that the model is predicting with a certain confidence that the patient in question will/will not finish the current prescription. To further examine specific prescription data (step 386) instead of all the prescription data at once, the searched prescription (step 430) may be selected. FIGS. 5E-5G are the data for the specific selected prescription (step 430). FIG. 5E is the basic information, such as patient name, prescription start date, drug name, drug type, time between each dosage, number of pills prescribed, and how many pills have currently been taken. Unlock PillSafe Button (step 412) triggers the ability for the patient to unlock the dispensing body assembly 100 from the disposable capsule assembly 102.)
Whitworth does not explicitly teach provide, using a prescriptive analytics module comprising a second artificial intelligence and machine learning model, a treatment recommendation based on the risk score and the attribute variables, wherein the second artificial intelligence and machine learning model is pre-trained on the training database, wherein the treatment recommendation comprises one or more of a dosage, an alternate medication, a craving- suppression medication, a behavioral intervention, a therapy, a withdrawal reduction treatment, and educational materials to overcome the opioid use disorder.
However, Bostic teaches a prescriptive analytics module comprising a second artificial intelligence and machine learning model, a treatment recommendation based on the risk score and the attribute variables, wherein the second artificial intelligence and machine learning model is pre-trained on the training database, wherein the treatment recommendation comprises one or more of a dosage, an alternate medication, a craving- suppression medication, a behavioral intervention, a therapy, a withdrawal reduction treatment, and educational materials to overcome the opioid use disorder:
(Paragraphs [0048] and [0049] of Bostic. The teaching describes that with respect to the misuse of controlled medication function, the present disclosure provides for techniques for determining whether a usage profile of a patient is indicative of potential misuse of one or more controlled medications. A machine learning model [a first machine learning model that outputs risk] or other forms of artificial intelligence are utilized to generate a potential misuse score or similar measurement of the likelihood that a patient is or has the potential for misusing a controlled substance. In some aspects, laboratory test results from a laboratory that are indicative of a toxicology screen of the patient are utilized, in conjunction with patient attributes of the patient, to generate the usage profile of the patient. Various features of the usage profile can be utilized with the artificial intelligence system to determine the likelihood that the patient is or has the potential for misusing a controlled substance. In response to determining that the patient is or has the potential for misusing a controlled substance, or when a healthcare professional is otherwise treating the patient, a notification or report of the patient's potential for misusing a controlled substance can be provided in order to assist with the treatment of the patient. With respect to the laboratory test recommendation function, the present disclosure provides for techniques for determining whether to recommend one or more laboratory tests for a patient, e.g., at a time of prescribing a controlled substance. A machine learning model [a second machine learning model that is based on the risk output of the first] or other forms of artificial intelligence are utilized to generate a laboratory test recommendation [a therapy] or similar measurement of the likelihood that the patient would benefit from one or more specific laboratory tests before the patient is given a proposed prescription. In some aspects, a proposed prescription for the patient is utilized, in conjunction with patient attributes of the patient (e.g., a diagnosis), to determine whether to recommend one or more specific laboratory tests. Various features of the proposed prescription and patient attributes can be utilized with the artificial intelligence system to determine the likelihood that the patient would benefit from a specific laboratory test before beginning the prescription of the controlled substance. In response to determining that the patient would benefit from one or more specific laboratory tests, or when a healthcare professional is otherwise treating the patient, a notification, report, or laboratory test recommendation for the patient can be provided in order to assist with the treatment of the patient.)
It would have been obvious to one of ordinary skill in the art before the time of filing to add to the teachings of Whitworth, the second machine learning model of Bostic. Paragraph [0044] of Bostic teaches that the disclosed invention is aimed to improve methods and systems to avoid opioid abuse. One of ordinary skill in the art in possession of Whitworth would have looked to Bostic to gain such improvements in its own invention. One of ordinary skill in the art would have added to the teaching of Whitworth, the teaching of Bostic based on this incentive without yielding unexpected results.
As per claim 45,
The combined teaching of Whitworth and Bostic teaches the limitations of claim 44.
Whitworth further teaches wherein the input data is considered from one or more databases, wherein the databases comprise Prescription Drug Monitoring Program (PDMP) and Electronic Health Record (EHR):
(Paragraph [0052] of Whitworth. The teaching describes that the memory 222 is additionally used to store prescription specific information, patient information, and database information. Prescription-specific information may include the date, time, number of pills taken, iterations, and the number of pills initially prescribed, etc. Patient information such as a uniquely generated identification number, date of the prescription on the dispensing device, and the name of the prescription may also be stored.)
As per claim 46,
The combined teaching of Whitworth and Bostic teaches the limitations of claim 44.
Whitworth further teaches wherein the first patient data comprises at least one or more of patient identification details, patient date of birth, and patient location:
(Paragraph [0052] of Whitworth. The teaching describes that the memory 222 is additionally used to store prescription specific information, patient information, and database information. Prescription-specific information may include the date, time, number of pills taken, iterations, and the number of pills initially prescribed, etc. Patient information such as a uniquely generated identification number, date of the prescription on the dispensing device, and the name of the prescription may also be stored.)
As per claim 47,
The combined teaching of Whitworth and Bostic teaches the limitations of claim 44.
Whitworth further teaches wherein the first prescription data comprises at least one or more of prescription filled data, prescription date, and prescription number:
(Paragraph [0052] of Whitworth. The teaching describes that the memory 222 is additionally used to store prescription specific information, patient information, and database information. Prescription-specific information may include the date, time, number of pills taken, iterations, and the number of pills initially prescribed, etc. Patient information such as a uniquely generated identification number, date of the prescription on the dispensing device, and the name of the prescription may also be stored.)
As per claim 48,
The combined teaching of Whitworth and Bostic teaches the limitations of claim 44.
Whitworth further teaches wherein the first drug data comprises at least one or more of drug name, drug strength, drug form, drug quantity, and number of days of supply:
(Paragraph [0052] of Whitworth. The teaching describes that the memory 222 is additionally used to store prescription specific information, patient information, and database information. Prescription-specific information may include the date, time, number of pills taken, iterations, and the number of pills initially prescribed, etc. Patient information such as a uniquely generated identification number, date of the prescription on the dispensing device, and the name of the prescription may also be stored.)
As per claim 49,
The combined teaching of Whitworth and Bostic teaches the limitations of claim 44.
Whitworth further teaches wherein the first dispenser data comprises one or more of dispenser identification details and dispenser location:
(Paragraph [0053] of Whitworth. The teaching describes pill dispensing device 10 communicates to both the database and the user interface. Starting at PillSafe Device (step 402) can both send and receive data directly from the database 389 through the use of 2G/3G/4G/5G cellular data or WIFI (step 400). The PillSafe Device 402 can communicate directly to the PillSafe User Interface 392 through the use of classic Bluetooth or Bluetooth Low Energy. Every form of communication the PillSafe Device 402 has is encrypted for security of the data. The PillSafe User Interface 392 has its own memory 394 which helps limit the need for repeated sending and receiving of data from the database 389. This means that the dispenser identification information must be shared and stored for the connection to be made.)
As per claim 50,
The combined teaching of Whitworth and Bostic teaches the limitations of claim 44.
Whitworth further teaches wherein the first prescriber data comprises one or more of prescriber identification details and prescriber location:
(Paragraph [0052] of Whitworth. The teaching describes that the memory 222 is additionally used to store prescription specific information, patient information, and database information. Prescription-specific information may include the date, time, number of pills taken, iterations, and the number of pills initially prescribed, etc. Patient information such as a uniquely generated identification number, date of the prescription on the dispensing device, and the name of the prescription may also be stored. It is understood that prescriber information is included in the prescription specific information given that prescriptions cannot be filled without an identified prescriber.)
As per claim 51,
The combined teaching of Whitworth and Bostic teaches the limitations of claim 44.
Whitworth further teaches wherein the second patient data comprises one or more of patient unique identification, patient age, and patient location:
(Paragraph [0052] of Whitworth. The teaching describes that the memory 222 is additionally used to store prescription specific information, patient information, and database information. Prescription-specific information may include the date, time, number of pills taken, iterations, and the number of pills initially prescribed, etc. Patient information such as a uniquely generated identification number, date of the prescription on the dispensing device, and the name of the prescription may also be stored.)
(Paragraph [0061] of Whitworth. The teaching describes that three options share the same basic prescription information such as the type, name, start date, total number of pills, pills taken so far, and the pills left in the prescription. What can vary are the diagnostics of the PillSafe Device. For example, FIG. 5B shows what a patient would see if the dispensing button 104 was restricted in the bottom position. The information shown details the exact time, date, and elapsed time from current time until the alarm will trigger and the dispensing button 104 will be released.)
As per claim 52,
The combined teaching of Whitworth and Bostic teaches the limitations of claim 44.
Whitworth further teaches wherein the second prescription data comprises one or more of number of prescriptions in a time period, days since last prescription, and average duration between prescriptions:
(Paragraph [0061] of Whitworth. The teaching describes that three options share the same basic prescription information such as the type, name, start date, total number of pills, pills taken so far, and the pills left in the prescription. What can vary are the diagnostics of the PillSafe Device. For example, FIG. 5B shows what a patient would see if the dispensing button 104 was restricted in the bottom position. The information shown details the exact time, date, and elapsed time from current time until the alarm will trigger and the dispensing button 104 will be released.)
As per claim 53,
The combined teaching of Whitworth and Bostic teaches the limitations of claim 44.
Whitworth further teaches wherein the second drug data comprises one or more of average drug quantity, highest drug quantity, average days of supply, highest days of supply, highest drug strength, lowest drug strength as per the first prescription data and the first drug data:
(Paragraph [0061] of Whitworth. The teaching describes that three options share the same basic prescription information such as the type, name, start date, total number of pills, pills taken so far, and the pills left in the prescription. What can vary are the diagnostics of the PillSafe Device. For example, FIG. 5B shows what a patient would see if the dispensing button 104 was restricted in the bottom position. The information shown details the exact time, date, and elapsed time from current time until the alarm will trigger and the dispensing button 104 will be released.)
As per claim 54,
The combined teaching of Whitworth and Bostic teaches the limitations of claim 44.
Whitworth further teaches wherein the second dispenser data comprises one or more of dispenser identification details, dispenser location, highest number of prescriptions filled by a dispenser, identification details of most frequently used dispenser for filling the prescription drug:
(Paragraph [0061] of Whitworth. The teaching describes that three options share the same basic prescription information such as the type, name, start date, total number of pills, pills taken so far, and the pills left in the prescription. What can vary are the diagnostics of the PillSafe Device. For example, FIG. 5B shows what a patient would see if the dispensing button 104 was restricted in the bottom position. The information shown details the exact time, date, and elapsed time from current time until the alarm will trigger and the dispensing button 104 will be released. Here it is understood that the locked Pillsafe Device is an identification detail as it pertains to a particular dispending device that is locked)
As per claim 55,
The combined teaching of Whitworth and Bostic teaches the limitations of claim 44.
Whitworth further teaches wherein the second prescriber data comprises one or more of prescriber identification details, prescriber location, highest number of prescriptions prescribed, and most frequent prescriber identification details:
(Paragraph [0062] and Figure 5O of Whitworth. The teaching describes that where the medical professional successfully logged in and searched the patient, they can now search for the various prescriptions under the selected patient as illustrated in FIG. 5K. Step 430 is an example of one prescription for this patient and this section shows where each prescribed prescription would populate showing the type and date of the prescription. Selecting the check patient button (step 418), the medical profession gets a quick snapshot of the selected patient's overall prescription score and risk assessment as illustrated in FIG. 50 . The overall prescription score may be established by taking average statistics over all prescriptions under the selected patient. The risk assessment addresses how risky this patient is with respect to being prescribed opioids and it can be used to predict or assesses the likelihood of future abuse. Such risk assessment may be calculated based on certain parameters, minor calculations and/or human input, but it is contemplated that future applications may also utilise artificial intelligence and machine learning to make a similar assessment. The following is one example of how artificial intelligence and machine learning may be used to calculate the risk assessment of a patient. Other predictions of patient adherence can be made but the process will be the same. All that changes for other risk predictions is that a new machine learning model would need to be trained for each desired risk prediction output. One risk prediction output based on the PillSafe devices captured data is to determine whether or not a patient is going to finish the prescription. Input data such as the number of pills taken, number of pills in the prescription, and amount of time between the alarm and taken time for each pill may be formatted into column tabular data format. The output data will be a “yes” or “no” response based on if the prescription is finished. The finishing of a prescription is measured by the successful unlocking of the disposable capsule from the dispensing device. Using a select number of patients' past data regarding their relationship with finishing their prescriptions, a neural network regression model may be trained. This model will then be used to assess whether new or active prescriptions run the risk of the output being a “no”. This means that the model is predicting with a certain confidence that the patient in question will/will not finish the current prescription. To further examine specific prescription data (step 386) instead of all the prescription data at once, the searched prescription (step 430) may be selected. FIGS. 5E-5G are the data for the specific selected prescription (step 430). FIG. 5E is the basic information, such as patient name, prescription start date, drug name, drug type, time between each dosage, number of pills prescribed [prescriber identification details; the particular physician prescribed this number of pills], and how many pills have currently been taken. Unlock PillSafe Button (step 412) triggers the ability for the patient to unlock the dispensing body assembly 100 from the disposable capsule assembly 102.)
As per claim 58,
The combined teaching of Whitworth and Bostic teaches the limitations of claim 44.
The combined teaching of Whitworth and Bostic further teaches wherein the system is configured for use by a physician as a decision support system for prescribing one or more of the prescription drug and the treatment recommendation:
(Paragraph [0062] and Figure 5O of Whitworth. The teaching describes that where the medical professional successfully logged in and searched the patient, they can now search for the various prescriptions under the selected patient as illustrated in FIG. 5K. Step 430 is an example of one prescription for this patient and this section shows where each prescribed prescription would populate showing the type and date of the prescription. Selecting the check patient button (step 418), the medical profession gets a quick snapshot of the selected patient's overall prescription score and risk assessment as illustrated in FIG. 50 . The overall prescription score may be established by taking average statistics over all prescriptions under the selected patient. The risk assessment addresses how risky this patient is with respect to being prescribed opioids and it can be used to predict or assesses the likelihood of future abuse. Such risk assessment may be calculated based on certain parameters, minor calculations and/or human input, but it is contemplated that future applications may also utilise artificial intelligence and machine learning to make a similar assessment. The following is one example of how artificial intelligence and machine learning may be used to calculate the risk assessment of a patient. Other predictions of patient adherence can be made but the process will be the same. All that changes for other risk predictions is that a new machine learning model would need to be trained for each desired risk prediction output. One risk prediction output based on the PillSafe devices captured data is to determine whether or not a patient is going to finish the prescription. Input data such as the number of pills taken, number of pills in the prescription, and amount of time between the alarm and taken time for each pill may be formatted into column tabular data format. The output data will be a “yes” or “no” response based on if the prescription is finished. The finishing of a prescription is measured by the successful unlocking of the disposable capsule from the dispensing device. Using a select number of patients' past data regarding their relationship with finishing their prescriptions, a neural network regression model may be trained. This model will then be used to assess whether new or active prescriptions run the risk of the output being a “no”. This means that the model is predicting with a certain confidence that the patient in question will/will not finish the current prescription. To further examine specific prescription data (step 386) instead of all the prescription data at once, the searched prescription (step 430) may be selected. FIGS. 5E-5G are the data for the specific selected prescription (step 430). FIG. 5E is the basic information, such as patient name, prescription start date, drug name, drug type, time between each dosage, number of pills prescribed, and how many pills have currently been taken. Unlock PillSafe Button (step 412) triggers the ability for the patient to unlock the dispensing body assembly 100 from the disposable capsule assembly 102.)
(Paragraphs [0048] and [0049] of Bostic. The teaching describes that with respect to the misuse of controlled medication function, the present disclosure provides for techniques for determining whether a usage profile of a patient is indicative of potential misuse of one or more controlled medications. A machine learning model [a first machine learning model that outputs risk] or other forms of artificial intelligence are utilized to generate a potential misuse score or similar measurement of the likelihood that a patient is or has the potential for misusing a controlled substance. In some aspects, laboratory test results from a laboratory that are indicative of a toxicology screen of the patient are utilized, in conjunction with patient attributes of the patient, to generate the usage profile of the patient. Various features of the usage profile can be utilized with the artificial intelligence system to determine the likelihood that the patient is or has the potential for misusing a controlled substance. In response to determining that the patient is or has the potential for misusing a controlled substance, or when a healthcare professional is otherwise treating the patient, a notification or report of the patient's potential for misusing a controlled substance can be provided in order to assist with the treatment of the patient. With respect to the laboratory test recommendation function, the present disclosure provides for techniques for determining whether to recommend one or more laboratory tests for a patient, e.g., at a time of prescribing a controlled substance. A machine learning model [a second machine learning model that is based on the risk output of the first] or other forms of artificial intelligence are utilized to generate a laboratory test recommendation [a therapy] or similar measurement of the likelihood that the patient would benefit from one or more specific laboratory tests before the patient is given a proposed prescription. In some aspects, a proposed prescription for the patient is utilized, in conjunction with patient attributes of the patient (e.g., a diagnosis), to determine whether to recommend one or more specific laboratory tests. Various features of the proposed prescription and patient attributes can be utilized with the artificial intelligence system to determine the likelihood that the patient would benefit from a specific laboratory test before beginning the prescription of the controlled substance. In response to determining that the patient would benefit from one or more specific laboratory tests, or when a healthcare professional is otherwise treating the patient, a notification, report, or laboratory test recommendation for the patient can be provided in order to assist with the treatment of the patient.)
As per claim 60,
Claim 60 is substantially similar to claim 43. Accordingly claim 60 is rejected for the same reasons as claim 43.
As per claim 61,
The combined teaching of Whitworth and Bostic teaches the limitations of claim 60.
Whitworth further teaches wherein the risk score is a value between 0 and 1; and wherein the risk score along with the list of attributes is one or more of displayed on a display, stored to a database, and generates an alarm:
(Paragraph [0062] and Figure 5O of Whitworth. The teaching describes that where the medical professional successfully logged in and searched the patient, they can now search for the various prescriptions under the selected patient as illustrated in FIG. 5K. Step 430 is an example of one prescription for this patient and this section shows where each prescribed prescription would populate showing the type and date of the prescription. Selecting the check patient button (step 418), the medical profession gets a quick snapshot of the selected patient's overall prescription score and risk assessment as illustrated in FIG. 50 . The overall prescription score may be established by taking average statistics over all prescriptions under the selected patient. The risk assessment addresses how risky this patient is with respect to being prescribed opioids and it can be used to predict or assesses the likelihood of future abuse. Such risk assessment may be calculated based on certain parameters, minor calculations and/or human input, but it is contemplated that future applications may also utilise artificial intelligence and machine learning to make a similar assessment. The following is one example of how artificial intelligence and machine learning may be used to calculate the risk assessment of a patient. Other predictions of patient adherence can be made but the process will be the same. All that changes for other risk predictions is that a new machine learning model would need to be trained for each desired risk prediction output. One risk prediction output based on the PillSafe devices captured data is to determine whether or not a patient is going to finish the prescription. Input data such as the number of pills taken, number of pills in the prescription, and amount of time between the alarm and taken time for each pill may be formatted into column tabular data format. The output data will be a “yes” or “no” [1 o 0 respectively] response based on if the prescription is finished. The finishing of a prescription is measured by the successful unlocking of the disposable capsule from the dispensing device. Using a select number of patients' past data regarding their relationship with finishing their prescriptions, a neural network regression model may be trained. This model will then be used to assess whether new or active prescriptions run the risk of the output being a “no”. This means that the model is predicting with a certain confidence that the patient in question will/will not finish the current prescription. To further examine specific prescription data (step 386) instead of all the prescription data at once, the searched prescription (step 430) may be selected. FIGS. 5E-5G are the data for the specific selected prescription (step 430). FIG. 5E is the basic information, such as patient name, prescription start date, drug name, drug type, time between each dosage, number of pills prescribed, and how many pills have currently been taken. Unlock PillSafe Button (step 412) triggers the ability for the patient to unlock the dispensing body assembly 100 from the disposable capsule assembly 102.)
As per claim 62,
The combined teaching of Whitworth and Bostic teaches the limitations of claim 60.
Bostic further teaches wherein the first artificial intelligence and machine learning model and the second artificial intelligence and machine learning model are retrained based on false positive and false negative results during a period of use of the first artificial intelligence and machine learning model and the second artificial intelligence and machine learning model:
(Paragraph [0071] of Bostic. The teaching describes that the the prescription monitoring system 106 may utilize machine learning and AI techniques to determine if a patient is misusing a prescription medication. In some of these embodiments, the prescription monitoring system 106 may leverage one or more machine learned models to determine if a patient is misusing a prescription medication. For example, in embodiments, a machine learned model may be trained to identify when a patient is likely abusing a particular prescription medication (e.g., an opiate, a benzodiazepine, or amphetamine). These models may be trained on training data samples relating to patients that were determined to be abusing the medication and patients that were determined to be using the prescription medication properly [construed as inclusive of false positives and negatives]. In these embodiments, the prescription monitoring system 106 may obtain lab test results of the patient (e.g., a blood test and/or a urine analysis test), a prescription medication that the patient is being prescribed, and relevant patient information (e.g., an ailment of the patient, other prescriptions of the patient, an age of the patient, a gender of the patient, a weight of the patient, a body fat percentage of the patient, and the like). In embodiments, the machine learned model may output a classification relating to the patient that indicates whether the patient is likely abusing the medication or using the medication properly. In embodiments, the classification may include a confidence score, whereby a higher confidence score indicates a higher degree of confidence in the classification. In some of these embodiments, the machine learned model(s) may be trained to identify the type of abuse of a medication (e.g., overuse/addiction, use with other controlled substances, and the like). In embodiments, the prescription monitoring system 106 may leverage other machine learned models. For instance, the prescription monitoring system 106 may leverage a machine learned model that is trained to identify when a patient is underusing a prescription medication, which may be indicative of a patient illegally distributing the prescription medication to other people.)
As per claim 63,
Claim 63 is substantially similar to claim 43. Accordingly claim 63 is rejected for the same reasons as claim 43.
Claims 56 and 57 are rejected under 35 U.S.C. 103 as being unpatentable over Whitworth and Bostic in further view of Choi et al. (US 2015/0066818; herein referred to as Choi)
As per claim 56,
The combined teaching of Whitworth and Bostic teaches the limitations of claim 44.
Whitworth further teaches wherein the first artificial intelligence and machine learning model comprises why a patient is at risk:
(Paragraph [0062] and Figure 5O of Whitworth. The teaching describes that where the medical professional successfully logged in and searched the patient, they can now search for the various prescriptions under the selected patient as illustrated in FIG. 5K. Step 430 is an example of one prescription for this patient and this section shows where each prescribed prescription would populate showing the type and date of the prescription. Selecting the check patient button (step 418), the medical profession gets a quick snapshot of the selected patient's overall prescription score and risk assessment as illustrated in FIG. 50 . The overall prescription score may be established by taking average statistics [percent contribution] over all prescriptions under the selected patient. The risk assessment addresses how risky this patient is with respect to being prescribed opioids and it can be used to predict or assesses the likelihood of future abuse. Such risk assessment may be calculated based on certain parameters, minor calculations and/or human input, but it is contemplated that future applications may also utilise artificial intelligence and machine learning to make a similar assessment. The following is one example of how artificial intelligence and machine learning may be used to calculate the risk assessment of a patient. Other predictions of patient adherence can be made but the process will be the same. All that changes for other risk predictions is that a new machine learning model would need to be trained for each desired risk prediction output. One risk prediction output based on the PillSafe devices captured data is to determine whether or not a patient is going to finish the prescription. Input data such as the number of pills taken, number of pills in the prescription, and amount of time between the alarm and taken time for each pill may be formatted into column tabular data format. The output data will be a “yes” or “no” response based on if the prescription is finished. The finishing of a prescription is measured by the successful unlocking of the disposable capsule from the dispensing device. Using a select number of patients' past data regarding their relationship with finishing their prescriptions, a neural network regression model may be trained. This model will then be used to assess whether new or active prescriptions run the risk of the output being a “no”. This means that the model is predicting with a certain confidence that the patient in question will/will not finish the current prescription. To further examine specific prescription data (step 386) instead of all the prescription data at once, the searched prescription (step 430) may be selected. FIGS. 5E-5G are the data for the specific selected prescription (step 430). FIG. 5E is the basic information, such as patient name, prescription start date, drug name, drug type, time between each dosage, number of pills prescribed, and how many pills have currently been taken. Unlock PillSafe Button (step 412) triggers the ability for the patient to unlock the dispensing body assembly 100 from the disposable capsule assembly 102.)
The combined teaching of Whitworth does not explicitly teach a logistic regression model incorporated into the first artificial intelligence and machine learning model.
However, Choi teaches a machine learning model that predicts the state of a patient that includes a logistic regression model:
(Paragraph [0048] of Choi. The teaching describes that associating the feature vector with the presence or absence of plaque at each point of the patient-specific geometric model (step 416). Method 400 may involve continuing to perform the above steps 412, 414, 416, for each of a plurality of points in the patient-specific geometric model (step 418), and for each of any number of patients on which a machine learning algorithm may be based (step 420). Method 400 may then include training the machine learning algorithm to predict the probability of the presence of plaque at the points from the feature vectors at the points (step 422). Examples of machine learning algorithms suitable for performing this task may include support vector machines (SVMs), multi-layer perceptrons (MLPs), and/or multivariate regression (MVR) (e.g., weighted linear or logistic regression).)
It would have been obvious to one of ordinary skill in the art before the time of filing to add to the machine learning model of the combined teaching of Whitworth and Bostic, the logistic regression processes of Choi. Paragraph [0073] of Choi describes that the machine learning methods disclosed, including the logistic regression, are at a level well-known in the art and among top selections of those who implement machine learning methods. One of ordinary skill in the art would have added to the combined teaching of Whitworth and Bostic, the teachings of Choi based on this understanding without yielding unexpected results.
As per claim 57,
The combine teaching of Whitworth, Bostic and Choi teaches the limitation of claim 56.
Choi further teaches wherein the logistic regression model applies a linear and weighted contribution of the input data; and wherein the logistic regression model comprises a multivariate logistic regression:
(Paragraph [0048] of Choi. The teaching describes that associating the feature vector with the presence or absence of plaque at each point of the patient-specific geometric model (step 416). Method 400 may involve continuing to perform the above steps 412, 414, 416, for each of a plurality of points in the patient-specific geometric model (step 418), and for each of any number of patients on which a machine learning algorithm may be based (step 420). Method 400 may then include training the machine learning algorithm to predict the probability of the presence of plaque at the points from the feature vectors at the points (step 422). Examples of machine learning algorithms suitable for performing this task may include support vector machines (SVMs), multi-layer perceptrons (MLPs), and/or multivariate regression (MVR) (e.g., weighted linear or logistic regression).)
Claim 59 is rejected under 35 U.S.C. 103 as being unpatentable over Whitworth and Bostic in further view of Yocca et al. (US 2022/0202373; herein referred to as Yocca)
As per claim 59,
The combined teaching of Whitworth and Bostic teaches the limitations of claim 44.
The combined teaching of Whitworth and Bostic does not explicitly teach wherein the second artificial intelligence and machine learning model comprises a clustering model, wherein the clustering model comprises one of recursive partitioning and random forest clustering.
However, Yocca teaches a machine learning model that is used to determine treatments to give to an at risk patient wherein the machine learning model is a clustering model comprising a random forest clustering scheme:
(Paragraphs [0037]-[0042] and [0144] of Yocca. The teaching describes (a) receiving first physiological data of sympathetic nervous system activity; (b) establishing a baseline value of at least one physiological parameter by training at least one machine learning model using the first physiological data; (c) receiving, from a first monitoring device attached to a subject, second physiological data of sympathetic nervous system activity in the subject; (d) analyzing, using the at least one machine learning model and based on the baseline value of at least one physiological parameter, the second physiological data to predict an agitation episode in the subject; and (e) sending, based on predicting the agitation episode of the subject, a signal to a second monitoring device to notify the second monitoring device of the prediction of the agitation episode in the subject such that treatment can be provided to the subject to decrease sympathetic nervous system activity in the subject. The machine learning models (or other mathematical models) can be trained using supervised learning and unsupervised learning. The machine learning model (or other mathematical models) of the apparatus (800) is trained based on at least one of supervised learning, unsupervised learning, semi-supervised learning, and/or reinforcement learning. In some implementations the supervised learning can include a regression model (e.g., linear regression), in which a target value is found based on independent predictors. This follows that the said model is used to find the relation between a dependent variable and an independent variable. The at least one machine learning model may be any suitable type of machine learning model, including, but not limited to, at least one of a linear regression model, a logistic regression model, a decision tree model, a random forest model, a neural network, a deep neural network, and/or a gradient boosting model.)
It would have been obvious to one of ordinary skill in the art before the time of filing to add to the second machine learning model of the combined teaching of Whitworth and Bostic, the random forest-based machine learning capabilities of Yocca. Both the combined teaching of Whitworth and Bostic and Yocca exist in the same field of endeavor of machine learning-based medical treatments. All of the limitations claims are contained within these prior arts, though in separate references. Each of the prior arts would have performed as they had when combined as they would have apart and have yielded no unexpected results. Accordingly, such a combination would have been obvious to one of ordinary skill in the art.
Conclusion
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/CHAD A NEWTON/Primary Examiner, Art Unit 3681
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