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 .
Election by Original Presentation
Newly submitted claim 33 directed to an invention that is independent or distinct from the invention originally claimed for the following reasons:
Claim 33 is directed to a method of treating a patient that does not qualify for percutaneous coronary intervention (PCI), referred to as Invention II. Originally presented claims were directed to a method and system of identifying whether a patient is eligible for a protected percutaneous coronary intervention (PCI), referred to as Invention I.
Inventions I and II are related as subcombinations disclosed as usable together in a single combination. The subcombinations are distinct if they do not overlap in scope and are not obvious variants, and if it is shown that at least one subcombination is separately usable. In the instant case, Invention I has a scope of determining eligibility for a protected PCI, whereas Invention II has a scope of treating a patient that does not qualify for a PCI. These scopes do not overlap as they are divergent. Furthermore, Invention II has separate utility such as performing the protected PCI procedure on the patient. See MPEP § 806.05(d).
Since applicant has received an action on the merits for the originally presented invention, this invention has been constructively elected by original presentation for prosecution on the merits. Accordingly, claim 33 is withdrawn from consideration as being directed to a non-elected invention. See 37 CFR 1.142(b) and MPEP § 821.03.
To preserve a right to petition, the reply to this action must distinctly and specifically point out supposed errors in the restriction requirement. Otherwise, the election shall be treated as a final election without traverse. Traversal must be timely. Failure to timely traverse the requirement will result in the loss of right to petition under 37 CFR 1.144. If claims are subsequently added, applicant must indicate which of the subsequently added claims are readable upon the elected invention.
Should applicant traverse on the ground that the inventions are not patentably distinct, applicant should submit evidence or identify such evidence now of record showing the inventions to be obvious variants or clearly admit on the record that this is the case. In either instance, if the examiner finds one of the inventions unpatentable over the prior art, the evidence or admission may be used in a rejection under 35 U.S.C. 103 or pre-AIA 35 U.S.C. 103(a) of the other invention.
Status of Claims
This office action for the 18/332050 application is in response to the communications filed January 16, 2026.
Claims 1, and 31 were amended January 16, 2026.
Claim 32 was cancelled January 16, 2026.
Claim 33 was added as new January 16, 2026.
Claim 33 is hereby withdrawn for the reasons indicated above.
Claims 1, 2, 10, 11, 14, 16-20 and 23-31 are currently pending and considered below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 2, 10, 11, 14, 16-20 and 23-31 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 identifying whether a patient is eligible for a protected percutaneous coronary intervention (PCI), the method comprising: receiving medical information for a patient; extracting one or more features from the received medical information; determining the patient's eligibility for a protected PCI based, at least in part, on the extracted one or more features, to generate a patient classification, wherein the protected PCI is a PCI procedure during which mechanical circulatory support is provided to the patient; and outputting an indication of the patient classification, wherein outputting the indication comprises updating the health record of the patient to include the indication of the patient classification. These steps, as drafted, under the broadest reasonable interpretation are directed to:
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. 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 claim recite a list of rules or instructions that a human person can perform in 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:
“electronic” which corresponds to merely using a computer as a tool to perform an abstract idea. Paragraph [0013] of the as-filed specification describes that the hardware that implements the abstract idea is at a level of 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.
add insignificant extra-solution activity to the abstract idea, see MPEP 2106.05(g), such as:
“from an electronic health record of the patient” 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:
“from an electronic health record of the patient” 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 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:
“wherein the medical information for the patient includes one or more of an … health record, a laboratory report, an electrocardiograph report, and a medical imaging report.” 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.
“electronic” 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:
amount to mere instructions to apply an exception, see MPEP 2106.05(f), such as:
“electronic” which corresponds to merely using a computer as a tool to perform an abstract idea. Paragraph [0013] of the as-filed specification describes that the hardware that implements the abstract idea is at a level of 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.
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:
“determining whether the patient satisfies one or more inclusion criteria and/or one or more exclusion criteria, and wherein determining the patient’s eligibility comprises determining the patient’s eligibility based, at least in part, on whether the patient satisfies the one or more inclusion criteria and/or the one or more exclusion criteria.” 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,
Claim 11 depends from claim 10 and inherits all the limitations of the claim from which it depends. Claim 11 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein determining the patient’s eligibility comprises determining that the patient is not eligible for a protected PCI when the patient does not satisfy at least one of the one or more inclusion criteria and/or when the patient satisfies at least one of the one or more exclusion criteria.” 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 14,
Claim 14 depends from claim 1 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:
“providing as input to at least one model, the extracted one or more features, wherein the patient classification is generated based, at least in part, on an output of the at least one model.” 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 14 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:
“receiving data indicating whether the patient received protected PCI or would have qualified for protected PCI; and updating the at least one model based, at least in part, on a comparison of the received data and the generated patient classification.” 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 14 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:
“wherein the at least one model is configured to output a score, and wherein the patient’s eligibility is determined based, at least in part, on the score output from the at least one model.” 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 18,
Claim 18 depends from claim 17 and inherits all the limitations of the claim from which it depends. Claim 18 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“determining whether the score output from the at least one model is above a threshold value; and classifying the patient as eligible for a protected PCI when it is determined that the score is above the threshold value.” 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 19,
Claim 19 depends from claim 1 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:
“wherein the medical information for the patient is first medical information received at a first time, the method further comprising: receiving second medical information for the patient at a second time after the first time; reclassifying the patient the patient with regard to the patient's eligibility for a protected PCI based, at least in part, on the received second medical information, to generate an updated patient classification; and outputting an indication of the updated patient classification.” 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 20,
Claim 20 depends from claim 19 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:
“wherein receiving the second medical information comprises receiving the second medical information from the … health record of the patient” 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.
“electronic” 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:
amount to mere instructions to apply an exception, see MPEP 2106.05(f), such as:
“electronic” which corresponds to merely using a computer as a tool to perform an abstract idea. Paragraph [0013] of the as-filed specification describes that the hardware that implements the abstract idea is at a level of 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.
“receiving the second medical information via a user interface provided by a computer-implemented system.” 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:
“receiving the second medical information via a user interface provided by a computer-implemented system.” 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:
“receiving the second medical information via a user interface provided by a computer-implemented system.” 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 23,
Claim 23 depends from claim 19 and inherits all the limitations of the claim from which it depends. Claim 23 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“extracting one or more updated features from the second medical information, wherein the reclassifying is performed based, at least in part, on the one or more updated features.” 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 24,
Claim 24 depends from claim 23 and inherits all the limitations of the claim from which it depends. Claim 24 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein extracting one or more features comprises: applying feature-specific extraction logic to the received medical information to extract the one or more features.” 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 25,
Claim 25 depends from claim 24 and inherits all the limitations of the claim from which it depends. Claim 25 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein applying feature-specific extraction logic comprises: selecting a procedure report from the received medical information; searching the selected procedure report for one or more keywords; and extracting a feature value for a particular keyword based on identification of the one or more keywords in the selected procedure report.” 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 26,
Claim 26 depends from claim 25 and inherits all the limitations of the claim from which it depends. Claim 26 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“identifying multiple keywords of the one or more keywords in the selected procedure report; and selecting, based on precedence information associated with the multiple keywords in the feature-specific extraction logic, a particular keyword of the multiple keywords, wherein extracting a feature value for the one or more features comprises extracting the feature value for the particular keyword.” 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 27,
Claim 27 depends from claim 25 and inherits all the limitations of the claim from which it depends. Claim 27 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“determining that the particular keyword is associated with an absolute feature value; and extracting the absolute feature value as the feature value.” 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 28,
Claim 28 depends from claim 25 and inherits all the limitations of the claim from which it depends. Claim 28 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“determining that the particular keyword is associated with a range of values; and extracting the feature value as an average value from the range of values.” 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 29,
Claim 29 depends from claim 25 and inherits all the limitations of the claim from which it depends. Claim 29 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“determining that the particular keyword is associated with text; accessing a lookup table that includes a mapping of words to values; and extracting the feature value based, at least in part, on words in the text associated with the particular keyword and the mapping in the lookup table.” 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 30,
Claim 30 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 30 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“simulating classification of the patient with regard to the patient's eligibility for a protected PCI based, at least in part, on the changed one or more values, to generate a simulated patient classification” 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.
“providing a user interface configured to display values for the one or more features;”, “receiving user input via the user interface to change one or more of the values for the one or more features;” and “displaying, on the user interface, the simulated patient classification.” 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:
“providing a user interface configured to display values for the one or more features;”, “receiving user input via the user interface to change one or more of the values for the one or more features;” and “displaying, on the user interface, the simulated patient classification.” 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:
“providing a user interface configured to display values for the one or more features;”, “receiving user input via the user interface to change one or more of the values for the one or more features;” and “displaying, on the user interface, the simulated patient classification.” 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 31,
Claim 31 is substantially similar to claim 1. Accordingly, claim 31 is rejected for the same reasons as claim 1.
Claim 31 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“A computer-implemented system for”, “at least one hardware computer processor;” and “at least one non-transitory computer readable medium encoded with a plurality of instructions that, when processed by the at least one hardware computer processor perform” 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:
amount to mere instructions to apply an exception, see MPEP 2106.05(f), such as:
“A computer-implemented system for”, “at least one hardware computer processor;” and “at least one non-transitory computer readable medium encoded with a plurality of instructions that, when processed by the at least one hardware computer processor perform” which corresponds to merely using a computer as a tool to perform an abstract idea. Paragraph [0013] of the as-filed specification describes that the hardware that implements the abstract idea is at a level of 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.
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 § 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 2, 10, 11, 14, 16-20, 23, 24 and 30-31 are rejected under 35 U.S.C. 103 as being unpatentable over Itu et al. (US 2021/0251577; herein referred to as Itu) in view of Liu et al. (US 2019/0192753; herein referred to as Liu).
As per claim 1,
Itu teaches a method of identifying whether a patient is eligible for a percutaneous coronary intervention (PCI), the method comprising: receiving, from an electronic health record of a patient that does not have an implanted mechanical circulatory support device, medical information for the patient; extracting one or more features from the received medical information; determining the patient's eligibility for a PCI based, at least in part, on the extracted one or more features, to generate a patient classification; and outputting an indication of the patient classification, wherein outputting the indication comprises updating the electronic health record of the patient to include the indication of the patient classification:
(Paragraphs [0002]-[0005], [0038] and [0060] of Burzotta. The teaching describes embodiments relate to machine-based assistance in percutaneous coronary intervention (PCI). One common complication of catheterization in PCI is periprocedural myocardial infarction (PMI), with a prevalence of 5% to 30%, depending on the evaluation criteria Methods, and computer readable media with instructions are provided for machine-based risk prediction or assistance for peri-procedural myocardial infarction (PMI). A machine-learned model is used to predict risk of PMI and/or recommend courses of action to avoid PMI in percutaneous coronary intervention (PCI). The teaching further describes a method is provided for machine-based risk prediction [classifying the patient] for peri-procedural myocardial infarction. A medical diagnostic imaging system images a patient. The imaging provides first image data [receiving, from an electronic health record of a patient that does not have an implanted mechanical circulatory support device, medical information for the patient] representing an internal cardiac region of the patient. The first image data is from a first mode of data acquisition. Second data for the patient is acquired. The second data is for a second mode of data acquisition different than the first mode. A processor determines a risk [extracting one or more features to generate a patient classification] of the peri-procedural myocardial infarction by input of first data responsive to the first image data and the second data to a machine-learned model. Information based on the risk is displayed [outputting an indication of the patient classification]. Various types of indications (e.g., recommendations) may be provided by the AI-based system. The type of stent to be implanted (e.g., bioresorbable scaffolds (BRS) versus first-generation sirolimus-eluting stents (SES)) may be recommended. BRS have been associated with a higher incidence of PMI, mainly due to a higher abluminal strut surface area. If the risk of PMI is below a threshold, then the BRS is recommended to prevent acute recoil or occlusion, allowing sealing of post-procedural dissections following acute barotrauma, and providing inhibition of in-segment restenosis through efficient drug-elution. In the case of high risk of PMI, BRS may represent a suboptimal solution so SES is recommended. In patients with stable angina, using a PCB (Paclitaxel-Coated Balloon) compared with deployment of a DES (Drug-Eluting Stent) is associated with a significant reduction in the risk of PMI. Where the risk of PMI is above a threshold, then PCB is recommended. For higher risk of PMI, then a high-dose statin pretreatment may be recommended. Antithrombotic and antiplatelet (e.g. clopidogrel, prasugrel) agents may be recommended based on the level of PMI risk. The use of devices which protect against distal embolization may be recommended for higher risk of PMI [eligibility for a protected percutaneous coronary intervention]. FIG. 1 shows the AI-based system for continuous or updated assessment of PMI embedded in a continual learning framework. Although initially based on an existing large database with patient-specific records, the system may be updated, such as to continuously learn, or retrained 15 from new data. The retrained machine-learned model 16 is redeployed for use with other patients. As further examples or samples for different patients are provided, such as due to application of the machine-learned model and observation of complications from PCI, these further examples are used to update the training and corresponding machine-learned model. By updating the training after a result from the model has been made, this establishes an update in the patient-specific records that were used to train the model initially.)
Itu does not explicitly teach a protected PCI, wherein the protected PCI is a PCI procedure during which mechanical circulatory support is provided to the patient.
However, Liu teaches the prediction and determination of risk of a protected percutaneous intervention procedure wherein the protected percutaneous intervention is a percutaneous intervention procedure during which mechanical circulatory support is provided to the patient:
(Paragraphs [0006], [0009] and [0109] of Liu. The teaching describes systems, devices, and methods presented herein determine a heart health index and/or predict patient survival using measurements relating to a patient's health. In some implementations, the measurements are heart parameters related to cardiac function. an intravascular heart pump system is inserted into vasculature of the patient. The heart pump system may be inserted using a minimally invasive procedure. For example, the heart pump system may be inserted via a catheterization through the femoral artery or vein. In some implementations, the heart pump system includes a cannula, a pump inlet, a pump outlet, and a rotor. For example, the intravascular heart pump system may be a percutaneous ventricular assist device [a protected percutaneous intervention], such as the IMPELLA® family of devices (Abiomed, Inc., Danvers Mass.). The method includes predicting a patient outcome, the method comprising: acquiring, during a first time period and from a heart pump system, first data related to time-varying parameters of the heart pump system; extracting a plurality of features from the first data; determining, using a prediction model and based on the plurality of features, a heart health index indicative of the health of the patient's heart; and predicting, based on the heart health index, a patient outcome.)
It would have been obvious to one of ordinary skill in the art before the time of filing to add to the PCI procedure prediction methods of Itu, the protected percutaneous intervention prediction methods of Liu. Paragraph [0021] of Liu teaches that a clinician may use the heart health index to determine quantitatively that a patient's probability of survival is decreasing steadily over the course of several days (or weeks). This determination would allow the clinician to intervene in the patient's care (such as by adjusting the operation parameters of the patient's heart pump) to improve the patient's outlook. One of ordinary skill in the art in possession of Itu would have looked to Liu for this reason and because they are both in the same field of endeavor; percutaneous intervention assessments. One of ordinary skill in the art would have added to the teaching of Itu, the teaching of Liu based on this incentive without yielding unexpected results.
As per claim 2,
The combined teaching of Itu and Liu teaches the limitations of claim 1.
Itu further teaches wherein the medical information for the patient includes one or more of an electronic health record, a laboratory report, an electrocardiograph report, and a medical imaging report:
(Paragraphs [0050]-[0052] of Itu. The teaching describes that intravascular r non-invasive imaging (e.g., x-ray angiography) acquired during the PCI procedure may be used. In act 22, non-image data is accessed. Data from other modes of acquisition for the patient is acquired. The data is extracted automatically and/or manually. The non-image data is from sensors, the computerized patient medical record, manual input, pathology database, laboratory database, and/or other source. The non-image data represents one or more characteristics of the patient, such as family history, medications taken, temperature, body-mass index, and/or other information. For example, genomic (genetic), ECG, blood test (e.g., troponin level), demographic, clinical, measurement, molecular, and/or family history data of the patient are acquired from memory, transform, data mining, and/or manual input.)
As per claim 10,
The combined teaching of Itu and Liu teaches the limitations of claim 1.
Itu further teaches determining whether the patient satisfies one or more inclusion criteria and/or one or more exclusion criteria, and wherein determining the patient’s eligibility comprises determining the patient’s eligibility based, at least in part, on whether the patient satisfies the one or more inclusion criteria and/or the one or more exclusion criteria:
(Paragraph [0010] of Itu. The teaching describes determination and/or display are performed at any one or multiple of different times relative to the PCI. For example, determining and displaying are performed prior to a percutaneous coronary intervention for the patient. As another example, determining the risk is performed multiple times during a percutaneous coronary intervention for the patient, such as with the acquisition of another image during the intervention. Displaying is performed where the risk from one of the times is above a threshold level. In another example, determining is performed multiple times after a percutaneous coronary intervention for the patient, such as each time additional data becomes available. A suggested course of action is displayed where the risk is above a threshold level.)
As per claim 11,
The combined teaching of Itu and Liu teaches the limitations of claim 10.
Itu further teaches wherein determining the patient’s eligibility comprises determining that the patient is not eligible for a protected PCI when the patient does not satisfy at least one of the one or more inclusion criteria and/or when the patient satisfies at least one of the one or more exclusion criteria:
(Paragraph [0010] of Itu. The teaching describes determination and/or display are performed at any one or multiple of different times relative to the PCI. For example, determining and displaying are performed prior to a percutaneous coronary intervention for the patient. As another example, determining the risk is performed multiple times during a percutaneous coronary intervention for the patient, such as with the acquisition of another image during the intervention. Displaying is performed where the risk from one of the times is above a threshold level. In another example, determining is performed multiple times after a percutaneous coronary intervention for the patient, such as each time additional data becomes available. A suggested course of action is displayed where the risk is above a threshold level.)
As per claim 14,
The combined teaching of Itu and Liu teaches the limitations of claim 1.
Itu further teaches wherein determining the patient’s eligibility comprises: providing as input to at least one model, the extracted one or more features, wherein the patient classification is generated based, at least in part, on an output of the at least one model:
(Paragraphs [0037] and [0038] of Itu. The teaching describes a trained model which is stored. The network parameters, such as connections, convolution kernels, weights, or other learned values for the network are stored. The network is stored in memory to be used for application or testing. Once trained, the machine-learned model may be applied to estimate an outcome (e.g., diagnosis, risk for PMI, and/or indication or recommendation of action to take). The many samples in the training data are used to learn to output given an unseen sample of input data for a future or given patient. Although initially based on an existing large database with patient-specific records, the system may be updated, such as to continuously learn, or retrained 15 from new data. The retrained machine-learned model 16 is redeployed for use with other patients. As further examples or samples for different patients are provided, such as due to application of the machine-learned model and observation of complications from PCI, these further examples are used to update the training and corresponding machine-learned model.)
As per claim 16,
The combined teaching of Itu and Liu teaches the limitations of claim 14.
Itu further teaches further comprising: receiving data indicating whether the patient received protected PCI or would have qualified for protected PCI; and updating the at least one model based, at least in part, on a comparison of the received data and the generated patient classification:
(Paragraphs [0037] and [0038] of Itu. The teaching describes a trained model which is stored. The network parameters, such as connections, convolution kernels, weights, or other learned values for the network are stored. The network is stored in memory to be used for application or testing. Once trained, the machine-learned model may be applied to estimate an outcome (e.g., diagnosis, risk for PMI, and/or indication or recommendation of action to take). The many samples in the training data are used to learn to output given an unseen sample of input data for a future or given patient. Although initially based on an existing large database with patient-specific records, the system may be updated, such as to continuously learn, or retrained 15 from new data. The retrained machine-learned model 16 is redeployed for use with other patients. As further examples or samples for different patients are provided, such as due to application of the machine-learned model and observation of complications from PCI, these further examples are used to update the training and corresponding machine-learned model.)
As per claim 17,
The combined teaching of Itu and Liu teaches the limitations of claim 14.
Itu further teaches wherein the at least one model is configured to output a score, and wherein the patient’s eligibility is determined based, at least in part, on the score output from the at least one model:
(Paragraphs [0029], [0037] and [0038] of Itu. The teaching describes a trained model which is stored. The network parameters, such as connections, convolution kernels, weights, or other learned values for the network are stored. The network is stored in memory to be used for application or testing. Once trained, the machine-learned model may be applied to estimate an outcome (e.g., diagnosis, risk for PMI, and/or indication or recommendation of action to take). The many samples in the training data are used to learn to output given an unseen sample of input data for a future or given patient. Although initially based on an existing large database with patient-specific records, the system may be updated, such as to continuously learn, or retrained 15 from new data. The retrained machine-learned model 16 is redeployed for use with other patients. As further examples or samples for different patients are provided, such as due to application of the machine-learned model and observation of complications from PCI, these further examples are used to update the training and corresponding machine-learned model. To assist in PMI prevention in PCI patients, the AI automatically detects imaging and/or non-imaging characteristics and features associated with PMI, provides a risk score for fine grained assessment of PMI risk, provides indications during the PCI procedure.)
As per claim 18,
The combined teaching of Itu and Liu teaches the limitations of claim 17.
Itu further teaches further comprising: determining whether the score output from the at least one model is above a threshold value; and determining the patient’s eligibility for a protected PCI when it is determined that the score is above the threshold value:
(Paragraphs [0029], [0037] and [0038] of Itu. The teaching describes a trained model which is stored. The network parameters, such as connections, convolution kernels, weights, or other learned values for the network are stored. The network is stored in memory to be used for application or testing. Once trained, the machine-learned model may be applied to estimate an outcome (e.g., diagnosis, risk for PMI, and/or indication or recommendation of action to take). The many samples in the training data are used to learn to output given an unseen sample of input data for a future or given patient. Although initially based on an existing large database with patient-specific records, the system may be updated, such as to continuously learn, or retrained 15 from new data. The retrained machine-learned model 16 is redeployed for use with other patients. As further examples or samples for different patients are provided, such as due to application of the machine-learned model and observation of complications from PCI, these further examples are used to update the training and corresponding machine-learned model. To assist in PMI prevention in PCI patients, the AI automatically detects imaging and/or non-imaging characteristics and features associated with PMI, provides a risk score for fine grained assessment of PMI risk, provides indications during the PCI procedure.)
(Paragraph [0010] of Itu. The teaching describes determination and/or display are performed at any one or multiple of different times relative to the PCI. For example, determining and displaying are performed prior to a percutaneous coronary intervention for the patient. As another example, determining the risk is performed multiple times during a percutaneous coronary intervention for the patient, such as with the acquisition of another image during the intervention. Displaying is performed where the risk from one of the times is above a threshold level. In another example, determining is performed multiple times after a percutaneous coronary intervention for the patient, such as each time additional data becomes available. A suggested course of action is displayed where the risk is above a threshold level.)
As per claim 19,
The combined teaching of Itu and Liu teaches the limitations of claim 1.
Itu further teaches wherein the medical information for the patient is first medical information received at a first time, the method further comprising: receiving second medical information for the patient at a second time after the first time; reclassifying the patient the patient with regard to the patient's eligibility for a protected PCI based, at least in part, on the received second medical information, to generate an updated patient classification; and outputting an indication of the updated patient classification:
(Paragraphs [0002]-[0005] and [0060] of Burzotta. The teaching describes embodiments relate to machine-based assistance in percutaneous coronary intervention (PCI). One common complication of catheterization in PCI is periprocedural myocardial infarction (PMI), with a prevalence of 5% to 30%, depending on the evaluation criteria Methods, and computer readable media with instructions are provided for machine-based risk prediction or assistance for peri-procedural myocardial infarction (PMI). A machine-learned model is used to predict risk of PMI and/or recommend courses of action to avoid PMI in percutaneous coronary intervention (PCI). The teaching further describes a method is provided for machine-based risk prediction [classifying the patient] for peri-procedural myocardial infarction. A medical diagnostic imaging system images a patient. The imaging provides first image data [receiving medical information for a patient] representing an internal cardiac region of the patient. The first image data is from a first mode of data acquisition. Second data for the patient is acquired. The second data is for a second mode of data acquisition different than the first mode. A processor determines a risk [extracting one or more features to generate a patient classification] of the peri-procedural myocardial infarction by input of first data responsive to the first image data and the second data to a machine-learned model. Information based on the risk is displayed [outputting an indication of the patient classification]. Various types of indications (e.g., recommendations) may be provided by the AI-based system. The type of stent to be implanted (e.g., bioresorbable scaffolds (BRS) versus first-generation sirolimus-eluting stents (SES)) may be recommended. BRS have been associated with a higher incidence of PMI, mainly due to a higher abluminal strut surface area. If the risk of PMI is below a threshold, then the BRS is recommended to prevent acute recoil or occlusion, allowing sealing of post-procedural dissections following acute barotrauma, and providing inhibition of in-segment restenosis through efficient drug-elution. In the case of high risk of PMI, BRS may represent a suboptimal solution so SES is recommended. In patients with stable angina, using a PCB (Paclitaxel-Coated Balloon) compared with deployment of a DES (Drug-Eluting Stent) is associated with a significant reduction in the risk of PMI. Where the risk of PMI is above a threshold, then PCB is recommended. For higher risk of PMI, then a high-dose statin pretreatment may be recommended. Antithrombotic and antiplatelet (e.g. clopidogrel, prasugrel) agents may be recommended based on the level of PMI risk. The use of devices which protect against distal embolization may be recommended for higher risk of PMI [eligibility for a protected percutaneous coronary intervention].)
(Paragraphs [0029], [0037] and [0038] of Itu. The teaching describes a trained model which is stored. The network parameters, such as connections, convolution kernels, weights, or other learned values for the network are stored. The network is stored in memory to be used for application or testing. Once trained, the machine-learned model may be applied to estimate an outcome (e.g., diagnosis, risk for PMI, and/or indication or recommendation of action to take). The many samples in the training data are used to learn to output given an unseen sample of input data for a future or given patient. Although initially based on an existing large database with patient-specific records, the system may be updated, such as to continuously learn, or retrained 15 from new data. The retrained machine-learned model 16 is redeployed for use with other patients. As further examples or samples for different patients are provided, such as due to application of the machine-learned model and observation of complications from PCI, these further examples are used to update the training and corresponding machine-learned model. To assist in PMI prevention in PCI patients, the AI automatically detects imaging and/or non-imaging characteristics and features associated with PMI, provides a risk score for fine grained assessment of PMI risk, provides indications during the PCI procedure.)
As per claim 20,
The combined teaching of Itu and Liu teaches the limitations of claim 19.
Itu further teaches wherein receiving the second medical information comprises receiving the second medical information from the electronic health record of the patient or receiving the second medical information via a user interface provided by a computer-implemented system:
(Paragraphs [0002]-[0005] and [0060] of Burzotta. The teaching describes embodiments relate to machine-based assistance in percutaneous coronary intervention (PCI). One common complication of catheterization in PCI is periprocedural myocardial infarction (PMI), with a prevalence of 5% to 30%, depending on the evaluation criteria Methods, and computer readable media with instructions are provided for machine-based risk prediction or assistance for peri-procedural myocardial infarction (PMI). A machine-learned model is used to predict risk of PMI and/or recommend courses of action to avoid PMI in percutaneous coronary intervention (PCI). The teaching further describes a method is provided for machine-based risk prediction [classifying the patient] for peri-procedural myocardial infarction. A medical diagnostic imaging system images a patient. The imaging provides first image data [receiving medical information for a patient] representing an internal cardiac region of the patient. The first image data is from a first mode of data acquisition. Second data for the patient is acquired. The second data is for a second mode of data acquisition different than the first mode. A processor determines a risk [extracting one or more features to generate a patient classification] of the peri-procedural myocardial infarction by input of first data responsive to the first image data and the second data to a machine-learned model. Information based on the risk is displayed [outputting an indication of the patient classification]. Various types of indications (e.g., recommendations) may be provided by the AI-based system. The type of stent to be implanted (e.g., bioresorbable scaffolds (BRS) versus first-generation sirolimus-eluting stents (SES)) may be recommended. BRS have been associated with a higher incidence of PMI, mainly due to a higher abluminal strut surface area. If the risk of PMI is below a threshold, then the BRS is recommended to prevent acute recoil or occlusion, allowing sealing of post-procedural dissections following acute barotrauma, and providing inhibition of in-segment restenosis through efficient drug-elution. In the case of high risk of PMI, BRS may represent a suboptimal solution so SES is recommended. In patients with stable angina, using a PCB (Paclitaxel-Coated Balloon) compared with deployment of a DES (Drug-Eluting Stent) is associated with a significant reduction in the risk of PMI. Where the risk of PMI is above a threshold, then PCB is recommended. For higher risk of PMI, then a high-dose statin pretreatment may be recommended. Antithrombotic and antiplatelet (e.g. clopidogrel, prasugrel) agents may be recommended based on the level of PMI risk. The use of devices which protect against distal embolization may be recommended for higher risk of PMI [eligibility for a protected percutaneous coronary intervention].)
(Paragraphs [0029], [0037] and [0038] of Itu. The teaching describes a trained model which is stored. The network parameters, such as connections, convolution kernels, weights, or other learned values for the network are stored. The network is stored in memory to be used for application or testing. Once trained, the machine-learned model may be applied to estimate an outcome (e.g., diagnosis, risk for PMI, and/or indication or recommendation of action to take). The many samples in the training data are used to learn to output given an unseen sample of input data for a future or given patient. Although initially based on an existing large database with patient-specific records, the system may be updated, such as to continuously learn, or retrained 15 from new data. The retrained machine-learned model 16 is redeployed for use with other patients. As further examples or samples for different patients are provided, such as due to application of the machine-learned model and observation of complications from PCI, these further examples are used to update the training and corresponding machine-learned model. To assist in PMI prevention in PCI patients, the AI automatically detects imaging and/or non-imaging characteristics and features associated with PMI, provides a risk score for fine grained assessment of PMI risk, provides indications during the PCI procedure.)
(Paragraphs [0050]-[0052] of Itu. The teaching describes that intravascular r non-invasive imaging (e.g., x-ray angiography) acquired during the PCI procedure may be used. In act 22, non-image data is accessed. Data from other modes of acquisition for the patient is acquired. The data is extracted automatically and/or manually. The non-image data is from sensors, the computerized patient medical record, manual input, pathology database, laboratory database, and/or other source. The non-image data represents one or more characteristics of the patient, such as family history, medications taken, temperature, body-mass index, and/or other information. For example, genomic (genetic), ECG, blood test (e.g., troponin level), demographic, clinical, measurement, molecular, and/or family history data of the patient are acquired from memory, transform, data mining, and/or manual input.)
As per claim 23,
The combined teaching of Itu and Liu teaches the limitations of claim 19.
Itu further teaches further comprising: extracting one or more updated features from the second medical information, wherein the reclassifying is performed based, at least in part, on the one or more updated features.
(Paragraphs [0002]-[0005] and [0060] of Burzotta. The teaching describes embodiments relate to machine-based assistance in percutaneous coronary intervention (PCI). One common complication of catheterization in PCI is periprocedural myocardial infarction (PMI), with a prevalence of 5% to 30%, depending on the evaluation criteria Methods, and computer readable media with instructions are provided for machine-based risk prediction or assistance for peri-procedural myocardial infarction (PMI). A machine-learned model is used to predict risk of PMI and/or recommend courses of action to avoid PMI in percutaneous coronary intervention (PCI). The teaching further describes a method is provided for machine-based risk prediction [classifying the patient] for peri-procedural myocardial infarction. A medical diagnostic imaging system images a patient. The imaging provides first image data [receiving medical information for a patient] representing an internal cardiac region of the patient. The first image data is from a first mode of data acquisition. Second data for the patient is acquired. The second data is for a second mode of data acquisition different than the first mode. A processor determines a risk [extracting one or more features to generate a patient classification] of the peri-procedural myocardial infarction by input of first data responsive to the first image data and the second data to a machine-learned model. Information based on the risk is displayed [outputting an indication of the patient classification]. Various types of indications (e.g., recommendations) may be provided by the AI-based system. The type of stent to be implanted (e.g., bioresorbable scaffolds (BRS) versus first-generation sirolimus-eluting stents (SES)) may be recommended. BRS have been associated with a higher incidence of PMI, mainly due to a higher abluminal strut surface area. If the risk of PMI is below a threshold, then the BRS is recommended to prevent acute recoil or occlusion, allowing sealing of post-procedural dissections following acute barotrauma, and providing inhibition of in-segment restenosis through efficient drug-elution. In the case of high risk of PMI, BRS may represent a suboptimal solution so SES is recommended. In patients with stable angina, using a PCB (Paclitaxel-Coated Balloon) compared with deployment of a DES (Drug-Eluting Stent) is associated with a significant reduction in the risk of PMI. Where the risk of PMI is above a threshold, then PCB is recommended. For higher risk of PMI, then a high-dose statin pretreatment may be recommended. Antithrombotic and antiplatelet (e.g. clopidogrel, prasugrel) agents may be recommended based on the level of PMI risk. The use of devices which protect against distal embolization may be recommended for higher risk of PMI [eligibility for a protected percutaneous coronary intervention].)
(Paragraphs [0029], [0037] and [0038] of Itu. The teaching describes a trained model which is stored. The network parameters, such as connections, convolution kernels, weights, or other learned values for the network are stored. The network is stored in memory to be used for application or testing. Once trained, the machine-learned model may be applied to estimate an outcome (e.g., diagnosis, risk for PMI, and/or indication or recommendation of action to take). The many samples in the training data are used to learn to output given an unseen sample of input data for a future or given patient. Although initially based on an existing large database with patient-specific records, the system may be updated, such as to continuously learn, or retrained 15 from new data. The retrained machine-learned model 16 is redeployed for use with other patients. As further examples or samples for different patients are provided, such as due to application of the machine-learned model and observation of complications from PCI, these further examples are used to update the training and corresponding machine-learned model. To assist in PMI prevention in PCI patients, the AI automatically detects imaging and/or non-imaging characteristics and features associated with PMI, provides a risk score for fine grained assessment of PMI risk, provides indications during the PCI procedure.)
(Paragraphs [0050]-[0052] of Itu. The teaching describes that intravascular r non-invasive imaging (e.g., x-ray angiography) acquired during the PCI procedure may be used. In act 22, non-image data is accessed. Data from other modes of acquisition for the patient is acquired. The data is extracted automatically and/or manually. The non-image data is from sensors, the computerized patient medical record, manual input, pathology database, laboratory database, and/or other source. The non-image data represents one or more characteristics of the patient, such as family history, medications taken, temperature, body-mass index, and/or other information. For example, genomic (genetic), ECG, blood test (e.g., troponin level), demographic, clinical, measurement, molecular, and/or family history data of the patient are acquired from memory, transform, data mining, and/or manual input.)
As per claim 24,
The combined teaching of Itu and Liu teaches the limitations of claim 23.
Itu further teaches wherein extracting one or more features comprises: applying feature-specific extraction logic to the received medical information to extract the one or more features:
(Paragraphs [0002]-[0005] and [0060] of Burzotta. The teaching describes embodiments relate to machine-based assistance in percutaneous coronary intervention (PCI). One common complication of catheterization in PCI is periprocedural myocardial infarction (PMI), with a prevalence of 5% to 30%, depending on the evaluation criteria Methods, and computer readable media with instructions are provided for machine-based risk prediction or assistance for peri-procedural myocardial infarction (PMI). A machine-learned model is used to predict risk of PMI and/or recommend courses of action to avoid PMI in percutaneous coronary intervention (PCI). The teaching further describes a method is provided for machine-based risk prediction [classifying the patient] for peri-procedural myocardial infarction. A medical diagnostic imaging system images a patient. The imaging provides first image data [receiving medical information for a patient] representing an internal cardiac region of the patient. The first image data is from a first mode of data acquisition. Second data for the patient is acquired. The second data is for a second mode of data acquisition different than the first mode. A processor determines a risk [extracting one or more features to generate a patient classification] of the peri-procedural myocardial infarction by input of first data responsive to the first image data and the second data to a machine-learned model. Information based on the risk is displayed [outputting an indication of the patient classification]. Various types of indications (e.g., recommendations) may be provided by the AI-based system. The type of stent to be implanted (e.g., bioresorbable scaffolds (BRS) versus first-generation sirolimus-eluting stents (SES)) may be recommended. BRS have been associated with a higher incidence of PMI, mainly due to a higher abluminal strut surface area. If the risk of PMI is below a threshold, then the BRS is recommended to prevent acute recoil or occlusion, allowing sealing of post-procedural dissections following acute barotrauma, and providing inhibition of in-segment restenosis through efficient drug-elution. In the case of high risk of PMI, BRS may represent a suboptimal solution so SES is recommended. In patients with stable angina, using a PCB (Paclitaxel-Coated Balloon) compared with deployment of a DES (Drug-Eluting Stent) is associated with a significant reduction in the risk of PMI. Where the risk of PMI is above a threshold, then PCB is recommended. For higher risk of PMI, then a high-dose statin pretreatment may be recommended. Antithrombotic and antiplatelet (e.g. clopidogrel, prasugrel) agents may be recommended based on the level of PMI risk. The use of devices which protect against distal embolization may be recommended for higher risk of PMI [eligibility for a protected percutaneous coronary intervention].)
(Paragraphs [0029], [0037] and [0038] of Itu. The teaching describes a trained model which is stored. The network parameters, such as connections, convolution kernels, weights, or other learned values for the network are stored. The network is stored in memory to be used for application or testing. Once trained, the machine-learned model may be applied to estimate an outcome (e.g., diagnosis, risk for PMI, and/or indication or recommendation of action to take). The many samples in the training data are used to learn to output given an unseen sample of input data for a future or given patient. Although initially based on an existing large database with patient-specific records, the system may be updated, such as to continuously learn, or retrained 15 from new data. The retrained machine-learned model 16 is redeployed for use with other patients. As further examples or samples for different patients are provided, such as due to application of the machine-learned model and observation of complications from PCI, these further examples are used to update the training and corresponding machine-learned model. To assist in PMI prevention in PCI patients, the AI automatically detects imaging and/or non-imaging characteristics and features associated with PMI, provides a risk score for fine grained assessment of PMI risk, provides indications during the PCI procedure.)
(Paragraphs [0050]-[0053] of Itu. The teaching describes that intravascular r non-invasive imaging (e.g., x-ray angiography) acquired during the PCI procedure may be used. In act 22, non-image data is accessed. Data from other modes of acquisition for the patient is acquired. The data is extracted automatically and/or manually. The non-image data is from sensors, the computerized patient medical record, manual input, pathology database, laboratory database, and/or other source. The non-image data represents one or more characteristics of the patient, such as family history, medications taken, temperature, body-mass index, and/or other information. For example, genomic (genetic), ECG, blood test (e.g., troponin level), demographic, clinical, measurement, molecular, and/or family history data of the patient are acquired from memory, transform, data mining, and/or manual input. Data may be extracted from a radiology report, such as by using natural language processing (NLP).)
As per claim 30,
The combined teaching of Itu and Liu teaches the limitations of claim 1.
Itu further teaches further comprising: providing a user interface configured to display values for the one or more features; receiving user input via the user interface to change one or more of the values for the one or more features; simulating classification of the patient with regard to the patient's eligibility for a protected PCI based, at least in part, on the changed one or more values, to generate a simulated patient classification; and displaying, on the user interface, the simulated patient classification:
(Paragraph [0065] of Itu. The teaching describes input data, such as the data most important or reflective of the risk as estimated by the machine-learned model, may be output. For example, an image showing anatomy indicative of higher risk is output with an annotation of the value of the risk of PMI. Other information may be displayed with the predicted risk. More than one prediction may be output, such as outcome predictions for different therapies and/or times. Risks resulting from taking different courses of action may be output, such as in a table)
As per claim 31,
Claim 31 is substantially similar to claim 1. Accordingly, claim 31 is rejected for the same reasons as claim 1.
Claims 25-29 are rejected under 35 U.S.C. 103 as being unpatentable over Itu in view of Lucas et al. (US 2020/0126663; herein referred to as Lucas).
As per claim 25,
The combined teaching of Itu and Liu teaches the limitations of claim 24.
Itu does not explicitly teach wherein applying feature-specific extraction logic comprises: selecting a procedure report from the received medical information; searching the selected procedure report for one or more keywords; and extracting a feature value for a particular keyword based on identification of the one or more keywords in the selected procedure report.
However, Lucas teaches applying feature-specific extraction logic comprises: selecting a procedure report from the received medical information; searching the selected procedure report for one or more keywords; and extracting a feature value for a particular keyword based on identification of the one or more keywords in the selected procedure report:
(Paragraphs [0097]0-[0099], [0112] and [0279] and of Lucas. The teaching describes a mobile device may be used to capture a document such as a NGS report, the document including medical information such as sequencing information about a patient. At step 604, an entity linking engine may be used to extract at least some of that information, such as using the techniques described above. At step 606, that information then may be provided to one or more data repositories, such as in a structured format. In addition to this patient-specific data, the system also may process journal articles from medical publications by similarly extracting structured medical information from those sources at step 608 and then adding that structured information to the data repository at step 610 to curate a knowledge database based upon features of each report which are relevant to each database specialization. For example, a knowledge database for cancer treatments may desire to receive new articles which are relevant by filtering newly published articles to find articles which are oncology and treatment focused which may be added into the knowledge database along with key words, phrases, and other indexable features. The new entry may then be reviewed for curation or automatically entered into the knowledge database. Incorporating a combination of machine learning algorithms (MLA) and natural language processing (NLP) algorithms into this process may substantially improve the efficiency of the analysts or replace them altogether. The system may use a combination of text extraction techniques, text cleaning techniques, natural language processing techniques, machine learning algorithms, and medical concept (Entity) identification, normalization, and structuring techniques. The system also maintains and utilizes a continuous collection of training data across clinical use cases (such as diagnoses, therapies, outcomes, genetic markers, etc.) that help to increase both accuracy and reliability of predictions specific to a patient record. The system accelerates a structuring of clinical data in a patient's record. The system may execute subroutines that highlight, suggest, and pre-populate an electronic medical record (“EHR” or “EMR”). The system may provide other formats of structured clinical data, with relevant medical concepts extracted from the text and documents of record.)
It would have been obvious to one of ordinary skill in the art before the time of filing to add to the natural language processing teachings of combined teaching of Itu and Liu, the natural language processing techniques of Lucas. Lucas teaches at paragraph [0112] that the use of NLP in general and their NLP in particular substantially improves the efficiency of extracting useable information from a medical record. One of ordinary skill in the art would have added to the teaching of Itu, the teaching of Lucas based on this incentive without yielding unexpected results.
As per claim 26,
The combined teaching of Itu, Liu and Lucas teaches the limitations of claim 25.
Lucas further teaches further comprising: identifying multiple keywords of the one or more keywords in the selected procedure report; and selecting, based on precedence information associated with the multiple keywords in the feature-specific extraction logic, a particular keyword of the multiple keywords, wherein extracting a feature value for the one or more features comprises extracting the feature value for the particular keyword:
(Paragraphs [0097]0-[0099], [0112] and [0279] and of Lucas. The teaching describes a mobile device may be used to capture a document such as a NGS report, the document including medical information such as sequencing information about a patient. At step 604, an entity linking engine may be used to extract at least some of that information, such as using the techniques described above. At step 606, that information then may be provided to one or more data repositories, such as in a structured format. In addition to this patient-specific data, the system also may process journal articles from medical publications by similarly extracting structured medical information from those sources at step 608 and then adding that structured information to the data repository at step 610 to curate a knowledge database based upon features of each report which are relevant to each database specialization. For example, a knowledge database for cancer treatments may desire to receive new articles which are relevant by filtering newly published articles to find articles which are oncology and treatment focused which may be added into the knowledge database along with key words, phrases, and other indexable features. The new entry may then be reviewed for curation or automatically entered into the knowledge database. Incorporating a combination of machine learning algorithms (MLA) and natural language processing (NLP) algorithms into this process may substantially improve the efficiency of the analysts or replace them altogether. The system may use a combination of text extraction techniques, text cleaning techniques, natural language processing techniques, machine learning algorithms, and medical concept (Entity) identification, normalization, and structuring techniques. The system also maintains and utilizes a continuous collection of training data across clinical use cases (such as diagnoses, therapies, outcomes, genetic markers, etc.) that help to increase both accuracy and reliability of predictions specific to a patient record. The system accelerates a structuring of clinical data in a patient's record. The system may execute subroutines that highlight, suggest, and pre-populate an electronic medical record (“EHR” or “EMR”). The system may provide other formats of structured clinical data, with relevant medical concepts extracted from the text and documents of record.)
As per claim 27,
The combined teaching of Itu, Liu and Lucas teaches the limitations of claim 25.
Lucas further teaches further comprising: determining that the particular keyword is associated with an absolute feature value; and extracting the absolute feature value as the feature value:
(Paragraphs [0097]0-[0099], [0112] and [0279] and of Lucas. The teaching describes a mobile device may be used to capture a document such as a NGS report, the document including medical information such as sequencing information about a patient. At step 604, an entity linking engine may be used to extract at least some of that information, such as using the techniques described above. At step 606, that information then may be provided to one or more data repositories, such as in a structured format. In addition to this patient-specific data, the system also may process journal articles from medical publications by similarly extracting structured medical information from those sources at step 608 and then adding that structured information to the data repository at step 610 to curate a knowledge database based upon features of each report which are relevant to each database specialization. For example, a knowledge database for cancer treatments may desire to receive new articles which are relevant by filtering newly published articles to find articles which are oncology and treatment focused which may be added into the knowledge database along with key words, phrases, and other indexable features. The new entry may then be reviewed for curation or automatically entered into the knowledge database. Incorporating a combination of machine learning algorithms (MLA) and natural language processing (NLP) algorithms into this process may substantially improve the efficiency of the analysts or replace them altogether. The system may use a combination of text extraction techniques, text cleaning techniques, natural language processing techniques, machine learning algorithms, and medical concept (Entity) identification, normalization, and structuring techniques. The system also maintains and utilizes a continuous collection of training data across clinical use cases (such as diagnoses, therapies, outcomes, genetic markers, etc.) that help to increase both accuracy and reliability of predictions specific to a patient record. The system accelerates a structuring of clinical data in a patient's record. The system may execute subroutines that highlight, suggest, and pre-populate an electronic medical record (“EHR” or “EMR”). The system may provide other formats of structured clinical data, with relevant medical concepts extracted from the text and documents of record.)
(Paragraphs [0103]-[0111] of Lucas. The teaching describes that medical data may include numerous fields including, but not limited to, patient demographics, clinical diagnoses, treatments and outcomes, and genetic testing and laboratory information, and each of the fields may also have a plurality of subfields. Text: The entirety of the text (“The patient was given Tylenol 50 mg at 10:35 am.”), Medication: Identifying any medication mentioned in the text (Tylenol). Medications may be brand name or generic name. This field does not include information about the dosage or method of administration, Active Ingredient: Identifying the active ingredients (acetaminophen) of the medication mentioned using a list such as a search table linking drug names to their active ingredients, Dosage & Dosage Units: The dosage (50 mg) associated with the medication mentioned. These are all examples of an absolute value extracted from the analysis.)
As per claim 28,
The combined teaching of Itu, Liu and Lucas teaches the limitations of claim 25.
Lucas further teaches further comprising: determining that the particular keyword is associated with a range of values; and extracting the feature value as an average value from the range of values:
(Paragraphs [0097]0-[0099], [0112] and [0279] and of Lucas. The teaching describes a mobile device may be used to capture a document such as a NGS report, the document including medical information such as sequencing information about a patient. At step 604, an entity linking engine may be used to extract at least some of that information, such as using the techniques described above. At step 606, that information then may be provided to one or more data repositories, such as in a structured format. In addition to this patient-specific data, the system also may process journal articles from medical publications by similarly extracting structured medical information from those sources at step 608 and then adding that structured information to the data repository at step 610 to curate a knowledge database based upon features of each report which are relevant to each database specialization. For example, a knowledge database for cancer treatments may desire to receive new articles which are relevant by filtering newly published articles to find articles which are oncology and treatment focused which may be added into the knowledge database along with key words, phrases, and other indexable features. The new entry may then be reviewed for curation or automatically entered into the knowledge database. Incorporating a combination of machine learning algorithms (MLA) and natural language processing (NLP) algorithms into this process may substantially improve the efficiency of the analysts or replace them altogether. The system may use a combination of text extraction techniques, text cleaning techniques, natural language processing techniques, machine learning algorithms, and medical concept (Entity) identification, normalization, and structuring techniques. The system also maintains and utilizes a continuous collection of training data across clinical use cases (such as diagnoses, therapies, outcomes, genetic markers, etc.) that help to increase both accuracy and reliability of predictions specific to a patient record. The system accelerates a structuring of clinical data in a patient's record. The system may execute subroutines that highlight, suggest, and pre-populate an electronic medical record (“EHR” or “EMR”). The system may provide other formats of structured clinical data, with relevant medical concepts extracted from the text and documents of record.)
(Paragraph [0264] of Lucas. The teaching describes that a cohort report may contain all resulting treatment regimens from the subset of similar patients, the treatments that have a statistically significant incidence, or all treatments which meet at least a minimum threshold of patients. Such threshold may be based off a predetermined number of patients, or may be based upon the number of patients in each of a plurality of treatment regimens, such that only regimens which have a certain percentage of the patients of the whole cohort are included, for example, by summing the number of patients in the treatment regimens and only displaying treatments with at least 5% or higher of the total patients. The lower (5%) and upper bounds of this threshold may be determined based off of the number of regimens included in the report and their incidence rate as well. In another example, if several regimens have patients by the tens or hundreds, a regimen with only five patients may be excluded from display. The values may use the average, the mean, the average of the mean, or other calculations to identify where the lower and upper threshold cutoffs should be placed. An exemplary numerical description may quantify the number of patients who received the treatment, the number of patients who responded favorably to the treatment, the number of patients who had no change from the treatment, and/or the number of patients who responded unfavorably to the treatment, identified as “Complete Response (CD), Partial Response (PR), Stable Disease (SD), Progressive Disease (PD)”, respectively. In an exemplary visual representation, a color coded graph 186 may be provided to the user to visually represent the same features as the numerical description outlined above. For example, patients who responded favorably may be color coded green and given a distribution of the graphical representation directly proportional to the percentage and patients who responded unfavorably may be color coded red and given a distribution of the graphical representation directly proportional to the percentage)
As per claim 29,
The combined teaching of Itu, Liu and Lucas teaches the limitations of claim 25.
Lucas further teaches further comprising: determining that the particular keyword is associated with text; accessing a lookup table that includes a mapping of words to values; and extracting the feature value based, at least in part, on words in the text associated with the particular keyword and the mapping in the lookup table:
(Paragraphs [0097]0-[0099], [0112] and [0279] and of Lucas. The teaching describes a mobile device may be used to capture a document such as a NGS report, the document including medical information such as sequencing information about a patient. At step 604, an entity linking engine may be used to extract at least some of that information, such as using the techniques described above. At step 606, that information then may be provided to one or more data repositories, such as in a structured format. In addition to this patient-specific data, the system also may process journal articles from medical publications by similarly extracting structured medical information from those sources at step 608 and then adding that structured information to the data repository at step 610 to curate a knowledge database based upon features of each report which are relevant to each database specialization. For example, a knowledge database for cancer treatments may desire to receive new articles which are relevant by filtering newly published articles to find articles which are oncology and treatment focused which may be added into the knowledge database along with key words, phrases, and other indexable features. The new entry may then be reviewed for curation or automatically entered into the knowledge database. Incorporating a combination of machine learning algorithms (MLA) and natural language processing (NLP) algorithms into this process may substantially improve the efficiency of the analysts or replace them altogether. The system may use a combination of text extraction techniques, text cleaning techniques, natural language processing techniques, machine learning algorithms, and medical concept (Entity) identification, normalization, and structuring techniques. The system also maintains and utilizes a continuous collection of training data across clinical use cases (such as diagnoses, therapies, outcomes, genetic markers, etc.) that help to increase both accuracy and reliability of predictions specific to a patient record. The system accelerates a structuring of clinical data in a patient's record. The system may execute subroutines that highlight, suggest, and pre-populate an electronic medical record (“EHR” or “EMR”). The system may provide other formats of structured clinical data, with relevant medical concepts extracted from the text and documents of record.)
(Paragraphs [0103]-[0111] of Lucas. The teaching describes that medical data may include numerous fields including, but not limited to, patient demographics, clinical diagnoses, treatments and outcomes, and genetic testing and laboratory information, and each of the fields may also have a plurality of subfields. Text: The entirety of the text (“The patient was given Tylenol 50 mg at 10:35 am.”), Medication: Identifying any medication mentioned in the text (Tylenol). Medications may be brand name or generic name. This field does not include information about the dosage or method of administration, Active Ingredient: Identifying the active ingredients (acetaminophen) of the medication mentioned using a list such as a search table linking drug names to their active ingredients. This search table is construed as a look up table to map words to values [the medication of “Tylenol” is mapped to the active ingredient value determination of “acetaminophen” in the search table].)
Response to Arguments
Applicant's arguments filed January 16, 2026 have been fully considered.
Applicant arguments pertaining to rejections made under 35 U.S.C. 101 are not persuasive.
The Applicant argues that claim 1 recites limitations that demonstrate an improvement to a technology other than a computer. Specifically, the limitations of "receiving, from an electronic health record of the patient that does not have an implanted mechanical circulatory support device, medical information for the patient", "extracting one or more features from the received medical information," and "outputting an indication of the patient classification, wherein outputting the indication comprises updating the electronic health record of the patient to include the indication of the patient classification" achieve this improvement to technology. The Applicant further articulates that the technical improvement relates to an improved electronic health record system. When the claim is considered as a whole, it integrates any alleged abstract idea into a practical application.
The Examiner respectfully disagrees. The latter to limitations of what is argued are elements that are included in the abstract idea. Only additional elements to an abstract idea can qualify to be an additional element that can provide for a practical application. If such an improvement exists within these two limitations, the improvement would exclusively be abstract. With regard to the first argued limitation, such functions are well-known, routine and conventional as is shown in the rejection above. Accordingly, these limitations cannot provide for an improvement to technology. However, if more technical detail were claimed to describe HOW data were received, HOW data was being extracted, or HOW data was being outputted, such details might lend itself to be eligible if an improvement were actually present. Currently, the functions are claimed at such a high level of generality, no definitive improvement can be identified by the Examiner.
Applicant’s arguments pertaining to rejections made under 35 U.S.C. 103 are not persuasive.
The Applicant argues that the combined teaching of Itu and Liu does not teach “receiving, from an electronic health record of a patient that does not have an implanted mechanical circulatory support device, medical information for the patient”.
The Examiner respectfully disagrees. Liu is not relied upon to teach this limitation. Accordingly, the arguments against Liu are not relevant. As for Itu’s applicability to this limitation, Itu does not include any sort of “implanted mechanical circulatory support device” when receiving the cited medical information. Accordingly, this information is construed to be collected from a patient that does not have an implanted mechanical circulatory support device. To say that Itu does not teach this limitation, it would necessarily require a implanted mechanical circulatory support device in the patient at the time of receiving the health record to be recited in the reference. Such a feature is absent from the reference.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHAD A NEWTON whose telephone number is (313)446-6604. The examiner can normally be reached M-F 8:00AM-4:00PM (EST).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PETER H. CHOI can be reached on (469) 295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/CHAD A NEWTON/Primary Examiner, Art Unit 3681