DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is a non-final rejection
Claims 1-20 are pending
Claims 21-62 were cancelled
Claims 1-5, 12-20 are rejected under 35 USC § 112
Claims 1-20 are rejected under 35 USC § 101
Claims 1-20 are rejected under 35 USC § 103
Priority
Acknowledgement is made of Applicant’s claim for a domestic priority date of 5-2-2023
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claims 1, 3, 5, 12, 15, 17 meet the three-prong test and therefore invokes § 112(f). The words of the claim do not rebut the presumption that the claim limitation is to be interpreted under § 112(f).
Regarding claim 1
Second limitation: The Examiner interprets “data integration means for seamless integration of the sepsis screening tool with electronic health records (EHRs) and patient monitoring devices” as invoking 35 U.S.C. §112(f). However, there is no disclosure of corresponding structure that performs the claimed function.
Fifth limitation: The Examiner interprets “feedback means for providing healthcare professionals with information related to sepsis risk and patient progress” as invoking 35 U.S.C. §112(f). However, there is no disclosure of corresponding structure that performs the claimed function.
Regarding claim 3
The Examiner interprets “wherein the data integration means accommodates various data formats and communication protocols to ensure compatibility with existing and future healthcare information systems” as invoking 35 U.S.C. §112(f). However, there is no disclosure of corresponding structure that performs the claimed function.
Regarding claim 5
The Examiner interprets “wherein the feedback means includes visual representations of patient screening statuses and alerts or notifications related to sepsis risk” as invoking 35 U.S.C. §112(f). However, there is no disclosure of corresponding structure that performs the claimed function.
Regarding claim 12
The Examiner interprets “further comprising means for incorporating additional data sources, including but not limited to patient wearables, medical imaging, or genomics data, to improve the accuracy and adaptability of the AI model” as invoking 35 U.S.C. §112(f). However, there is no disclosure of corresponding structure that performs the claimed function.
Regarding claim 15
The Examiner interprets “further comprising means for integration with telemedicine platforms to enable remote monitoring and consultations” as invoking 35 U.S.C. §112(f). However, there is no disclosure of corresponding structure that performs the claimed function.
Regarding claim 17
The Examiner interprets “further comprising means for incorporating clinical explainability to the AI model to provide transparency and understanding of the model's predictions to healthcare professionals” as invoking 35 U.S.C. §112(f).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Independent claim 1 and its dependent claims 2-5, 12-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Independent claim 1 and its dependent claims 2-5, 12-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The second limitation of claim 1 “data integration means for seamless integration of the sepsis screening tool with electronic health records (EHRs) and patient monitoring devices” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function since the disclosure is devoid of any structure that performs the function in the claim. The Examiner notes that it is nevertheless not clear how the structure performs the recited function of the second limitation of claim 15. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Furthermore the disclosure does not provide an algorithm or sufficient details of how the computer or computer component performs the claimed function. For computer-implemented functional claims, the determination of the sufficiency of the disclosure under § 112(a) requires an inquiry into whether the specification provides a disclosure of the computer and algorithm that achieve the claimed function in sufficient detail that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed subject matter at the time of filing. See MPEP § 2161.01(I) and 2181(II)(B). Therefore Independent claim 1 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
The fifth limitation of claim 1 “feedback means for providing healthcare professionals with information related to sepsis risk and patient progress” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function since the disclosure is devoid of any structure that performs the function in the claim. The Examiner notes that it is nevertheless not clear how the structure performs the recited function of the fifth limitation of claim 1. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Furthermore the disclosure does not provide an algorithm or sufficient details of how the computer or computer component performs the claimed function. For computer-implemented functional claims, the determination of the sufficiency of the disclosure under § 112(a) requires an inquiry into whether the specification provides a disclosure of the computer and algorithm that achieve the claimed function in sufficient detail that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed subject matter at the time of filing. See MPEP § 2161.01(I) and 2181(II)(B). Therefore Independent claim 1 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
claim 3 “wherein the data integration means accommodates various data formats and communication protocols to ensure compatibility with existing and future healthcare information systems” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function since the disclosure is devoid of any structure that performs the function in the claim. The Examiner notes that it is nevertheless not clear how the structure performs the recited function of claim 3. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Furthermore the disclosure does not provide an algorithm or sufficient details of how the computer or computer component performs the claimed function. For computer-implemented functional claims, the determination of the sufficiency of the disclosure under § 112(a) requires an inquiry into whether the specification provides a disclosure of the computer and algorithm that achieve the claimed function in sufficient detail that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed subject matter at the time of filing. See MPEP § 2161.01(I) and 2181(II)(B). Therefore dependent claim 3 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
claim 5 “wherein the feedback means includes visual representations of patient screening statuses and alerts or notifications related to sepsis risk” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function since the disclosure is devoid of any structure that performs the function in the claim. The Examiner notes that it is nevertheless not clear how the structure performs the recited function of claim 5. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Furthermore the disclosure does not provide an algorithm or sufficient details of how the computer or computer component performs the claimed function. For computer-implemented functional claims, the determination of the sufficiency of the disclosure under § 112(a) requires an inquiry into whether the specification provides a disclosure of the computer and algorithm that achieve the claimed function in sufficient detail that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed subject matter at the time of filing. See MPEP § 2161.01(I) and 2181(II)(B). Therefore dependent claim 5 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
claim 12 “further comprising means for incorporating additional data sources, including but not limited to patient wearables, medical imaging, or genomics data, to improve the accuracy and adaptability of the AI model” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function since the disclosure is devoid of any structure that performs the function in the claim. The Examiner notes that it is nevertheless not clear how the structure performs the recited function of claim 12. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Furthermore the disclosure does not provide an algorithm or sufficient details of how the computer or computer component performs the claimed function. For computer-implemented functional claims, the determination of the sufficiency of the disclosure under § 112(a) requires an inquiry into whether the specification provides a disclosure of the computer and algorithm that achieve the claimed function in sufficient detail that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed subject matter at the time of filing. See MPEP § 2161.01(I) and 2181(II)(B). Therefore dependent claim 12 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
claim 15 “further comprising means for integration with telemedicine platforms to enable remote monitoring and consultations” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function since the disclosure is devoid of any structure that performs the function in the claim. The Examiner notes that it is nevertheless not clear how the structure performs the recited function of claim 15. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Furthermore the disclosure does not provide an algorithm or sufficient details of how the computer or computer component performs the claimed function. For computer-implemented functional claims, the determination of the sufficiency of the disclosure under § 112(a) requires an inquiry into whether the specification provides a disclosure of the computer and algorithm that achieve the claimed function in sufficient detail that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed subject matter at the time of filing. See MPEP § 2161.01(I) and 2181(II)(B). Therefore dependent claim 15 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
further comprising means for integration with telemedicine platforms to enable remote monitoring and consultations
claim 17 “further comprising means for incorporating clinical explainability to the AI model to provide transparency and understanding of the model's predictions to healthcare professionals” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function since the disclosure is devoid of any structure that performs the function in the claim. The Examiner notes that it is nevertheless not clear how the structure performs the recited function of claim 17. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Furthermore the disclosure does not provide an algorithm or sufficient details of how the computer or computer component performs the claimed function. For computer-implemented functional claims, the determination of the sufficiency of the disclosure under § 112(a) requires an inquiry into whether the specification provides a disclosure of the computer and algorithm that achieve the claimed function in sufficient detail that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed subject matter at the time of filing. See MPEP § 2161.01(I) and 2181(II)(B). Therefore dependent claim 17 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
The Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Dependent claims 2 and 7 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 2 and 7, the phrase "such as" renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are not patent eligible because the claimed invention is directed to an abstract idea without significantly more.
Analysis
First, claims are directed to one or more of the following statutory categories: a process, a machine, a manufacture, and a composition of matter. Regarding claims 1-20 the claims recite an abstract idea of “sepsis detection”.
Independent Claims 1, 6 and 11 are rejected under 35 U.S.C 101 based on the following analysis.
-Step 1 (Does the claim fall within a statutory category? YES): claim 1 recites a system, for sepsis detection.
-Step 2A Prong One (Does the claim fall within at least one of the groupings of abstract ideas?: YES): The claimed invention:
sepsis detection... comprising: ;
a. a customized sepsis screening tool for assessing patients for sepsis, adaptable for different patient populations and specific disease conditions;
b. data integration means for seamless integration of the sepsis screening tool with .. health records and patient monitoring;
c. an model, which processes data from the sepsis screening tool, health records, and patient monitoring ..to generate alerts for clinicians when it suspects a screening needs to be reevaluated;
a granular visual input based layer that allows the healthcare professional to indicate why the ... output is accurate or inaccurate through a series of pre-formatted queries;
d. feedback means for providing healthcare professionals with information related to sepsis risk and patient progress;
belong to the grouping of mental processes under concepts performed in the human mind (including an observation, evaluation, judgement, opinion) as it recites “sepsis detection”. Alternatively the claims belong to certain methods of organizing human activity under managing personal behavior or relationships or interrelations between people as it recites “sepsis detection”. The justification for the latter is that the claim invention is a system that is used to detect sepsis in a patient. Accordingly this claim recites an abstract idea.
-Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO).
Claim 1 recites:
sepsis detection using reinforcement learning from human feedback (RLHF),;
electronic health records (EHRs);
patient monitoring devices;
an artificial intelligence (AI) model, trained using RLHF;
Al tool's output;
Amounting to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, these additional elements, when considered separately and as an ordered combination do not integrate the judicial exception/abstract idea into a “practical application” of the judicial exception because they do not impose any meaningful limit on practicing the judicial exception. Support for this can be found in the specification, paragraphs (0007-0010).
-Step 2B (Does the additional elements of the claim provide an inventive concept?: NO. As discussed previously with respect to Step 2A Prong Two, claim 1 recites:
Claim 1 recites:
sepsis detection using reinforcement learning from human feedback (RLHF),;
electronic health records (EHRs);
patient monitoring devices;
an artificial intelligence (AI) model, trained using RLHF;
Al tool's output;
Amounting to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)) Accordingly, even when viewed as a whole the claim does not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible.
-Step 1 (Does the claim fall within a statutory category? YES): claims 6 recites a method for sepsis detection.
-Step 2A Prong One (Does the claim fall within at least one of the groupings of abstract ideas?: YES): The claimed invention:
sepsis detection, comprising: ;
a. assessing patients for sepsis using a customized sepsis screening tool;
integrating data from the sepsis screening tool, .. health records, and patient monitoring;
c. processing the integrated data using an ..model;
d. generating alerts for clinicians when the model suspects a screening needs to be reevaluated;
e. providing healthcare professionals with feedback related to sepsis risk and patient progress;
belong to the grouping of mental processes under concepts performed in the human mind (including an observation, evaluation, judgement, opinion) as it recites “sepsis detection”. Alternatively the claims belong to certain methods of organizing human activity under managing personal behavior or relationships or interrelations between people as it recites “sepsis detection”. The justification for the latter is that the claim invention is a system that is used to detect sepsis in a patient. Accordingly this claim recites an abstract idea.
-Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO).
Claims 6, 11 recite:
sepsis detection using reinforcement learning from human feedback (RLHF),;
electronic health records (EHRs);
patient monitoring devices;
artificial intelligence (AI) model continuously training with RLHF;
Claim 11 recites:
A computer-readable medium containing instructions for sepsis detection using reinforcement learning from human feedback (RLHF), which when executed by a processor, cause the processor to perform the steps.
Amounting to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, these additional elements, when considered separately and as an ordered combination do not integrate the judicial exception/abstract idea into a “practical application” of the judicial exception because they do not impose any meaningful limit on practicing the judicial exception. Support for this can be found in the specification, paragraphs (0007-0010).
-Step 2B (Does the additional elements of the claim provide an inventive concept?: NO. As discussed previously with respect to Step 2A Prong Two, claim 1 recites:
Claims 6, 11 recite:
sepsis detection using reinforcement learning from human feedback (RLHF),;
electronic health records (EHRs);
patient monitoring devices;
artificial intelligence (AI) model continuously training with RLHF;
Claim 11 recites:
A computer-readable medium containing instructions for sepsis detection using reinforcement learning from human feedback (RLHF), which when executed by a processor, cause the processor to perform the steps;
Amounting to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)) Accordingly, even when viewed as a whole the claim does not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible.
-Step 1 (Does the claim fall within a statutory category? NO): Regarding claim 11, it is not patent eligible because the claimed invention is directed to non-statutory subject matter. The claim(s) does not fall within at least one of the four categories of patent eligible subject matter because the claim(s) is directed to a signal per se. MPEP 2106.03 recites in pertinent part: “Non-limiting examples of claims that are not directed to any of the statutory categories include: Transitory forms of signal transmission (often referred to as “signals per se”), such as a propagating electrical or electromagnetic signal or carrier wave”
Dependent Claims:
Step 2A Prong One: The following dependent claims recites additional limitations that further define the abstract idea of “sepsis detection”. The claim limitations include:
Claim 2 & 7: wherein the customized sepsis screening tool accounts for various factors such as age, medical history, and demographics;
Claim 3 & 8: wherein the data integration means/step accommodates various data formats and communication protocols to ensure compatibility with existing and future healthcare information systems;
Claim 4 & 9: wherein the model uses new screening outcomes as feedback to learn and improve its sepsis detection capabilities;
Claim 5 & 10: wherein the feedback means/provided to health care professionals, includes visual representations of patient screening statuses and alerts or notifications related to sepsis risk;
Claim 12: further comprising means for incorporating additional data sources, including but not limited to patient wearables, medical imaging, or genomics data, to improve the accuracy and adaptability of the model;
Claim 13: further comprising an improved experience, including advanced visualization techniques, voice commands, or remote access and monitoring;
Claim 14: wherein the model is further capable of recommending appropriate treatment options based on the patient's condition, medical history, and individual characteristics;
Claim 15: further comprising means for integration with telemedicine platforms to enable remote monitoring and consultations;
Claim 16: wherein the model provides real-time predictions and alerts to healthcare professionals as new data becomes available;
Claim 17: further comprising means for incorporating clinical explainability to the model to provide transparency and understanding of the model's predictions to healthcare professionals;
Claim 18: wherein the model identifies patient- specific risk factors and tailors the sepsis screening tool to each patient's unique needs;
Claim 19: wherein the system employs remote virtual nurses to conduct proactive sepsis screenings for all patients in an inpatient unit;
Claim 20: wherein the remote virtual nurses use the patented sepsis screening tool and real-time patient data to assess sepsis risk and adapt the screening process based on individual patient characteristics and the evolving nature of their condition.
Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO). The following dependent claims recite mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, the claims as a whole do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims include:
Claim 12: AI model;
Claim 13:
user interface;
mobile applications .
Claim 14: AI model;
Claim 16: AI model;
Claim 17: AI model;
Claim 18: AI model.
Step 2B (Does the additional elements of the claim provide an inventive concept?: NO). As discussed previously with respect to Step 2A Prong Two, the following dependent claims recite mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, the claim does not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible. The claims include:
Claim 12: AI model;
Claim 13:
user interface;
mobile applications .
Claim 14: AI model;
Claim 16: AI model;
Claim 17: AI model;
Claim 18: AI model;
Claim Rejections - 35 USC § 103
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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
non-obviousness.
Claims 1-13, 15-16, 18 are rejected under 35 U.S.C. 103 as being un-patentable by Selvaraj et.al. (WO 2022216220 A1) hereinafter “Selvaraj” in view of Pedersen et.al (US 20240203006 A1) hereinafter “Pedersen”.
Regarding claim 1 Selvaraj teaches:
a. a customized sepsis screening tool (plurality of sepsis prediction model) for assessing patients for sepsis (monitored patient to detect infection and sepsis), adaptable for different patient populations (identifying the sepsis prediction model with the training cohort most similar to the monitored patient (i.e. “like patients”)) and specific disease conditions (selecting the most similar sepsis prediction model 140 in response to a change in the patient health data); (See at least [0014] via: “ ... there is provided a computational method for detection of infection and sepsis..”; in addition see at least [0042] via: “ ... The system and method are configured to generate a plurality of sepsis prediction models and a general population sepsis prediction model each trained using the stored patient health data 120... Generating each of the plurality of sepsis prediction models may comprise identifying a training cohort of similar patients according to a patient similarity measure 122 and then training a sepsis prediction model using the training cohort of similar patients 124...”; in addition see at least [0043] via: “ ... The system is configured to select a sepsis prediction model from the plurality of sepsis prediction models for monitoring the monitored patient. This is performed by identifying the sepsis prediction model with the training cohort most similar to the monitored patient (i.e. “like patients”), and if no similar training cohort can be identified then selecting the general population sepsis prediction model 140...”; in addition see at least [0044] via: “ ... The selected sepsis prediction model is then used to monitor the monitored patient to detect infection and sepsis events 150, for example by processing new/updates to patient health data. This may be used to generate electronic alerts if an infection and sepsis event is detected 152. The system may also repeat the step of selecting the most similar sepsis prediction model 140 in response to a change in the patient health data of the monitored patient over time 154. This allows the system to keep using the most similar (and arguably relevant) patient cohort as the patient’s measurements and symptoms change, for example as the monitored patient begins to show signs of an infection or sepsis...”)
b. data integration means for seamless integration of the sepsis screening tool with electronic health records (EHRs) (electronic medical records) and patient monitoring devices (clinical data obtained from ... one or more wearable); (See at least [0076] via: “...The Input/Output Interface may comprise a network interface and/or communications module for communicating with an equivalent communications module in another device using a predefined communications protocol (e.g. IEEE 802.11, IEEE 802.15, 4G/5G, TCP/IP, UDP, etc.)...”; in addition see at least [0047] via: “...A clinician interface 40 is also provided to interface with the one or more clinical data sources such as electronic medical records and a clinician user interface..”; in addition see at least [0043] via: “... the patient health data may comprise a plurality of clinical data obtained from one or more clinical data sources, a plurality of patient measurement data obtained from one or more wearable, home, and community based biomedical sensors such as wearable sensors and/or vital sign sensors (including non-invasive and invasive vital sign sensors), ... Updates may also be obtained from clinical data sources, such as laboratory test results, treatments and clinician notes....”)
c. an artificial intelligence (AI) model (AI-based models), , which processes data from the sepsis screening tool (sepsis prediction model), EHRs (electronic medical records), and patient monitoring devices (wearable biomedical sensors) to generate alerts for clinicians when it suspects a screening needs to be reevaluated (generate an alert if an infection and sepsis event is detected) (See at least [0042] via: “... The system and method are configured to generate a plurality of sepsis prediction models and a general population sepsis prediction model each trained using the stored patient health data 120. These models may be machine learning or AI-based models, such as classifier models, and may be stored in a model store 20, such as a database or file store that electronically stores the relevant model parameters and configuration (for example by exporting a trained model) to allow later use of the stored model. Generating each of the plurality of sepsis prediction models may comprise identifying a training cohort of similar patients according to a patient similarity measure 122 and then training a sepsis prediction model using the training cohort of similar patients 124...”; in addition see at least [0047] via: “...A clinician interface 40 is also provided to interface with the one or more clinical data sources such as electronic medical records and a clinician user interface..”; in addition see at least [0043] via: “...the patient health data may comprise a plurality of clinical data obtained from one or more clinical data sources, a plurality of patient measurement data obtained from one or more wearable, home, and community based biomedical sensors such as wearable sensors and/or vital sign sensors (including non-invasive and invasive vital sign sensors), ... Updates may also be obtained from clinical data sources, such as laboratory test results, treatments and clinician notes... The system is configured to select a sepsis prediction model from the plurality of sepsis prediction models for monitoring the monitored patient. This is performed by identifying the sepsis prediction model with the training cohort most similar to the monitored patient (i.e. “like patients”), and if no similar training cohort can be identified then selecting the general population sepsis prediction model 140...”; in addition see at least [0044] via: “...The selected sepsis prediction model is then used to monitor the monitored patient to detect infection and sepsis events 150, for example by processing new/updates to patient health data...”; in addition see at least [0019] via: “...each sepsis prediction model may be a machine learning classifier which is configured to monitor updates to patient health data for the monitored patient and generate an alert if an infection and sepsis event is detected...”)
d. a granular visual input (visualize the patient’s health data.. images) based layer that allows the healthcare professional to indicate why the Al tool's output is accurate or inaccurate (feedback regarding whether the generated alerts are true positives or potentially false alarms 48) through a series of pre-formatted queries (See at least [0047] via: “...A clinician interface 40 is also provided to interface with the one or more clinical data sources such as electronic medical records and a clinician user interface. The clinician user interface also allows the clinician (including doctors, surgeons, medical specialists and other health care professionals and service providers) to access or visualize the patient’s health data and trends from a set of dashboards or summary pages or graphic illustrations on mobile application or website portals 42. ... Laboratory (lab) test reports, images and documents may also be viewed or imported, uploaded or access granted 44. ..., the clinician is also able to review push notifications of alerts on sepsis detector 50 outputs and wearable sensor notifications and provide clinical feedback regarding whether the generated alerts are true positives or potentially false alarms 48. ..”)
e. feedback (Alerts 59 may be sent) means for providing healthcare professionals (caregiver... health care provider) with information related to sepsis risk (alert them to a potentially serious infection) and patient progress (health trend data) (See at least [0051] via: “..Alerts 59 may be sent to the patient or their caregiver via the patient user interface 34, for example to alert them to a potentially serious infection or deterioration event. Alerts 59 may also be sent to the clinician interface to notify the clinician, health care provider and associated parties and displayed in the clinician user interface, for example on a mobile application and or web portal. Additional data such as health trend data may be included with the alert. The clinician can review the generated positive alerts 59, the corresponding health trend data, and can verify the validity of the generated alerts and provide a feedback in annotating the infection and sepsis events to be true positives or false positives 48. In case of a new clinical event, the clinician interface allows the clinician to make entries of clinical events including severe adverse events and changes in medications. The clinician’s feedback for the generated infection and sepsis events or the new entries of clinical events are pushed and updated as the corresponding reference data for the given patient’s health information, measurements and symptoms 54...”)
However, Selvaraj is silent regarding training the artificial model with RLHF as taught by Pedersen
trained using RLHF the (See at least [0063] via: “...one or more parameters of the models 120 and/or 140 can be updated using reinforcement learning with Human Feedback (RLHF) data. The RLHF data can include user feedback data, and other user data. The user feedback data can include acceptance or rejection of suggestions proposed by the model...”; in addition see at least [0065] via: “..The training of models 120 and/or 140 can include obtaining a training instance having user data and reinforcement learning from human feedback (RLHF) data. A set of training data can include user data and RLHF data...”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Selvaraj to incorporate the teachings of Pedersen. Those in the art would have recognized that Selvaraj’s teaching regarding a method for early detection of sepsis and infection in patients using personalized detection models. could be modified to include Pedersen’s’s teaching regarding a set of training data that includes RLHF data. This combination would be beneficial to medical personnel by training the sepsis prediction models of Selvaraj with training data that includes RLHF data as taught by Pedersen since it guides AI models to generate responses that humans find preferable, making the AI's outputs more useful and aligned with human values, especially in complex tasks without single correct answers, like those that may involve detecting and reviewing the sepsis condition of a patient
Regarding claims 6 & 11 Selvaraj teaches:
a. assessing patients for sepsis using a customized sepsis screening tool; (See at least [0014] via: “ ... there is provided a computational method for detection of infection and sepsis..”; in addition see at least [0042] via: “ ... The system and method are configured to generate a plurality of sepsis prediction models and a general population sepsis prediction model each trained using the stored patient health data 120... Generating each of the plurality of sepsis prediction models may comprise identifying a training cohort of similar patients according to a patient similarity measure 122 and then training a sepsis prediction model using the training cohort of similar patients 124...”; in addition see at least [0043] via: “ ... The system is configured to select a sepsis prediction model from the plurality of sepsis prediction models for monitoring the monitored patient. This is performed by identifying the sepsis prediction model with the training cohort most similar to the monitored patient (i.e. “like patients”), and if no similar training cohort can be identified then selecting the general population sepsis prediction model 140...”; in addition see at least [0044] via: “ ... The selected sepsis prediction model is then used to monitor the monitored patient to detect infection and sepsis events 150, for example by processing new/updates to patient health data. This may be used to generate electronic alerts if an infection and sepsis event is detected 152. The system may also repeat the step of selecting the most similar sepsis prediction model 140 in response to a change in the patient health data of the monitored patient over time 154. This allows the system to keep using the most similar (and arguably relevant) patient cohort as the patient’s measurements and symptoms change, for example as the monitored patient begins to show signs of an infection or sepsis...”)
b. integrating data from the sepsis screening tool, electronic health records (EHRs), and patient monitoring devices; (See at least [0043] via: “... monitor patients in order to detect infection and predict sepsis development well in advance. .. The system is configured to obtain patient health data for the monitored patient 140. As outlined above the patient health data may comprise a plurality of clinical data obtained from one or more clinical data sources, a plurality of patient measurement data obtained from one or more wearable, home, and community based biomedical sensors such as wearable sensors and/or vital sign sensors (including non-invasive and invasive vital sign sensors), and a plurality of symptoms obtained from the patient. When the patient is first monitored, patient health data may be captured or imported from electronic health records and clinical record systems, or access may be provided to the electronic health records or systems. Moving forward, the system can continuously monitor the patient collecting regular or ad-hoc patient health data from wearable and home/clinic based vital sign sensors, as well as symptoms. Updates may also be obtained from clinical data sources, such as laboratory test results, treatments and clinician notes. The system is configured to select a sepsis prediction model from the plurality of sepsis prediction models for monitoring the monitored patient. This is performed by identifying the sepsis prediction model with the training cohort most similar to the monitored patient (i.e. “like patients”), and if no similar training cohort can be identified then selecting the general population sepsis prediction model 140...”; in addition see at least [0044] via: “...The selected sepsis prediction model is then used to monitor the monitored patient to detect infection and sepsis events 150, for example by processing new/updates to patient health data...”)
c. processing the integrated data using an artificial intelligence (AI) model machine learning or AI-based models, such as classifier models, and may be