DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/21/2026 has been entered.
Response to Amendment
This office action is in response to the remarks filed on 03/16/2026.
The amendment filed 03/16/2026 has been entered. Claims 1-8 and 10-20 remain pending in the application, claim 9 has been canceled, and claims 13-20 have been previously withdrawn.
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 1-4, 6, 8 and 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Mert et al. (US 20230181148 A1, of record, hereinafter "Mert") in view of Sandgaard et al. (US 20220202350 A1, of record, hereinafter “Sandgaard”) and Minamide et al. (US 20230218165 A1, hereinafter “Minamide”).
Regarding claim 1, Mert teaches a medical system, comprising:
a first pad (additional ultrasound patches 10 a [0074]; [fig.7]) applied to a skin surface of a patient (ultrasound probe that makes contact with a skin of the subject. [0028]), the pad configured to acquire an image of a first vasculature of the patient (the ultrasound images 192, 194 produced by the ultrasound patches 10 a, 10 b [0075]; shows vasculature as shown in fig. 8 below),
a system module having a console (ultrasound user console 3 [0049]) coupled with the first pad (additional ultrasound patches 10 a [0074]; [fig.7]), the console including a processor and a memory having logic stored thereon that, when executed by the processor performs operations that include (a signal processor 22 [0049]):
receiving the image of the first vasculature from the first pad (receiving ultrasound data generated by an ultrasound probe being displaced across a part of the patient's anatomy containing the section of the patient's vascular system [0019]), the first vascular including a first blood vessel and the first blood vessel including an artery or a vein (arterial locations 157 and 157 [0054]);
determining a value of a vascular parameter of the first vasculature from the image of the first vasculature (said ultrasound data comprising image data and Doppler data; generating ultrasound images from said image data… processing the received Doppler data to obtain blood flow characteristics for the section of the patient's vascular system [0019]), the vascular parameter of the first vasculature including one or more of a size of the first blood vessel, a shape of the first blood vessel, or a blood flow rate through the first blood vessel ([0067] discloses calculation of blood flow rate in a blood vessel);
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Fig. 7 reproduced of Mert reproduced above
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Fig. 8 of Mert reproduced above
Mert, however, does not teach:
applying an artificial intelligence (AI) algorithm to the value of a vascular parameter of the first vasculature to determine a status of a sepsis of the patient based on the value of the vascular parameter of the first vasculature, wherein:
the Al algorithm is defined by Al logic performed on an external computing device,
the Al logic receives historical data from a plurality of the medical systems, the historical data including:
historical vascular parameter values of the first vasculature and historical sepsis statuses, wherein:
each historical sepsis status corresponds to a historical vascular parameter value of the first vasculature, and each historical sepsis status is acquired by a clinician using a suitable processes or equipment independent from the medical system and input by the clinician into the medical system,
the Al logic defines the Al algorithm as a correlation between the status of a sepsis and the value of the vascular parameter values and the Al logic transmits the Al algorithm to the medical system.
Sandgaard is considered analogous to the instant application as “Multiparameter noninvasive sepsis monitor” is disclosed (title).
Sandgaard teaches:
applying an artificial intelligence (AI) algorithm … ([0074] and [0082] discloses using cardiac performance/blood pressure as a vital sign) to determine a status of a sepsis of the patient based on the value of the vascular parameter of the first vasculature (Additional methods of using machine learning to extract principal components or patterns from a plurality of physiological parameters based on outcomes of septic onset in monitored patients to further improve the sensitivity and specificity of individual or combined parameters in detection of features that are indicative of sepsis or the onset of sepsis in a patient [0082]), wherein:
the Al algorithm is defined by Al logic performed on an external computing device (a database 128 is remote from the other elements of the system 100 and may store data suitable for use before, during, and/or after monitoring the patient 104 for sepsis…..For instance, a database 128 may include sepsis monitoring data for multiple patients 104, where such sepsis monitoring data may be fed into a neural network 136 as training data from the database 128 (and/or from the processing circuitry 108) that executes one or more machine learning algorithms to improve the accuracy of sepsis detection and corresponding sepsis treatments for future patients 104 [0098])
the Al logic receives historical data from a plurality of medical systems ([0082]-[0083] discloses using machine learning for multiple physiological parameters, [0086] discloses that the database includes multiple sensor information), the historical data including:
historical vascular parameter values…. and historical sepsis statuses, (Examples of historical data include data collected from a previous sepsis monitoring session or other medical monitoring of a patient and/or data from monitoring sessions of other patients [0134]) wherein:
each historical sepsis status corresponds to a historical vascular parameter value of the first vasculature (Examples of historical data include data collected from a previous sepsis monitoring session or other medical monitoring of a patient and/or data from monitoring sessions of other patients [0134]), and each historical sepsis status is acquired by a clinician using a suitable processes or equipment independent from the medical system and input by the clinician into the medical system (The historical data and real-time data may be processed by an machine learning system or neural network (e.g., neural network 136) that is programmed to improve the coefficients or the functions of the coefficients using the historical data and/or real-time data [0098]),
the Al logic defines the Al algorithm as a correlation between the status of a sepsis and the value of the vascular parameter values and the Al logic transmits the Al algorithm to the medical system (The coefficients and/or functions that determine the coefficients may be determined based on historical data, real-time data, or both. Examples of historical data include data collected from a previous sepsis monitoring session or other medical monitoring of a patient and/or data from monitoring sessions of other patients. Examples of real-time data include data collected during the current monitoring session of a patient and/or data collected during concurrent monitoring sessions of other patients [0134]; calculate patient parameter changes for the purpose of determining a sepsis index, where such sepsis index is used to determine the sepsis state of the patient 104 [0103]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the invention of Mert to include applying an artificial intelligence (AI) algorithm to the value of a vascular parameter to determine a status of a sepsis of the patient based on the value of the vascular parameter of the first vasculature, wherein: the Al algorithm is defined by Al logic performed on an external computing device, the Al logic receives historical data from a plurality of medical systems, the historical data including: historical vascular parameter values and historical sepsis statuses, wherein: each historical sepsis status corresponds to a historical vascular parameter value, and each historical sepsis status is acquired by a clinician using a suitable processes or equipment independent from the medical system and input by the clinician into the medical system, the Al logic defines the Al algorithm as a correlation between the status of a sepsis and the value of the vascular parameter values and the Al logic transmits the Al algorithm to the medical system, as taught by Sandgaard. Doing so would allow for reducing some of the burden of manual patient monitoring while improving prognosis of septic patients through proactive, early detection and alerting, as suggested by Sandgaard ([0058]).
The combined invention is silent regarding
[applying an artificial intelligence (AI) algorithm to the value of the vascular parameter] of the first vasculature to [determine a status of a sepsis of the patient based on the value of the vascular parameter of the first vasculature],
[each historical sepsis status corresponds to a historical vascular parameter value] of the first vasculature.
Minamide is considered analogous to the instant application as “Medical system and medical information processing apparatus” is disclosed (title).
Minamide, first also teaches:
applying an artificial intelligence (AI) algorithm to the value of a vascular parameter of the first vasculature (the data processor is configured to generate information on an object formed in a blood vessel based at least on the blood flow information [0020]) to determine a status of a sepsis of the patient based on the value of the vascular parameter of the first vasculature (the technique and technology according to the present disclosure may be capable of inputting these kinds of data into a learned model constructed by machine learning and outputting an index or indicator related to aggravation of an infectious disease [0176]; wherein
each historical sepsis status corresponds to a historical vascular parameter value of the first vasculature (The blood flow velocity distribution data part 121 is a region in which information representing a distribution of blood flow velocity in a blood vessel generated by the data processor 20 is recorded [0088]; the technique and technology according to the present disclosure may be capable of inputting these kinds of data into a learned model constructed by machine learning and outputting an index or indicator related to aggravation of an infectious disease [0176]; [0123] further discloses training data for the machine learning model).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the combined invention of Mert to include applying an artificial intelligence (AI) algorithm to the value of the vascular parameter of the first vasculature to determine a status of a sepsis of the patient based on the value of the vascular parameter of the first vasculature, and each historical sepsis status corresponds to a historical vascular parameter value of the first vasculature as taught by Minamide. Doing so would enable non-invasive early detection of a change in a medical condition and provision of various kinds of diagnostic support information, as suggested by Minamide ([0176]).
Regarding claim 2, modified Mert teaches system according to claim 1, as discussed above. Mert further teaches w herein the first pad includes a number of ultrasonic transducers extending across a patient contact surface of the first pad (ultrasound probe 10, e.g. an array of ultrasound transducer elements 66, which may be arranged in a one-dimensional or two-dimensional array of transducer elements [0045], The ultrasound probe 10 may take any suitable shape, e.g. a hand-held probe, mounted probe, patch and so on [0046]) the ultrasonic transducers configured to acquire the image of the first vasculature (the ultrasound images 192, 194 produced by the ultrasound patches 10 a, 10 b respectively may be shown on the display screen [0074]; the patches capture images of the vasculature as shown in fig. 8).
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Fig. 8 of Mert reproduced above
Regarding claim 3, modified Mert teaches the system according to claim 1, as discussed above. Mert further teaches the image of the first vasculature includes a first blood vessel, and the vascular parameter includes a size of the first blood vessel vasculature (the ultrasound images 192, 194 produced by the ultrasound patches 10 a, 10 b respectively may be shown on the display screen [0074]; the patches capture images of the vasculature as shown in fig. 8), a shape of the first blood vessel, or a blood flow rate through the first blood vessel (such ultrasound patches 10 a, 10 b may provide real-time vessel information, such as blood flow velocities, vessel diameter, stenosis information, and so on [0075]).
Regarding claim 4, modified Mert teaches system according to claim 1, as discussed above. Mert further wherein the vascular parameter is of the first vasculature includes at least two of the size of the first blood vessel (vessel diameter [0075]), the shape of the first blood vessel, or the blood flow rate through the first blood vessel (such ultrasound patches 10 a, 10 b may provide real-time vessel information, such as blood flow velocities, vessel diameter, stenosis information, and so on [0075]).
Regarding claim 6, modified Mert teaches the system according to claim 1, as discussed above. Mert further teaches:
the image of the first vasculature includes a second blood vessel of the first vasculature ([0066]-[0067] discloses that the branched vessel network is imaged; fig. 8 shows the field of view of the ultrasound patches 10a and 10b, which covers multiple blood vessels within a vasculature; fig.5 [0070] also depicts an ultrasound parch which covers multiple blood vessels within a vasculature), and
the vascular parameter includes at least one of a size of the second blood vessel, a shape of the second blood vessel, or a blood flow rate through the second blood vessel (The processor arrangement 50 may further evaluate the blood flow characteristics in operation 113 for the purpose of deconstructing or segmenting the branched vessel network or vessel tree within the section of the vascular system 155 of the patient… based on analysis of the pulsability of the blood vessel and/or blood flow profile through the vessel, and to identify bifurcations in the branched vessel network. [0066])
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Fig. 5 of Mert reproduced above
Regarding claim 8, modified Mert teaches the system according to claim 1, as discussed above. Mert, however, does not teach wherein the AI algorithm is defined from a plurality of sepsis events across a population of patients
Sandgaard, however, teaches wherein the AI algorithm is defined from a plurality of sepsis events across a population of patients (The historical data and real-time data may be processed by an machine learning system or neural network (e.g., neural network 136) that is programmed to improve the coefficients or the functions of the coefficients using the historical data and/or real-time data [0098]; The coefficients and/or functions that determine the coefficients may be determined based on historical data, real-time data, or both. Examples of historical data include data collected from a previous sepsis monitoring session or other medical monitoring of a patient and/or data from monitoring sessions of other patients. Examples of real-time data include data collected during the current monitoring session of a patient and/or data collected during concurrent monitoring sessions of other patients [0134]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the combined invention of Mert to include wherein the AI algorithm is defined from a plurality of sepsis events across a population of patients, as taught by Sandgaard. Doing so would allow for reducing some of the burden of manual patient monitoring while improving prognosis of septic patients through proactive, early detection and alerting, as suggested by Sandgaard ([0058]).
Regarding claim 10, modified Mert teaches system according to claim 1, as discussed above. Mert, however, does not teach wherein: the historical data further include patient data corresponding to the sepsis statuses, and the patient data include one or more of weight, height, sex, age, race, or body mass index.
Sandgaard, however, teaches the historical data further include patient data corresponding to the sepsis statuses (The coefficients and/or functions that determine the coefficients may be determined based on historical data, real-time data, or both. Examples of historical data include data collected from a previous sepsis monitoring session or other medical monitoring of a patient and/or data from monitoring sessions of other patient [0134]), and
the patient data include one or more of weight, height, sex, age, race, or body mass index (The coefficients and/or functions that determine the coefficients may be determined based on historical data, real-time data, or both. Examples of historical data include data collected from a previous sepsis monitoring session or other medical monitoring of a patient and/or data from monitoring sessions of other patients. Examples of real-time data include data collected during the current monitoring session of a patient and/or data collected during concurrent monitoring sessions of other patients [0134]; [0118] discloses that critical parameters are related to age, height, weight, gender, etc.).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the combined invention of Mert to include the historical data further include patient data corresponding to the sepsis statuses, and the patient data include one or more of weight, height, sex, age, race, or body mass index, as taught by Sandgaard. Doing so would allow for reducing some of the burden of manual patient monitoring while improving prognosis of septic patients through proactive, early detection and alerting, as suggested by Sandgaard ([0058]).
Regarding claim 11, modified Mert teaches the system according to claim 1, as discussed above. Mert, however, does not teach the system module is communicatively coupled with the external computing device, the external computing configured to receive the value of the vascular parameter from the system module, and the Al logic defines the Al algorithm based on the value of the vascular parameter and the historical data. Sandgaard, however, teaches:
the system module is communicatively coupled with the external computing device (a database 128 is remote from the other elements of the system 100 and may store data suitable for use before, during, and/or after monitoring the patient 104 for sepsis [0098]; Any such remote computer may be connected to the user's/operator's/administrator's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider) [0167]).
the external computing configured to receive the value of the vascular parameter from the system module (a database 128 may include sepsis monitoring data for multiple patients 104, where such sepsis monitoring data may be fed into a neural network 136 as training data from the database 128 (and/or from the processing circuitry 108) that executes one or more machine learning algorithms to improve the accuracy of sepsis detection and corresponding sepsis treatments for future patients 104 [0098]), and
the Al logic defines the Al algorithm based on the value of the vascular parameter and the historical data (using machine learning to extract principal components or patterns from a plurality of physiological parameters based on outcomes of septic onset in monitored patients to further improve the sensitivity and specificity of individual or combined parameters in detection of features that are indicative of sepsis or the onset of sepsis in a patient [0082]; [0134] discloses use of historical data to determine sepsis status).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the combined invention of Mert to include the system module is communicatively coupled with the external computing device, the external computing configured to receive the value of the vascular parameter from the system module, and the Al logic defines the Al algorithm based on the value of the vascular parameter and the historical data, as taught by Sandgaard. Doing so would allow for reducing some of the burden of manual patient monitoring while improving prognosis of septic patients through proactive, early detection and alerting, as suggested by Sandgaard ([0058]).
Claim 5, 7, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Mert et al. (US 20230181148 A1, hereinafter "Mert") in view of Sandgaard et al. (US 20220202350 A1, hereinafter “Sandgaard”), Minamide et al. (US 20230218165 A1, hereinafter “Minamide”), and Torp et al. (US 20210251599 A1, hereinafter "Torp").
Regarding claim 5, modified Mert teaches the system according to claim 1, as discussed above. Mert further teaches:
receiving a first image of the first vasculature from the first pad (receiving a stream of ultrasound data [0031];
receiving a second image of the first vasculature from the first pad, the second image subsequent the first image (receiving a stream of ultrasound data corresponding to a plurality of ultrasound images from the ultrasound imaging arrangement [0031]; each imaging ultrasound pad emits a plurality of images, which are in a stream, i.e. subsequent) ;
determining a first value of the vascular parameter of the first vasculature from the first image ([0052]-[0053] discloses obtaining blood flow throughout all of the images; ultrasound patches 10 a, 10 b may provide real-time vessel information, such as blood flow velocities, vessel diameter, stenosis information, and so on [0075]; [0065] discloses obtaining vascular parameters at two different points in time/two different images)
determining a second value of the vascular parameter of the first vasculature from the second image ([0052]-[0053] discloses obtaining blood flow throughout all of the images; ultrasound patches 10 a, 10 b may provide real-time vessel information, such as blood flow velocities, vessel diameter, stenosis information, and so on [0075]; [0065] discloses obtaining vascular parameters at two different points in time/two different images).
Mert, however, does not teach: determining a difference between the second value and the first value; and determining a progression of the sepsis based on the difference.
Torp, however, teaches
determining a difference between the second value and the first value ([0329]-[0332] discloses monitoring of the blood vessel, which is where multiple images are taken of the same area over time, to monitor the microcirculation); and
determining a progression of the sepsis based on the difference ([0335]-[0339] discloses monitoring the blood flow characteristics (i.e. finding the difference between the images) to determine if there is hemodynamic instability, to diagnose sepsis or the extent or severity thereof, or to provide a prognosis for the onset of and/or progression of sepsis in the subject, or to determine a response to the treatment of sepsis in the subject).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the combined invention of Mert to include determining a difference between the second value and the first value, and determining a progression of the sepsis based on the difference, as taught by Torp. Doing so would improve the early identification of sepsis in subjects at significant risk of sepsis, as suggested by Torp ([0319]-[0320]).
Regarding claim 7, modified Mert teaches the system according to claim 1, as discussed above. Mert further teaches:
a second pad (10 b; fig. 7; [0074]) applied to the skin surface at a location separate from the first pad (fig. 7, reproduced below, shows the second pad 10 b, applied at a separate surface than the first pad), the second pad configured to acquire an image of a second vasculature of the patient (the second pad has a separate field of view (FOV) that captures a second vasculature as shown in fig. 8 below),
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Fig. 8 of Mert reproduced above
wherein the console is coupled with the second pad (ultrasound image processing system 3 may take any suitable shape, such as a user console [0046]), and the operations further include:
receiving the image of the second vasculature from the second pad (the ultrasound images 192, 194 produced by the ultrasound patches 10 a, 10 b [0075]);
determining a value of a vascular parameter of the second vasculature from the image of the second vasculature (The processor arrangement 50 may further evaluate the blood flow characteristics in operation 113 for the purpose of deconstructing or segmenting the branched vessel network or vessel tree within the section of the vascular system 155 of the patient, for example for the purpose of distinguishing between arteries and veins, e.g. based on analysis of the pulsability of the blood vessel and/or blood flow profile through the vessel, and to identify bifurcations in the branched vessel network [0066]).
Mert, however, does not each determining the status of the sepsis based on the value of the vascular parameter of the second vasculature in combination with the value of the vascular parameter of the first vasculature.
Torp, however, teaches determining the status of the sepsis based on the value of the vascular parameter of the second vasculature (determining a characteristic of blood flow in multiple vessels, e.g. multiple vessels of the minor vasculature or multiple arterial microvessels or one or more of both, simultaneously. In these embodiments the ultrasound transducer is fastened to the surface (e.g. skin) of the subject at a site which contains a plurality of blood vessels, e.g. a plurality of vessels of the minor vasculature or a plurality of arterial microvessels or one or more of both, within range of the transducer [0156]) in combination with the value of the vascular parameter of the first vasculature (by determining the characteristics of blood flow in multiple blood vessels simultaneously the information obtained may contribute advantageously to the monitoring and/or analysis of the physiology of healthy vertebrate animals and to the diagnosis, monitoring or prediction of the progression of disease and pathological conditions and/or treatment responses in such subjects [0153]; primary purpose is to distinguish pathologic blood flow patterns in case of sepsis, from normal microcirculatory conditions in case of less grave infections, thereby providing a means to differentiate sepsis patients early in the progression of the condition. Likewise, it may be used to track a sepsis patient's response to treatment [0634]; blood flow is monitored along multiple blood vessels to monitor sepsis progression, i.e. status of sepsis).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the combined invention of Mert to include determining the status of the sepsis based on the value of the vascular parameter of the second vasculature in combination with the value of the vascular parameter of the first vasculature, as taught by Torp. Doing so would improve the early identification of sepsis in subjects at significant risk of sepsis, as suggested by Torp ([0319]-[0320]).
Regarding claim 12, modified Mert teaches system according to claim 1, as discussed above. Mert, however, does not teach wherein the operations further include: comparing the status of the sepsis with a plurality of ranked sepsis statuses stored in the memory, and providing a notification when the status of the sepsis exceeds a defined one of the ranked sepsis statuses.
Torp, however, teaches wherein the operations further include:
comparing the status of the sepsis with a plurality of ranked sepsis statuses stored in the memory (Values of the characteristic at two or more different depths may be compared; for example, a ratio, or other comparison operation, may be calculated. Outputs of this comparison operation may be displayed or monitored. They may provide a clinically-significant indicator which may be used for generating alerts by a monitoring system. In some embodiments, an aggregated value (e.g., mean or sum) from a plurality of depths may be generated, and may be output [0107]; [0377] discloses use of mathematical models, i.e. algorithms, to compare features of the vasculature, i.e. vascular parameter, to healthy patients and those with microvasculature dysfunctions), and
providing a notification when the status of the sepsis exceeds a defined one of the ranked sepsis statuses (Values of the characteristic at two or more different depths may be compared; for example, a ratio, or other comparison operation, may be calculated. Outputs of this comparison operation may be displayed or monitored. They may provide a clinically-significant indicator which may be used for generating alerts by a monitoring system. In some embodiments, an aggregated value (e.g., mean or sum) from a plurality of depths may be generated, and may be output [0107]; the signal may cause an alarm to be raised—e.g., by sounding an audible or visual alert (a flashing light, a message on a display screen, etc.) or by sending a message over a network connection. The system may be a patient monitoring system [0101]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the combined invention of Mert to include wherein the operations further include: comparing the status of the sepsis with a plurality of ranked sepsis statuses stored in the memory, and providing a notification when the status of the sepsis exceeds a defined one of the ranked sepsis statuses, as taught by Torp. Doing so would improve the early identification of sepsis in subjects at significant risk of sepsis, as suggested by Torp ([0319]-[0320]).
Response to Arguments
Applicant's arguments filed 03/16/2026 have been fully considered but they are moot.
Regarding the 35 USC § 103 rejection of claim 1, applicant’s arguments on pages 9-11 are premised upon the assertion that the prior art does not teach the newly added limitations regarding “determining a value of a vascular parameter of the first vasculature from the image of the first vasculature, the vascular parameter of the first vasculature including one or more of a size of the first blood vessel, a shape of the first blood vessel, or a blood flow rate through the first blood vessel” and “applying an artificial intelligence (AI) algorithm to the value of the vascular parameter of the first vasculature to determine a status of a sepsis of the patient based on the value of the vascular parameter of the first vasculature”. Regarding the limitation “determining a value of a vascular parameter of the first vasculature from the image of the first vasculature, the vascular parameter of the first vasculature including…”, this limitation is taught in Mert n the newly cited portions above. Regarding the limitation “applying an artificial intelligence (AI) algorithm to the value of the vascular parameter of the first vasculature to determine a status of a sepsis of the patient based on the value of the vascular parameter of the first vasculature”, the arguments are moot as the rejection now relies upon Minamide et al. (US 20230218165 A1, hereinafter “Minamide”), to teach this limitation. Accordingly, this argument is moot.
Regarding the 35 USC § 103 rejection of claim 2-8 and 10-12, applicant’s arguments on page 11 are premised upon the assertion that the claims are allowable due to dependency on an allowable claim. Examiner respectfully disagrees for the reasons stated above.
Conclusion
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/N.B./ Examiner, Art Unit 3798
/PASCAL M BUI PHO/ Supervisory Patent Examiner, Art Unit 3798
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