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 in reply to the current action filed on 12/18/2024.
Claims 1-20 are currently pending and have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/18/2024 was filed before the mailing date of the first action on the merits. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 § U.S.C 101 because the claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 Analysis:
Independent Claims 1, 9, and 16 are within the four statutory categories. Claims 1 and 9 are directed to a system, and Claim 16 is directed to a method. Dependent Claims 2-8 and 10-15 are further directed to a system and Claims 17-20 are further directed to a method, and therefore, the dependent claims also fall into one of the four statutory categories.
Step 2A Analysis – Prong One:
The substantially similar independent claims, taking Claim 1 as exemplary, recite the following:
A system for assessing blood perfusion, comprising: a wearable garment; a plurality of sensors affixed to the wearable garment; a processor communicatively coupled to the plurality of sensors;
a memory component communicatively coupled to the processor; a machine learning model stored in the memory component; and machine-readable instructions stored in the memory component that cause the processor to perform operations comprising:
receiving a first set of blood perfusion metrics associated with an individual wearing the wearable garment from the plurality of sensors;
generating a first reading based on the first set of blood perfusion metrics;
receiving a second set of blood perfusion metrics associated with the individual wearing the wearable garment from the plurality of sensors;
generating a second reading based on the second set of blood perfusion metrics; determining an intervention perfusion status of a medical intervention to improve blood perfusion for the individual based on the first reading and the second reading and indicative of a level of blood perfusion improvement;
and generating, with the machine learning model, an intervention recommendation indicative of whether additional intervention is recommended based on the first reading, the second reading, the intervention perfusion status, or combinations thereof.
The series of limitations as shown in underline above, given the broadest reasonable interpretation, recite the abstract idea certain methods of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teachings, and following rules or instructions – in this case, receiving a first set of metrics, generating a first reading based upon the first metrics, receiving a second set of metrics and generating a second reading based upon the second set of metrics, determining an intervention perfusion status, and generating an intervention recommendation), e.g., see MPEP 2106.04(a)(2). Any limitations not identified as part of the abstract idea are deemed “additional elements” and will be discussed in further detail below.
Dependent Claims 2, 4-7, 10, 12-15, and 17-20 recite additional limitations directed toward the abstract idea. For example, Claims 2 and 10 recite what the first and second set of blood perfusion metrics include, Claims 4, 12, and 17 recite the first reading is a pre-intervention reading for establishing a baseline reading of blood perfusion, the second reading is an intervention reading for establishing a current reading of blood perfusion during the medical intervention, and the intervention recommendation is a continuing intervention recommendation, Claims 5, 13, and 18 recite the first reading is a pre-intervention for establishing a baseline reading of blood perfusion, the second reading is a post-intervention for establishing a current reading of blood perfusion, and the intervention recommendation is a follow-up intervention recommendation that includes a recommended course of treatment or a predicted intervention perfusion status indicative of a predicted perfusion status result of the medical intervention within a time period following the medical intervention, Claims 6, 14, and 19 recite receiving a third set of blood perfusion metrics associated with the individual, generating a third reading as a follow-up intervention reading after generation of the intervention recommendation and improving subsequent follow-up intervention recommendations, Claims 7, 15, and 20 recite before generating the intervention recommendation, receiving a historical data set including prior blood perfusion metrics, prior interventions, prior intervention statuses, or combinations thereof from a plurality of individuals. Accordingly, the dependent claims only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, see MPEP 2106.04. Thus, dependent Claims 2, 4-7, 10, 12-15, and 17-20 are directed toward the same abstract idea of the independent claims that are grouped under certain methods of organizing human activity.
Step 2A Analysis – Prong Two:
Claims 1, 9, and 16 are not integrated into a practical application because the additional elements (i.e., the non-underlined limitations presented in prong one – in this case, the wearable garment, sensors, processor, memory, and machine learning model of Claim 1, the wearable device, sensors, processor, memory, and machine learning model of Claim 9, and the sensors, wearable device, and machine learning model of Claim 16) are recited at a high level of generality (i.e., as a generic processor performing generic computer functions) such that they amount to no more than mere instructions to apply the exceptions using a generic computer component. For example, Applicant’s specification explains that the wearable device 102 may include socks, gloves, sleeves, and/or any other wearable garment for assessing blood perfusion. For example, the wearable device 102 may be configured to be worn on a limb of a subject (e.g., arm, hand, leg, or foot) (see Applicant’s specification, ¶ 0015). The wearable device 102 may comprise a wearable garment, the one or more sensors 103 of FIG. 1 as a plurality of sensors 202, 204, 206, 208, 210, and may, in some embodiments, include one or more of the other components of the system control module 105 shown in FIG. 1 [0026]. Accordingly, each of the …processors of the processor 106 may be a controller, an integrated circuit, a microchip, or any other computing device [0016]. The memory 108 is communicatively coupled to the communication path 104 and may contain one or more memory modules comprising RAM, ROM, flash memories, hard drives, or any device capable of storing machine-readable and executable instructions [0017]. The perfusion module 112 may utilize supervised methods to train a machine learning model as an artificial intelligence (AI) model component that may be disposed in the memory 108 based on labeled training sets, wherein the machine learning model is a decision tree, a Bayes classifier, a support vector machine, a convolutional neural network, and/or the like [0021]. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the idea. Therefore, Claims 1, 9, and 16 are directed to an abstract idea without practical application.
Dependent Claims 3, 6-8, 11, 14-15, and 19-20 also recite additional elements. Claims 3 and 11 recite the previously recited wearable garment/device and sensors and specify the sensors are positioned on the garment so that they are adjacent to the individual when the garment/device is worn. Claims 6 and 14 recite the wearable garment/device, sensors, and machine learning model and specify the processor receives a third set of metrics associated with the individual wearing the garment/device, generates a third reading, and trains the machine learning model based on a comparison. Claim 7 recites the processor and specifies the processor receives a historical data set including prior blood perfusion metrics, prior interventions, prior intervention statuses, or combinations thereof from a plurality of individuals. Claim 8 recites the processor and machine learning model and specifies the processor trains the machine learning model based on the historical dataset. Claim 15 recites the processor and the machine learning model and specifies the processor receives a historical data set and trains the machine learning model based on the historical data set to generate intervention status predictions. Claim 19 recites the wearable device, sensors, and machine learning model and specifies receiving a third set of metrics of the individual wearing the garment/device, generates a third reading, and trains the machine learning model based on a comparison. Claim 20 recites the machine learning model and specifies training the machine learning model based on the historical data set to generate intervention status predictions. However, these additional elements are used in their expected fashion, so they do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on the abstract idea. These limitations amount to no more than mere instructions to apply an exception, and hence, do not integrate the aforementioned abstract idea into practical application.
Step 2B Analysis:
The claims, whether considered individually or as an ordered combination, do not include any additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of the wearable garment, sensors, processor, memory, and machine learning model of Claim 1, the wearable device, sensors, processor, memory, and machine learning model of Claim 9, and the sensors, wearable device, and machine learning model of Claim 16 amount to no more than mere instructions to apply an exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). MPEP MPEP 2106.05(I)(A) indicates that merely stating “apply it” or equivalent to the abstract idea cannot provide an inventive concept (“significantly more”). Accordingly, even in combination, these additional elements do not provide significantly more. As such, Claims 1, 9, and 16 are not patent eligible.
Dependent Claims 2, 4-5, 10, 12-13, and 17-18 do not recite any additional elements and only narrow the abstract idea. Claims 2 and 10 recite what the first and second set of blood perfusion metrics include, Claims 4, 12, and 17 recite the first reading is a pre-intervention reading for establishing a baseline reading of blood perfusion, the second reading is an intervention reading for establishing a current reading of blood perfusion during the medical intervention, and the intervention recommendation is a continuing intervention recommendation, Claims 5, 13, and 18 recite the first reading is a pre-intervention for establishing a baseline reading of blood perfusion, the second reading is a post-intervention for establishing a current reading of blood perfusion, and the intervention recommendation is a follow-up intervention recommendation that includes a recommended course of treatment or a predicted intervention perfusion status indicative of a predicted perfusion status result of the medical intervention within a time period following the medical intervention.
Dependent Claims 3, 6-8, 11, 14-15, and 19-20 recite previously recited additional elements, which are not eligible for the reasons stated above, and further narrow the abstract idea. Claims 3 and 11 recite the previously recited wearable garment/device and sensors and specify the sensors are positioned on the garment so that they are adjacent to the individual when the garment/device is worn. Claims 6 and 14 recite the wearable garment/device, sensors, and machine learning model and specify the processor receives a third set of metrics associated with the individual wearing the garment/device, generates a third reading, and trains the machine learning model based on a comparison. Claim 7 recites the processor and specifies the processor receives a historical data set including prior blood perfusion metrics, prior interventions, prior intervention statuses, or combinations thereof from a plurality of individuals. Claim 8 recites the processor and machine learning model and specifies the processor trains the machine learning model based on the historical dataset. Claim 15 recites the processor and the machine learning model and specifies the processor receives a historical data set and trains the machine learning model based on the historical data set to generate intervention status predictions. Claim 19 recites the wearable device, sensors, and machine learning model and specifies receiving a third set of metrics of the individual wearing the garment/device, generates a third reading, and trains the machine learning model based on a comparison. Claim 20 recites the machine learning model and specifies training the machine learning model based on the historical data set to generate intervention status predictions. Hence, Claims 2-8, 10-15, and 17-20 do not include any additional elements that amount to “significantly more” than the judicial exception.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 1-20 are nonetheless rejected under 35 U.S.C 101 as being directed to non-statutory subject matter.
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.
Claims 1-2, 4-10, and 12-20 are rejected under 35 USC § 103 as being unpatentable over Shelton et al. (US 20220233119 A1) in view of Griffin et al. (US 20210257093 A1).
Regarding Claim 1, Shelton discloses the following:
A system for assessing blood perfusion, comprising: (Shelton discloses a computing system may monitor a patient's biomarkers and predict a potential blood perfusion difficulty complication [0956].)
a wearable garment; a plurality of sensors affixed to the wearable garment; (Shelton discloses the wearable sensing system may be or may include a wristband patient sensing system, an ingestible pill patient sensing system,…an instrumented socks patient sensing system, etc. [1136]. The Examiner interprets instrumented socks as wearable garments. Measurement data related to a set of patient biomarkers for post-surgical monitoring may be received. For example, a computing system may be configured to receive the measurement data from one or more sensing systems. A sensing system may be or may include a patient wearable device. A sensing system may include one or more sensors [1097].)
a processor communicatively coupled to the plurality of sensors; a memory component communicatively coupled to the processor; (Shelton discloses the computing system may include a processor configured to obtain pre-surgical and/or in-surgical measurement data associated with one or more patient biomarkers via one or more sensing systems. The computing system may predict a blood perfusion difficulty complication based on the biomarker measurement data [0956].)
a machine learning model stored in the memory component; and machine-readable instructions stored in the memory component that cause the processor to perform operations comprising: (Shelton discloses predictions of complications and/or recovery milestones may be generated, for example, by one or more machine learning (ML) models, such as predictive models, trained to make predictions after being trained on training data [1123].)
receiving a first set of blood perfusion metrics associated with an individual wearing the wearable garment from the plurality of sensors; (Shelton discloses the computing system may obtain, from the sensing system(s), measurement data associated with one or more blood perfusion difficulty-related biomarkers, such as core body temperature,…oxygen saturation, blood sugar level, hydration state, and/or the like [0968]. The sensing system may be a patient wearable sensing system. The sensing system may be communicatively coupled with a computing device or a hub or a surgical hub. In an example, a sensing system (e.g., as shown in FIGS. 54 and 55) may comprise one or more processors configured (e.g., by executing instructions in an executable program) to (e.g., at least) perform a method to receive (e.g., from a hub or a surgical hub), a first threshold associated with a first patient biomarker and/or a second threshold associated with a second patient biomarker [1213].)
generating a first reading based on the first set of blood perfusion metrics; (Shelton discloses VO2Max measures the body's oxygen consumption ability. The measured VO2Max score may be compared against a VO2Max score threshold. A tissue irregularity complication may be determined when a SpO2 measurement is above such score threshold. For example, a respiration rate may measure the number of breaths per minute. The measured respiration rate may be compared against a respiration rate range threshold. A tissue irregularity complication may be determined when a respiration rate measurement is above such range threshold. For example, a heart rate variability score measured by a heart rate sensing system may be compared against a heart rate variability score range threshold [1023]. The Examiner interprets the measured values from the sensors as readings.)
Shelton does not disclose generating a second metric, determining an intervention status, and providing a recommendation of an additional intervention which is met by Griffin:
receiving a second set of … metrics associated with the individual; generating a second reading based on the second set of…metrics (Griffin teaches a patient profile may be automatically updated by an application installed in the patient's user device that monitors the patient's health by connecting to one or more health monitoring devices. For example, the patient may perform an at-home blood test and the raw data may be uploaded to their patient profile automatically using a device that takes the blood sample as input and connects to the application on the user device to upload raw data about the blood sample to the patient profile. The raw data may be analyzed to determine whether the patient has shown improvement in their medical condition [0080]. The Examiner interprets the updated patient measurement data as the second set of metrics.)
determining an intervention perfusion status of a medical intervention to improve …the individual based on the first reading and the second reading and indicative of a level of…improvement; (Griffin teaches a first health metric value measured at a time before an intervention and a second health metric value measured at a time after the intervention may be compared to determine if there is a positive or negative change in the health metric [0081].)
and generating, with the machine learning model, an intervention recommendation indicative of whether additional intervention is recommended based on the first reading, the second reading, the intervention perfusion status, or combinations thereof. (Griffin teaches machine learning models may be used to process the data …to procure insights and predictions that may be employed to control certain automated processes. For example, one or more interventions, recommendations, …may be automatically generated for a user based on the insights and/or predictions from the machine learning models [0012]. A first health metric value measured at a time before an intervention and a second health metric value measured at a time after the intervention may be compared to determine if there is a positive or negative change in the health metric…a negative change may induce another intervention that is more significant than the first intervention …A positive change may induce another intervention that is less significant than the first intervention. A first intervention of generating a digital recommendation to be sent to the patient's user device suggesting that the patient exercise three times per week…The patient's blood pressure may be monitored and if the blood pressure continues to increase, for example, by a threshold percentage more than the previous measure of blood pressure, then the second intervention may be sent to the patient [0081-82].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for using a wearable garment to obtain blood perfusion metrics and analyzing the data to assess a patient’s blood perfusion with a machine learning model as disclosed by Shelton to incorporate analyzing a second set of metrics, determining an intervention status, and outputting a recommendation of whether an additional intervention is recommended based on the intervention status as taught by Griffin. This modification would create a system and method capable of identifying risks and enabling a timely intervention to improve an individual’s health (see Griffin, ¶ 0045).
Regarding Claim 9, this claim recites limitations that are substantially similar to those recited in Claim 1 above; thus, the same rejection applies. Shelton further discloses:
…when the individual is wearing the wearable device;… (Shelton discloses patient biomarker measurements may be performed with one or more devices, such as a single device (e.g., a ring) or multiple devices (e.g., a bracelet or a watch and a ring), for example, as illustrated in FIGS. 54 and 56, and also described in FIGS. 11A-11D [1297].)
Regarding Claim 16, this claim recites limitations that are substantially similar to those recited in Claim 1 above; thus, the same rejection applies. Shelton further discloses:
A method for assessing blood perfusion… (Shelton discloses systems, methods, and instrumentalities are disclosed herein for a (e.g., pre-, in-, and/or post-operative) patient monitoring system [1186].)
…when the individual is wearing the wearable device;… (Shelton discloses patient biomarker measurements may be performed with one or more devices, such as a single device (e.g., a ring) or multiple devices (e.g., a bracelet or a watch and a ring), for example, as illustrated in FIGS. 54 and 56, and also described in FIGS. 11A-11D [1297].)
Regarding Claim 2, Shelton and Griffin teach the limitations as seen in the rejection of Claim 1 above. Shelton further discloses:
wherein the first set of blood perfusion metrics and the second set of blood perfusion metrics include blood oxygenation, heart rate, bioimpedance, temperature, ankle-brachial pressure, or combinations thereof. (Shelton discloses the computing system may obtain, from the sensing system(s), measurement data associated with one or more blood perfusion difficulty-related biomarkers, such as core body temperature, peripheral temperature, oxygen saturation, blood sugar level, hydration state, and/or the like [0968].)
Regarding Claim 10, this claim recites limitations that are substantially similar to those recited in Claim 2 above; thus, the same rejection applies.
Regarding Claim 4, Shelton and Griffin teach the limitations as seen in the rejection of Claim 1 above. Shelton further discloses:
the first reading is a pre-intervention reading for establishing a baseline… (Shelton discloses historical data and/or pre-operating patient measurements may be used to establish baselines against which analogous operative data may be compared (i.e., a common-mode analysis). The baseline comparison may be implemented in an appropriate scoring rubric. For example, baselines for breathing patterns may be assessed during an office visit and/or with an uncontrolled patient monitoring system before a scheduled surgery [0843].)
…reading of blood perfusion; (Shelton discloses VO2Max measures the body’s oxygen consumption ability. The measured VO2Max score may be compared against a VO2Max score threshold. A tissue irregularity complication may be determined when a SpO2 measurement is above such score threshold. For example, a respiration rate may measure the number of breaths per minute. The measured respiration rate may be compared against a respiration rate range threshold. A tissue irregularity complication may be determined when a respiration rate measurement is above such range threshold. For example, a heart rate variability score measured by a heart rate sensing system may be compared against a heart rate variability score range threshold [1023]. The Examiner interprets the measured values from the sensors as readings.)
Although Shelton discloses establishing a baseline for data, it does not disclose this data being for the application of blood perfusion metrics. However, this modification of the type of data being used as baseline data still provides the same improvements of ensuring the analysis properly determines changes to the blood perfusion of the patient prior to and/or following an intervention (see Applicant’s disclosure, ¶ 0029). Since each individual element and its function are shown in the prior art, albeit in different embodiments, this practice of utilizing blood perfusion metrics as a pre-interventions reading for establishing a baseline is well known in the art and would be obvious to try. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Shelton does not disclose the following limitations met by Griffin:
the second reading is an intervention reading for establishing a current reading of blood perfusion during the medical intervention; (Griffin teaches a patient profile may be automatically updated by an application installed in the patient's user device that monitors the patient's health by connecting to one or more health monitoring devices. For example, the patient may perform an at-home blood test and the raw data may be uploaded to their patient profile automatically using a device that takes the blood sample as input and connects to the application on the user device to upload raw data about the blood sample to the patient profile. The raw data may be analyzed to determine whether the patient has shown improvement in their medical condition [0080].The Examiner interprets the updated patient measurement data as the second set of metrics.)
and the intervention recommendation is a continuing intervention recommendation that includes an indication whether continued intervention should be provided, a predicted intervention perfusion status indicative of a predicted perfusion status result of the medical intervention at or after completion of the intervention, or combinations thereof. (Griffin teaches trends such as readmittance may be used to determine effectiveness of medical interventions and/or whether further medical interventions may assist in preventing future readmittances [0046]. Based on learned data, a probability for successful intervention may be calculated and used in determining which patient profiles to prioritize in engagement. For example, a patient profile may have certain characteristics that may be used as indications that corresponding interventions or notification would more likely be successful or effective. As an illustrative example, an intervention for a patient profile with characteristics associated with a pregnancy group may be more receptive to an intervention or notification than a patient profile with characteristics associated with an at-risk for diabetes group, or vice versa [0089]. The Examiner interprets the predicted level of effectiveness of an intervention as a predicted status of the patient as a result of the intervention.)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for using a wearable garment to obtain blood perfusion metrics and analyzing the data to assess a patient’s blood perfusion with a machine learning model as disclosed by Shelton to incorporate the second reading being an intervention reading for establishing a current metric and outputting a predicted status as a result of the medical intervention as taught by Griffin. This modification would create a system and method capable of identifying risks and enabling a timely intervention to improve an individual’s health (see Griffin, ¶ 0045).
Regarding Claim 12 and 17, these claims recite limitations that are substantially similar to those recited in Claim 4 above; thus, the same rejection applies.
Regarding Claim 5, Shelton and Griffin teach the limitations as seen in the rejection of Claim 1 above. Shelton further discloses:
the first reading is a pre-intervention reading for establishing a baseline… (Shelton discloses historical data and/or pre-operating patient measurements may be used to establish baselines against which analogous operative data may be compared (i.e., a common-mode analysis). The baseline comparison may be implemented in an appropriate scoring rubric. For example, baselines for breathing patterns may be assessed during an office visit and/or with an uncontrolled patient monitoring system before a scheduled surgery [0843].)
…reading of blood perfusion; (Shelton discloses VO2Max measures the body’s oxygen consumption ability. The measured VO2Max score may be compared against a VO2Max score threshold. A tissue irregularity complication may be determined when a SpO2 measurement is above such score threshold. For example, a respiration rate may measure the number of breaths per minute. The measured respiration rate may be compared against a respiration rate range threshold. A tissue irregularity complication may be determined when a respiration rate measurement is above such range threshold. For example, a heart rate variability score measured by a heart rate sensing system may be compared against a heart rate variability score range threshold [1023]. The Examiner interprets the measured values from the sensors as readings.)
Although Shelton discloses establishing a baseline for data, it does not disclose this data being for the application of blood perfusion metrics. However, this modification of the type of data being used as baseline data still provides the same improvements of ensuring the analysis properly determines changes to the blood perfusion of the patient prior to and/or following an intervention (see Applicant’s disclosure, ¶ 0029). Since each individual element and its function are shown in the prior art, albeit in different embodiments, this practice of utilizing blood perfusion metrics as a pre-interventions reading for establishing a baseline is well known in the art and would be obvious to try. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Shelton does not disclose the following limitations met by Griffin:
the second reading is a post-intervention reading for establishing a current reading… (Griffin teaches health metrics for the user may be measured and/or determined during a monitoring period to evaluate how effective the interventions …were in changing the user's health profile. For example, if the user was determined to have a significant health risk for a disease, health metrics that are pertinent to that disease would be monitored after an intervention is provided to the user to determine how effective the intervention was in changing the health risk of the user for that disease [0013].)
and the intervention recommendation is a follow-up intervention recommendation that includes a recommended course of treatment, a predicted intervention perfusion status indicative of a predicted … status result of the medical intervention within a time period following the medical intervention, or combinations thereof. (Griffin teaches trends such as readmittance may be used to determine effectiveness of medical interventions and/or whether further medical interventions may assist in preventing future readmittances [0046]. The patient profile may be monitored to determine an outcome and/or intermediate changes to one or more health metrics related to the medical condition of the patient as a result of the intervention provided at block 504….the changes may be evaluated to determine trends in the health metrics such as increasing, decreasing, or remaining the same. The intervention and monitored changes may be useful as part of training examples to re-train a machine learning model for further intervention decisions [0079]. The Examiner interprets a further intervention as a follow-up intervention.)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for using a wearable garment to obtain blood perfusion metrics and analyzing the data to assess a patient’s blood perfusion with a machine learning model as disclosed by Shelton to incorporate generating a follow-up intervention as taught by Griffin. This modification would create a system and method capable of identifying risks and enabling a timely intervention to improve an individual’s health (see Griffin, ¶ 0045).
Regarding Claims 13 and 18, these claims recite limitations that are substantially similar to those recited in Claim 5 above; thus, the same rejection applies.
Regarding Claim 6, Shelton and Griffin teach the limitations as seen in the rejection of Claim 1 above. Shelton further discloses:
the machine-readable instructions cause the processor to perform operations further comprising: …with the individual wearing the wearable garment from the plurality of sensors; (Shelton discloses the wearable sensing system may be or may include a wristband patient sensing system, an ingestible pill patient sensing system,…an instrumented socks patient sensing system, etc. [1136]. The Examiner interprets instrumented socks as wearable garments. Measurement data related to a set of patient biomarkers for post-surgical monitoring may be received. For example, a computing system may be configured to receive the measurement data from one or more sensing systems. A sensing system may be or may include a patient wearable device. A sensing system may include one or more sensors [1097].)
Shelton does not disclose training the machine learning model by comparing the recommendation and metric which is met by Griffin:
receiving a third set of … metrics associated with the individual… generating a third reading as a follow-up intervention reading after generation of the intervention…and based on the third set of … metrics; (Griffin teaches subsequent to the intervention, the patient profile may be monitored to determine an outcome and/or intermediate changes to one or more health metrics related to the medical condition of the patient as a result of the intervention provided at block 504 [0079].)
and training the machine learning model based on a comparison of the intervention recommendation and the third reading to improve subsequent follow-up intervention recommendations. (Griffin teaches the patient profile may be monitored to determine an outcome and/or intermediate changes to one or more health metrics related to the medical condition of the patient as a result of the intervention provided at block 504. For example, if the intervention was a referral to a specialist, the patient profile will be monitored to determine whether the patient scheduled and attends a visit with the specialist and whether the patient's medical condition improves after the visit. In one or more embodiments, the changes may be evaluated to determine trends in the health metrics such as increasing, decreasing, or remaining the same. The intervention and monitored changes may be useful as part of training examples to re-train a machine learning model for further intervention decisions [0079].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for using a wearable garment to obtain blood perfusion metrics and analyzing the data to assess a patient’s blood perfusion with a machine learning model as disclosed by Shelton to incorporate receiving a set of metrics following an intervention and training the machine learning model based on a comparison of the recommendation and the third reading as taught by Griffin. This modification would create a system and method capable of identifying risks and enabling a timely intervention to improve an individual’s health (see Griffin, ¶ 0045).
Regarding Claims 14 and 19, these claims recite limitations that are substantially similar to those recited in Claim 6 above; thus, the same rejection applies.
Regarding Claim 7, Shelton and Griffin teach the limitations as seen in the rejection of Claim 1 above. Shelton does not disclose the following limitations met by Griffin:
before generating the intervention recommendation, receiving a historical data set including prior blood perfusion metrics, prior interventions, prior intervention statuses, or combinations thereof from a plurality of individuals. (Griffin teaches the data sets represent a historical representation of all the medical care that an individual has received (e.g. data collected from claims made by the individual) along with real-time events (e.g. inpatient census datafiles), which can be combined with the individual's electronic health record as well as monitored information like vitals [0086]. The Examiner interprets previous medical care that an individual has received as prior interventions.)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for using a wearable garment to obtain blood perfusion metrics and analyzing the data to assess a patient’s blood perfusion with a machine learning model as disclosed by Shelton to incorporate generating an intervention recommendation by receiving historical data including prior interventions as taught by Griffin. This modification would create a system and method capable of identifying risks and enabling a timely intervention to improve an individual’s health (see Griffin, ¶ 0045).
Regarding Claim 8, Shelton and Griffin teach the limitations as seen in the rejection of Claim 7 above. Shelton further discloses:
the machine-readable instructions cause the processor to perform operations further comprising, (Shelton discloses predictions of complications and/or recovery milestones may be generated, for example, by one or more machine learning (ML) models, such as predictive models, trained to make predictions after being trained on training data [1123].)
Shelton does not disclose the following limitations met by Griffin:
before generating the intervention recommendation, training the machine learning model based on the historical data set. (Griffin teaches the large volume of data from disparate sources allows for optimal training opportunities for machine learning models to generate more effective decisions in the future. Consequently, machine intelligence performed on the enhanced pool of data may improve over time [0054]. At block 508, the machine learning models may be trained (e.g., re-trained, updated). The interventions and the monitored outcomes for patients may be used as new training data examples for the machine learning models to improve how interventions are determined. For example, if an intervention is proven to be effective in treating a patient with a high-risk for emergency room visits, the machine learning models may use data related to that intervention and the positive outcome as a training example for future interventions for other patients who are at a high-risk for emergency room visits [0085].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for using a wearable garment to obtain blood perfusion metrics and analyzing the data to assess a patient’s blood perfusion with a machine learning model as disclosed by Shelton to incorporate using historical data to train the model as taught by Griffin. This modification would create a system and method capable of identifying risks and enabling a timely intervention to improve an individual’s health (see Griffin, ¶ 0045).
Regarding Claim 15, Shelton and Griffin teach the limitations as seen in the rejection of Claim 14 above. Shelton does not disclose the following limitation met by Griffin:
before generating the intervention recommendation: receiving a historical data set including prior blood perfusion metrics, prior interventions, prior intervention statuses, or combinations thereof from a plurality of individuals; (Griffin teaches the data sets represent a historical representation of all the medical care that an individual has received (e.g. data collected from claims made by the individual) along with real-time events (e.g. inpatient census datafiles), which can be combined with the individual's electronic health record as well as monitored information like vitals [0086]. The Examiner interprets previous medical care that an individual has received as prior interventions.)
and training the machine learning model based on the historical data set to generate intervention status predictions based on blood perfusion metrics, interventions, intervention statuses, or combinations thereof. (Griffin teaches the large volume of data from disparate sources allows for optimal training opportunities for machine learning models to generate more effective decisions in the future. Consequently, machine intelligence performed on the enhanced pool of data may improve over time [0054]. At block 508, the machine learning models may be trained (e.g., re-trained, updated). The interventions and the monitored outcomes for patients may be used as new training data examples for the machine learning models to improve how interventions are determined. For example, if an intervention is proven to be effective in treating a patient with a high-risk for emergency room visits, the machine learning models may use data related to that intervention and the positive outcome as a training example for future interventions for other patients who are at a high-risk for emergency room visits [0085].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for using a wearable garment to obtain blood perfusion metrics and analyzing the data to assess a patient’s blood perfusion with a machine learning model as disclosed by Shelton to incorporate generating an intervention recommendation by receiving historical data including prior interventions and using this to train the model as taught by Griffin. This modification would create a system and method capable of identifying risks and enabling a timely intervention to improve an individual’s health (see Griffin, ¶ 0045).
Regarding Claim 20, this claim recites limitations that are substantially similar to those recited in Claim 15 above; thus, the same rejection applies.
Claims 3 and 11 are rejected under 35 USC § 103 as being unpatentable over Shelton and Griffin in view of Freckleton et al. (US 20220296847 A1).
Regarding Claim 3, Shelton and Griffin teach the limitations as seen in the rejection of Claim 1 above. Shelton and Griffin do not teach the following limitations met by Freckleton:
the plurality of sensors are positioned on the wearable garment such that the plurality of sensors are positioned adjacent to the individual when the wearable garment is worn by the individual. (Freckleton teaches the wearable device 100 can be operably engaged with the user via a wearable clothing item (e.g. shirt, pants, shorts, compression sleeve, sock, ring, watch, hat, helmet, patch, etc.) [0037]. The wearable device 100 can include a power supply, such as a battery, to supply power to one or more of the sensors 125, 135, 175 and/or other components in the wearable device 100. In at least one instance, the sensor 125 can be have a skin contact area of approximately 3.5 inches×2 inches. In other instances, the wearable device 100 can be sized to be on the user's wrist so that there is a skin contact area of approximately 1 inch×1 inch [0048].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for using a wearable garment to obtain blood perfusion metrics and analyzing the data to assess a patient’s blood perfusion with a machine learning model as disclosed by Shelton to incorporate the wearable garment having sensors adjacent to the individual as taught by Freckleton. This modification would create a system and method which can provide a user with physiological information whether an intervention is successful (see Freckleton, ¶ 0034).
Regarding Claim 11, this claim recites limitations that are substantially similar to those recited in Claim 3 above; thus, the same rejection applies.
Relevant Art Not Currently Being Applied
The following references are considered pertinent to Applicant’s disclosure but are not currently being applied:
Connors et al. (US 20210077023 A1) teaches a microcirculation assessment device which is a wearable garment with sensors to determine information about a patient’s perfusion level.
Looi et al. (US 20210030283 A1) teaches a system measuring blood perfusion using sensors and determining if an intervention is likely to succeed.
Kostense et al. (US 20230263482 A1) teaches a device which analyzes health input data and recommends an action to take to extend the user’s life using machine learning while updating the model with the most recent data.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OLIVIA R GEDRA whose telephone number is (571)270-0944. The examiner can normally be reached Monday - Friday 8:00am-5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter H Choi can be reached at (469)295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/OLIVIA R. GEDRA/Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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