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 07/27/2026.
Claims 1, 9, 11, 14, and 16 have been amended.
Claims 21-23 have been added.
Claims 1-23 are currently pending and have been examined.
This action is made final.
Claim Objections
Claim 1 is objected to for stating “a second set of blood perfusion metrics… such that the first set of blood perfusion metrics are from the first and second regions”. Based on the claim language of the other independent claims 9 and 16, Examiner will interpret the limitation as “such that the second set of blood perfusion metrics”.
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 1-23 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, 10-15, and 21-23 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, one or more of the plurality of sensors being positioned on the wearable garment so as to correspond to a first region of the individual wearing the wearable garment,
one or more of the plurality of sensors being positioned on the wearable garment so as to correspond to a different, second region of the individual wearing 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 such that the first set of blood perfusion metrics are from the first and second regions;
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 such that the first set of blood perfusion metrics are from the first and second regions;
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 within each of the first and second regions;
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.
Independent Claim 9 further recites the following:
a sock comprising a plurality of sensors for assessing blood perfusion when the individual is wearing the sock,
one or more of the plurality of sensors being positioned on the sock so as to correspond to a first region of the individual wearing the sock,
one or more of the plurality of sensors being positioned on the sock so as to correspond to a different, second region of the individual wearing the sock:
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 from a second region, 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. Claim 21 recites the first region is a forefoot, midfoot, hindfoot, or calf of the individual wearing the wearable garment, and the second region is the forefoot, midfoot, hindfoot, or calf of the individual wearing the wearable garment. Claim 22 recites the data includes changes to blood perfusion of the individual. Claim 23 recites the first reading includes a first regional difference of the blood perfusion between the first and second regions, and the second reading includes a second regional difference of the blood perfusion between the first and second regions. 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. Claim 21 recites the previously recited wearable garment and specifies the wearable garment is a sock and where the garment is located on the patient. Claim 22 recites the previously recited sensors and machine learning model and specifies data collected by the sensors is used as training inputs to the machine learning model to generate a recommendation. 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, 17-18, and 23 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. Claim 23 recites the first reading includes a first regional difference of the blood perfusion between the first and second regions, and the second reading includes a second regional difference of the blood perfusion between the first and second regions.
Dependent Claims 3, 6-8, 11, 14-15, and 19-22 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. Claim 21 recites the previously recited wearable garment and specifies the wearable garment is a sock and where the garment is located on the patient. Claim 22 recites the previously recited sensors and machine learning model and specifies data collected by the sensors is used as training inputs to the machine learning model to generate a recommendation, and the data includes changes to blood perfusion of the individual. Hence, Claims 2-8, 10-15, and 17-22 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 1-23 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, 12-20, and 22-23 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) and Zherebtsov et al. (Zherebtsov et al., "Novel wearable VCSEL-based sensors for multipoint measurements of blood perfusion", Proc. SPIE 10877, Dynamics and Fluctuations in Biomedical Photonics XVI, 1087708 (Mar 2019); (Year: 2019)).
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;…such that the first set of blood perfusion metrics… (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…such that 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).
Shelton and Griffin do not teach the sensors being in two regions which is met by Zherebtsov:
one or more of the plurality of sensors being positioned on the wearable garment so as to correspond to a first region of the individual wearing the wearable garment, one or more of the plurality of sensors being positioned on the wearable garment so as to correspond to a different, second region of the individual wearing the wearable garment;… from the first and second regions;… are from the first and second regions; (Zherebtsov teaches sensors were attached symmetrically without applying any pressure on the study area: 2 on a palmar surface of the middle fingers distal phalanges and 2 on a dorsal side of the wrists. Blood perfusion signals were recorded continuously for 10 minutes (p. 2, ¶ 0005-6). The Examiner interprets the sensors on the palmar surface as the first region and sensors on the dorsal side of the wrists as the second region.)
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 collecting the blood perfusion data from two different regions as taught by Zherebtsov. This modification would create a system and method capable of increasing the diagnostic value of blood perfusion measurements (see Zherebtsov, p. 3, ¶ 0003).
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].)
…a sock comprising a plurality of sensors… (Shelton discloses the measurement data may be received from one or more patient sensing systems, such as a wristband patient sensing system, an ingestible pill patient sensing system, an ultra-thin catheter patient sensing system, an instrumented socks patient sensing system, and/or the like [1110].)
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, Griffin, and Zherebtsov 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, Griffin, and Zherebtsov 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, Griffin, and Zherebtsov 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, Griffin, and Zherebtsov 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, Griffin, and Zherebtsov 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, Griffin, and Zherebtsov 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, Griffin, and Zherebtsov 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.
Regarding Claim 22, Shelton, Griffin, and Zherebtsov teach the limitations as seen in the rejection of Claim 1 above. Shelton further discloses:
data collected by 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, peripheral temperature, oxygen saturation, blood sugar level, hydration state, and/or the like [0968].)
the data including changes in the blood perfusion of the individual… (Shelton discloses the tissue perfusion sensing system may illuminate skin and measure the light transmitted and reflected to detect changes in blood flow [0229].)
Shelton does not disclose the following limitations met by Griffin:
data collected…is used as training inputs to the machine learning model to generate the intervention recommendation, (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. 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].)
…changes in the [data]…prior to, during, and/or following the medical intervention (Griffin teaches Changes in monitored health metric(s) for a patient profile may be used to provide additional interventions. 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].)
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 data collected being used as training data for a machine learning model and changes in the data being collected after an 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).
Shelton and Griffin do not teach the following limitations met by Zherebtsov:
…within each of the first and second regions. (Zherebtsov teaches sensors were attached symmetrically without applying any pressure on the study area: 2 on a palmar surface of the middle fingers distal phalanges and 2 on a dorsal side of the wrists. Blood perfusion signals were recorded continuously for 10 minutes (p. 2, ¶ 0005-6).)
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 collecting the blood perfusion data from two different regions as taught by Zherebtsov. This modification would create a system and method capable of increasing the diagnostic value of blood perfusion measurements (see Zherebtsov, p. 3, ¶ 0003).
Regarding Claim 23, Shelton, Griffin, and Zherebtsov teach the limitations as seen in the rejection of Claim 1 above. Shelton and Griffin do not teach the following limitations met by Zherebtsov:
the first reading includes a first regional difference of the blood perfusion between the first and second regions, and the second reading includes a second regional difference of the blood perfusion between the first and second regions. (Zherebtsov teaches sensors were attached symmetrically without applying any pressure on the study area: 2 on a palmar surface of the middle fingers distal phalanges and 2 on a dorsal side of the wrists. Blood perfusion signals were recorded continuously for 10 minutes. As can be seen from Fig. 1 a high level of blood perfusion rhythms synchronisation has been observed in the fingers. By the simultaneous measurements we have the potential to distinguish system and local impacts of the vascular regulation. A similar result is demonstrated in Fig. 2, which shows an example of synchronous registration of blood perfusion in the fingers and wrists (p. 2, ¶ 0005-6).)
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 collecting the blood perfusion data from two different regions and providing an indication of regional differences in the blood perfusion as taught by Zherebtsov. This modification would create a system and method capable of increasing the diagnostic value of blood perfusion measurements (see Zherebtsov, p. 3, ¶ 0003).
Claims 3 and 11 are rejected under 35 USC § 103 as being unpatentable over Shelton, Griffin, and Zherebtsov in view of Freckleton et al. (US 20220296847 A1).
Regarding Claim 3, Shelton, Griffin, and Zherebtsov teach the limitations as seen in the rejection of Claim 1 above. Shelton, Griffin, and Zherebtsov 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.
Claim 21 is rejected under 35 USC § 103 as being unpatentable over Shelton, Griffin, and Zherebtsov in view of Dervish et al. (US 20190000177 A1).
Regarding Claim 21, Shelton, Griffin, and Zherebtsov teach the limitations as seen in the rejection of Claim 1 above. Shelton further discloses:
the wearable garment is a sock,… (Shelton discloses the measurement data may be received from one or more patient sensing systems, such as a wristband patient sensing system, an ingestible pill patient sensing system, an ultra-thin catheter patient sensing system, an instrumented socks patient sensing system, and/or the like [1110].)
Shelton, Griffin, and Zherebtsov do not teach the following limitations met by Dervish:
the first region is one of a forefoot of a foot of the individual wearing the wearable garment, a midfoot of the foot of the individual wearing the wearable garment, a hindfoot of the foot of the individual wearing the wearable garment, and a calf of the individual wearing the wearable garment, and the second region is another of the forefoot of the foot of the individual wearing the wearable garment, the midfoot of the foot of the individual wearing the wearable garment, the hindfoot of the foot of the individual wearing the wearable garment, and the calf of the individual wearing the wearable garment. (Dervish teaches a plurality of…sensors distributed throughout the sole or inner sole comprising: a first lateral row of…sensors in the toe region; a second lateral row of…sensors in the forefoot region; a first longitudinal row of… sensors along the inner side of the sole or inner sole from the heel region to the midfoot region; and a second longitudinal row of…sensors along the outer side of the sole or inner sole from the heel region to the midfoot region [0005].)
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 sensors being placed on the forefoot and the hindfoot as taught by Dervish. This modification would create a system which can generate necessary metrics at the correct points of the foot (see Dervish, ¶ 0003-4).
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.
Kumar et al. (Kumar, M., Suliburk, J.W., Veeraraghavan, A. et al. PulseCam: a camera-based, motion-robust and highly sensitive blood perfusion imaging modality. Sci Rep 10, 4825 (2020). (Year: 2020)) teaches a multi-sensor blood perfusion imaging modality.
Response to Arguments
Regarding rejections under 35 USC 101 to Claims 1-23, Applicant’s arguments have been considered but are not persuasive. The rejection has been updated in light of the amendments above.
Applicant argues claims 1, 9, and 16 are do not recite certain methods of organizing human activities. Claims 1, 9, and 16 are directed to a wearable garment/sock/device that includes a plurality of sensors affixed to the wearable garment/sock/device at particular locations on the wearable device such that the sensors correspond to a first region of an individual wearing the wearable device and sensors correspond to a different, second region of the individual wearing the wearable device. The claims recite collecting blood perfusion metrics using those sensors, processing the collected blood perfusion metrics, and generating an intervention recommendation on whether additional intervention is recommended based on the processed blood perfusion metrics. The claimed subject matter does not relate to fundamental economic principles or practices, commercial or legal interactions, or managing personal behavior or relationships or interactions between people. Rather, the claims are directed to the acquisition, and processing, and use of blood perfusion metrics using a particular sensor arrangement implemented in a physical and wearable garment/sock/device. To assert the contrary would be improperly expanding the subgroupings (see Applicant’s disclosure, p. 11-12).
Regarding (a), Examiner respectfully disagrees. Examiner firstly notes that as shown in the rejection above, the sensors and the wearable device are not identified as being a part of the abstract idea and instead are identified as additional elements. The human interaction subgroup “managing personal behavior or relationships or interactions between people” would include receiving metrics, generating reading based on the metrics, determining an intervention perfusion status, and generating an intervention recommendation, as these are all abstract steps which can be carried out by a person following a simple set of rules or instructions. It is important to note that the text within the parentheses stating social activities, teaching, and following rules or instructions are provided as examples and not an exclusive listing and that the October 2019 Update: Subject Matter Eligibility on p. 5 states certain activity between a person and a computer may fall within the “certain methods of organizing human activity” grouping.
Applicant argues the Office should consider "the claim as a whole. That is, the limitations containing the judicial exception as well as the additional elements in the claim besides the judicial exception need to be evaluated together to determine whether the claim integrates the judicial exception into a practical application." As recited in MPEP 2106.04(d) (I), "limitations the courts have found indicative that an additional element (or combination of elements) may have integrated the exception into a practical application include: [a]n improvement in the functioning of a computer, or an improvement to other technology or technical field." The claims include additional elements that integrate the abstract idea into a practical application because they provide an improvement to blood perfusion assessments (p. 12-13).
Regarding (b), Examiner respectfully disagrees. Firstly, Examiner notes that blood perfusion analysis is not a technical problem. That is because the concept of analyzing blood perfusion metrics to determine an outcome or a recommendation for management has existed since long before the advent of computer technology and thus cannot properly be considered a technological improvement and/or an improvement to the computer itself.
Applicant argues claims 1, 9, and 16 include a placement of sensors in defined regions which is not arbitrary. The sensors are dedicated to obtaining regional measurements that allow the device to identify localized blood perfusion metrics and distinctions between different regions. The application explains that this use of a plurality of region specific sensors “may aid in minimizing error and improving a signal-to-noise ratio” and that measurements from the sensor clusters “can improve the accuracy and reliability of the device.” (¶ 0028 of specification) (p.13).
Regarding (c), Examiner respectfully disagrees. Examiner notes that the section being referenced from the specification does not discuss how the placement of the regions provides any improvement, only the use of “a plurality of sensors” as a generic term, not a specific arrangement of the sensors. Further, the improved “accuracy and reliability of the device” comes from the sensors taking a combination of vitals (i.e., pulse oximetry as well as heart rate, temperature, etc.) and the specification clearly indicates that it is this variety and combination of vitals that provides the accuracy. Such limitations are not claimed in the independent claims.
Applicant argues that the claimed arrangement enables functionality that would not be otherwise possible. Specifically, the wearable garment/sock/device is capable of detecting regional differences in blood perfusion across different regions of the individual wearing the wearable garment/sock/device. For example, one region may exhibit critical blood perfusion while another region exhibits poor or good blood perfusion [0028]. Paragraph [0032] explains that the wearable device may determine regional blood perfusion differences between the different regions to identify a pattern of degradation in blood perfusion across those regions. These measurements and patterns can be used to determine interventional status (improved, not improved, etc.), such as during a PAD procedure, thereby also improving procedure success. There is a particular implementation involving a wearable garment/sock/device having sensors positioned at specific locations on the wearable device so as to coincide with particular regions of an individual wearing the wearable device and the processing of the region-specific blood perfusion metrics to generate improved blood perfusion assessments. Accordingly, any alleged abstract idea of claims 1, 9, and 16 is integrated into a practical application that improves the field of blood perfusion assessments (see p. 13-14).
Regarding (d), Examiner respectfully disagrees. Examiner firstly notes that there is nothing in the present specification that indicates identifying patterns. Furthermore, Examiner notes that the determination of the regional difference is not claimed in the independent claims, nor is the specifics of the locations for the sensors. Both of these limitations are claimed in dependent claims which are both separately dependent on independent claim 1. The claimed limitations in the independent claims are still recited at a high level of generality such that there are no details regarding the argued sensor particularity.
Applicant argues that the background of the application describes how diseases such as peripheral artery disease (PAD) may reduce blood perfusion to the legs and, occasionally, the arms, leading to complications such as gangrene and amputation (Para. [0002]). Moreover, current methods of treating PAD are not straightforward, with typical measurements such as ABI (ankle- brachial index) being helpful to diagnose but not particularly helpful in providing spatial resolution on potential perfusion problem areas. The success of PAD treatment is often subjective, leading to improper or omitted follow-up intervention. The claims solve this technological problem using a technological solution of "a plurality of sensors affixed to the wearable garment, one or more of the plurality of sensors being positioned on the wearable garment so as to correspond to a first region of the individual wearing the wearable garment, one or more of the plurality of sensors being positioned on the wearable garment so as to correspond to a different, second region of the individual wearing the wearable garment," "the first set of blood perfusion metrics being from the first and second regions," "the second set of blood perfusion metrics being from the first and second regions, [and] determining an intervention perfusion status for improving blood perfusion for the individual based on the first reading and the second reading and indicative of a level of blood perfusion improvement within each of the first and second regions" Accordingly, spatial resolution of the potential perfusion problem areas may be better identified, leading to better intervention procedures. That is, the claim elements particularly tie any alleged abstract idea into a practical application which is directly related to improving the technical field of perfusion detection and medical diagnostics. This is similar to Ex Parte Howard Austerlitz, where sensors being at two different heights was found to integrate an abstract idea into a practical application by improving the field of measuring fuel levels. (p. 14-15).
Regarding (e), Examiner respectfully disagrees. Examiner firstly notes that there is nothing in the present specification that indicates identifying patterns. Furthermore, Examiner notes that the determination of the regional difference is not claimed in the independent claims, nor is the specifics of the locations for the sensors. Both of these limitations are claimed in dependent claims which are both separately dependent on independent claim 1. The claimed limitations in the independent claims are still recited at a high level of generality such that there are no details regarding the argued sensor particularity. Regarding Ex Parte Howard Austerlitz, Examiner notes that the fact patterns of this case are not the same as the instant application and the cases are not analogous. In Austerlitz, the sensors are arranged in a specific manner such that the improvement to the tank is apparent. Conversely, the instant claims do not identify where the two sensors are located and how sensors function to provide an improvement. Examiner notes that the locations of the sensors and the determining of regional differences are not claimed in the independent claims.
Regarding rejections under 35 USC 103 to Claims 1-23, Applicant’s arguments have been considered and are persuasive. Therefore the rejection has been withdrawn. However, in light of the amendments, a new has been made, rejecting Claim 1 over Shelton in view of Griffin and Zherebtsov.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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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