DETAILED ACTION
Applicant’s arguments, filed on 07/09/2025, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Applicants have amended their claims, filed on 07/09/2025, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Claims1,3-8,10-11 and 13-20 are the current claims hereby under examination.
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 .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “the controller” in claim 10. This limitation invokes 112(f) based on the three-prong analysis:
Regarding “the controller” limitation in claim 10:
(A) “configured with” is a generic placeholder for “means”
(B) The functional language that modifies “the controller” is the steps of “a machine learning model, the machine learning model is trained using training data collected from a plurality of patients other than the user”
(C) “configured with” is not modified by a sufficient structure for performing the claimed functions, therefore 35 U.S.C. 112(f) is invoked.
The corresponding structure for “the controller” in claim 10 configured to use a machine learning model trained using training data from a plurality of patients other than the user requires disclosure of an algorithm for performing the claimed specific function. In other words, for a special purpose computer-implemented means-plus-function limitation, the structure required is more than simply a general-purpose computer or processor and the specification must disclose an algorithm for performing the claimed function. The requisite structure and algorithm is recited in paragraphs [0062]-[0068] in the specification, as well as being illustrated in Figure 7.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3 and 4 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 3, the claim recites the limitation “at least one sensor”. It is unclear if this refers to the first sensor, the second sensor, the plurality of sensors from claim 1, or a different sensor. If it is referring to any of the sensors from claim 1, it needs to refer back to it. If it is referring to a different sensor, it needs to be distinguished. For purposes of examination, it is being interpreted as referring to any of the sensors from claim 1.
Regarding claim 4, the claim recites the limitation “at least one sensor”. It is unclear if this refers to the first sensor, the second sensor, the plurality of sensors from claim 1, or a different sensor. If it is referring to any of the sensors from claim 1, it needs to refer back to it. If it is referring to a different sensor, it needs to be distinguished. For purposes of examination, it is being interpreted as referring to any of the sensors from claim 1.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3-4, 6-7, 11, 14, 16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over JP ‘382 (JP 2020507382) in further view of Bajaj (US Patent 10076286).
Regarding independent claim 1, JP ‘382 teaches a system for monitoring health of a user ([0002]: “The present invention also relates to a system and method for monitoring joint physical therapy and rehabilitation.”), the system comprising:
a plurality of sensors including a first sensor implanted in the user ([0019]: “a sensor module including sensors for monitoring patient rehabilitation in accordance with the present invention”; [0024]: “one or more of the sensors 102, and optionally a sensor processor, may be implanted in the patient”) and a second sensor worn by the user ([0022]: “The system includes a wearable device equipped with one or more sensors”);
a communication unit electronically coupled with the plurality of sensors and receiving data therefrom ([0009]: “The system includes at least one sensor unit configured and arranged to be placed on or in a patient, the sensor unit including at least two accelerometers in combination with a communication device”); and
a controller electronically coupled with the communication unit ([0021]: “The system includes one or more sensors that can communicate with a processor that can generate information based on the sensor readings and sensor data that allows the patient or another user, such as a clinician, physician, physical therapist, nurse, care coordinator, or other suitable person, to easily monitor the patient's activity, the condition of the orthopedic implant or surrounding tissue, or the effectiveness of rehabilitation or other therapy”), the controller configured to:
perform predictive analytics using a combination of the first data from the first sensor and second data from the second sensor ([0012]: “The processor is configured and arranged to perform operations including: a) predicting an average pivot point distance for a plurality of movements; b) acquiring a pivot point distance using at least two accelerometers of at least one sensor unit while performing one of the movements; c) identifying the movement being performed using the predicted average pivot point distance; and d) refining a predicted average pivot point distance for the movement being performed using the acquired pivot point distance.”),
determine parameters of the user based upon the predictive analytics ([0019]: “1 is a flowchart of one embodiment of a method for identifying or classifying movement using pivot point distance, according to the present invention”; [0106]: “Pivot point determination can also enhance understanding of a patient's anatomy, for example by providing an estimate of femur or tibia length using pivot point measurements for the ankle, knee, or hip. In some embodiments, more accurate femur or tibia length may improve gait, stride length analysis, lameness detection, or fall detection/classification.”; [0113]: “These results may indicate the success of the exercise execution graphically or numerically (see, for example, area 1392d) as well as the degree of compliance with the rehabilitation activity (such as the number of exercise repetitions performed). The location of such information can facilitate monitoring of a patient's progress, identifying progress or lack of progress, identifying areas of concern (such as an increase in temperature or an increase in the number of shocks or impacts), and the like.”), and
output information to at least one of the user or a health care provider for use by the user to preemptively address one or more health conditions prior to or in lieu of a surgical intervention for the user ([0006]: “the patient device includes a display, a tilt sensor, a memory, and a processor coupled to the display and the memory”).
However, JP ‘382 does not teach determine if first data from the first sensor is sufficient to perform predictive analytics, instruct the user to wear the second sensor if the first data is insufficient to perform the predictive analytics, and determine a personalized intervention recommendation.
Bajaj discloses a method and device using sensors to measure health parameters for characterizing a circadian rhythm. Specifically, Bajaj teaches determine if first data from the first sensor is sufficient to perform predictive analytics (Fig. 4; Column 11, lines 25-50: “The server may, on the other hand, analyze the initial data and determine that the initial data is insufficient to characterize the circadian rhythm of the wearer. In this respect, this analysis of the initial data can serve to calibrate the wearable device and the server to determine whether the metrics being monitored by the device are sufficient or appropriate for providing an indication of circadian rhythms of the wearer. In particular, the server may evaluate the data collected by the wearable device to determine the type, timing and frequency of data that the wearable device must collect in order to provide information on the wearer's circadian rhythms. Therefore, the one or more instructions determined by the server may be configured to cause the wearable device to collect additional data comprising one or more additional physiometric measurements over an additional period of time. The one or more instructions may further be configured to cause the wearable device to collect the one or more additional physiometric measurements using a different frequency of measurement as compared to the one or more initial physiometric measurements. Alternatively, the one or more instructions may further be configured to cause the wearable device to collect the one or more additional physiometric measurements using a revised set of one or more sensors of the wearable device. The revised set of one or more sensors may differ from the initial set of one or more sensors.”), and determine a personalized intervention recommendation (Column 8, lines 47-49: “the instructions may be for the controller 270 to provide one or more alerts or recommendations to the wearer via the interface”). JP ‘382 and Bajaj are analogous arts as they both relate to methods and devices using multiple sensors for monitoring a health condition of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the steps of determining which sensor data to use for the predictive analysis from Bajaj into the system from JP ‘382, as it allows the system to determine which sensor is providing the most sufficient, complete measurements so the predictive analysis is complete and accurate. Additionally, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the personalized intervention recommendation from Bajaj into the system from JP ‘382 as it allows the system to provide a recommendation to the user, which can help them improve their health condition.
However, the JP ‘382/Bajaj combination does not teach the step of instruct the user to wear the second sensor if the first data is insufficient to perform the predictive analytics.
Tanaka discloses a biological measurement device. Specifically, Tanaka teaches the step of instruct the user to wear the second sensor if the first data is insufficient to perform the predictive analytics ([0192]: “In a case where a solution cannot be found, it is possible to instruct the user to wear the device again”). JP ‘382, Bajaj, and Tanaka are analogous arts as they all relate to methods and devices using sensors for monitoring a health condition of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the instruction to wear the device from Tanaka into the system from JP ‘382 as it ensures that the user puts on the wearable device before the measurements are taken, which makes sure that the measurements are correct and are being taken accurately.
Regarding claim 3, the JP ‘382/Bajaj/Tanaka combination teaches the system of claim 1, wherein the at least one sensor has a small form factor and is implanted with a minimally invasive procedure and/or the one or more of the plurality of sensors comprises an inertial measurement unit (JP ‘382, [0036]: “The sensors 102 may be of any suitable type, including but not limited to, an accelerometer, a magnetometer, a gyroscope, a proximity sensor, an infrared sensor, an ultrasonic sensor, a thermistor or other temperature sensor, a camera, a piezoelectric or other pressure sensor, a sonar sensor, an external fluid sensor, a skin color change sensor, a pH sensor, or a microphone, or the like, or any combination thereof.”).
Regarding claim 4, the JP ‘382/Bajaj/Tanaka combination teaches the system of claim 1, wherein the at least one sensor has an active sensing component implanted at one of a location proximal of a knee joint of the user or distal of the knee joint of the user, wherein the at least one sensor includes a passive component that is implanted in another of the location proximal the knee joint or distal of the knee joint (JP ‘382, [0079]: “the sensor unit 422 may include a button, mechanical switch, or other mechanism for placing the sensor in an active mode, or an inactive or standby mode, or for switching modes, or for turning the sensor unit on or off”; [0006]: “The processor is configured and arranged to perform operations including determining the tilt angle of the patient device using a tilt angle sensor on the patient's thigh, obtaining the tilt angle of a sensor unit positioned on or within a portion of the patient's leg below the knee, and determining extension or flexion of the patient's knee using the tilt angle of the patient device and the tilt angle of the sensor unit.”), and wherein the at least one sensor measures a gap between the active sensing component and the passive component (JP ‘382, [0010]: “obtaining the pivot point distance includes obtaining at least one position value from each of two of the accelerometers and calculating the pivot point distance from the obtained position values.”).
Regarding claim 6, the JP ‘382/Bajaj/Tanaka combination teaches the system of claim 1, wherein the communication unit electronically shares the personalized intervention recommendation with the health care provider based upon input from the user (JP ‘382, [0021]: “The system includes one or more sensors that can communicate with a processor that can generate information based on the sensor readings and sensor data that allows the patient or another user, such as a clinician, physician, physical therapist, nurse, care coordinator, or other suitable person, to easily monitor the patient's activity, the condition of the orthopedic implant or surrounding tissue, or the effectiveness of rehabilitation or other therapy”).
Regarding claim 7, the JP ‘382/Bajaj/Tanaka combination teaches the system of claim 1, wherein the controller is located on a mobile device and the personal intervention recommendation is displayed on the mobile device (JP ‘382, [0019]: “1 is a diagram of one embodiment of a user interface of a mobile device displaying information obtained from a sensor unit, in accordance with the present invention. 1 is a diagram of one embodiment of a user interface of a mobile device displaying range of motion measurements in accordance with the present invention. FIG. 1 is a diagram of one embodiment of a user interface of a mobile device displaying a summary of exercise repetitions, in accordance with the present invention. 1 is a diagram of one embodiment of a user interface of a mobile device displaying information obtained from a sensor unit, in accordance with the present invention”).
Regarding independent claim 11, JP ‘382 teaches method for monitoring health of a user ([0002]: “The present invention also relates to a system and method for monitoring joint physical therapy and rehabilitation.”), the method comprising:
receiving, with a networked electronic controller ([0021]: “The system includes one or more sensors that can communicate with a processor that can generate information based on the sensor readings and sensor data that allows the patient or another user, such as a clinician, physician, physical therapist, nurse, care coordinator, or other suitable person, to easily monitor the patient's activity, the condition of the orthopedic implant or surrounding tissue, or the effectiveness of rehabilitation or other therapy”), first data from a first sensor worn by or implanted in the user ([0019]: “a sensor module including sensors for monitoring patient rehabilitation in accordance with the present invention”; [0024]: “one or more of the sensors 102, and optionally a sensor processor, may be implanted in the patient”);
receiving, with the networked electronic controller, second data from the second sensor ([0022]: “The system includes a wearable device equipped with one or more sensors”);
performing the predictive analytics using the first data or a combination of the first data and the second data ([0012]: “The processor is configured and arranged to perform operations including: a) predicting an average pivot point distance for a plurality of movements; b) acquiring a pivot point distance using at least two accelerometers of at least one sensor unit while performing one of the movements; c) identifying the movement being performed using the predicted average pivot point distance; and d) refining a predicted average pivot point distance for the movement being performed using the acquired pivot point distance.”);
determine parameters of the user based upon the predictive analytics ([0019]: “1 is a flowchart of one embodiment of a method for identifying or classifying movement using pivot point distance, according to the present invention”; [0106]: “Pivot point determination can also enhance understanding of a patient's anatomy, for example by providing an estimate of femur or tibia length using pivot point measurements for the ankle, knee, or hip. In some embodiments, more accurate femur or tibia length may improve gait, stride length analysis, lameness detection, or fall detection/classification.”; [0113]: “These results may indicate the success of the exercise execution graphically or numerically (see, for example, area 1392d) as well as the degree of compliance with the rehabilitation activity (such as the number of exercise repetitions performed). The location of such information can facilitate monitoring of a patient's progress, identifying progress or lack of progress, identifying areas of concern (such as an increase in temperature or an increase in the number of shocks or impacts), and the like.”); and
providing information to one of the user or a health care provider for use by the user to preemptively address one or more health conditions prior to or in lieu of a surgical intervention for the user ([0006]: “the patient device includes a display, a tilt sensor, a memory, and a processor coupled to the display and the memory”).
However, JP ‘382 does not teach determine if first data from the first sensor is sufficient to perform predictive analytics, instruct the user to wear the second sensor if the first data is insufficient to perform the predictive analytics, and determine a personalized intervention recommendation.
Bajaj discloses a method and device using sensors to measure health parameters for characterizing a circadian rhythm. Specifically, Bajaj teaches determining with the networked electronic controller, if the first data from the sensor provides sufficient data to perform predictive analytics (Fig. 4; Column 11, lines 25-50: “The server may, on the other hand, analyze the initial data and determine that the initial data is insufficient to characterize the circadian rhythm of the wearer. In this respect, this analysis of the initial data can serve to calibrate the wearable device and the server to determine whether the metrics being monitored by the device are sufficient or appropriate for providing an indication of circadian rhythms of the wearer. In particular, the server may evaluate the data collected by the wearable device to determine the type, timing and frequency of data that the wearable device must collect in order to provide information on the wearer's circadian rhythms. Therefore, the one or more instructions determined by the server may be configured to cause the wearable device to collect additional data comprising one or more additional physiometric measurements over an additional period of time. The one or more instructions may further be configured to cause the wearable device to collect the one or more additional physiometric measurements using a different frequency of measurement as compared to the one or more initial physiometric measurements. Alternatively, the one or more instructions may further be configured to cause the wearable device to collect the one or more additional physiometric measurements using a revised set of one or more sensors of the wearable device. The revised set of one or more sensors may differ from the initial set of one or more sensors.”), and determining a personalized intervention recommendation (Column 8, lines 47-49: “the instructions may be for the controller 270 to provide one or more alerts or recommendations to the wearer via the interface”). JP ‘382 and Bajaj are analogous arts as they both relate to methods and devices using multiple sensors for monitoring a health condition of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the steps of determining which sensor data to use for the predictive analysis from Bajaj into the method from JP ‘382, as it allows the method to determine which sensor is providing the most sufficient, complete measurements so the predictive analysis is complete and accurate. Additionally, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the personalized intervention recommendation from Bajaj into the system from JP ‘382 as it allows the system to provide a recommendation to the user, which can help them improve their health condition.
However, the JP ‘382/Bajaj combination does not teach the step of instruct the user to wear the second sensor if the first data is insufficient to perform the predictive analytics.
Tanaka discloses a biological measurement device. Specifically, Tanaka teaches the step of instructing the user to wear a second sensor if the first data is insufficient to perform the predictive analytics ([0192]: “In a case where a solution cannot be found, it is possible to instruct the user to wear the device again”). JP ‘382, Bajaj, and Tanaka are analogous arts as they all relate to methods and devices using sensors for monitoring a health condition of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the instruction to wear the device from Tanaka into the method from JP ‘382 as it ensures that the user puts on the wearable device before the measurements are taken, which makes sure that the measurements are correct and are being taken accurately.
Regarding claim 14, the JP ‘382/Bajaj/Tanaka combination teaches the method of claim 11, further comprising measuring a gap at a knee joint of the user with the first sensor, wherein the first sensor includes an active sensing component and a passive component, wherein the active sensing component is implanted in one of a location proximal of the knee joint or distal of the knee joint, and wherein the passive component is implanted in another of the location proximal of the knee joint or distal of the knee joint (JP ‘382, [0079]: “the sensor unit 422 may include a button, mechanical switch, or other mechanism for placing the sensor in an active mode, or an inactive or standby mode, or for switching modes, or for turning the sensor unit on or off”; [0006]: “The processor is configured and arranged to perform operations including determining the tilt angle of the patient device using a tilt angle sensor on the patient's thigh, obtaining the tilt angle of a sensor unit positioned on or within a portion of the patient's leg below the knee, and determining extension or flexion of the patient's knee using the tilt angle of the patient device and the tilt angle of the sensor unit.”; [0010]: “obtaining the pivot point distance includes obtaining at least one position value from each of two of the accelerometers and calculating the pivot point distance from the obtained position values.”).
Regarding claim 16, the JP ‘382/Bajaj/Tanaka combination teaches the method of claim 11, further comprising electronically sharing the personalized intervention recommendation with the health care provider with approval from the user (JP ‘382, [0021]: “The system includes one or more sensors that can communicate with a processor that can generate information based on the sensor readings and sensor data that allows the patient or another user, such as a clinician, physician, physical therapist, nurse, care coordinator, or other suitable person, to easily monitor the patient's activity, the condition of the orthopedic implant or surrounding tissue, or the effectiveness of rehabilitation or other therapy”).
Regarding claim 18, the JP ‘382/Bajaj/Tanaka combination teaches the method of claim 11, wherein the determining personalized intervention recommendation for the user using the predictive analytics includes selecting a machine learning model that is based upon one or more of an overall health of the user including any morbidity conditions, a fatigue level of the user, a pain level identified by the user, a range of motion of the user, a user exercise score, a user progress towards a goal defined by the user, a body orientation of the user based upon at least the first sensor, or a measured physiological characteristic of the user (JP ‘382, [0086]: “Utilizing machine learning tools to improve the accuracy of the sensor data evaluation over time provides a good measure of success, including updating the system with new training data sets”; [0039]: “One or more sensors (e.g., accelerometers, gyroscopes, magnetometers, or proximity sensors) may also sense, detect, or calculate the range of motion of a sensor, joint, or other body part of the patient, or the flexion of a joint.
This can be used, for example, to monitor a patient's rehabilitation, their activity, monitor compliance with an exercise regimen, or monitor possible signs of pain or other conditions that may hinder or aid rehabilitation. These or other sensors may be used to monitor the impact or effect on the orthopedic device or on the tissue surrounding the orthopedic device. These sensor data can also be used to monitor changes in range of motion or flexion, or trends in range of motion or flexion”; [0109]: “the user interface allows the user to set a goal, such as a number of steps or exercise repetitions over a particular period of time (e.g., 1 hour, 2 hours, 4 hours, 6 hours, or 7 hours, or a day or a week). The user interface can also display the current status towards achieving these goals. The user interface may also highlight significant events such as maximum steps or maximum exercise repetitions, elevated temperature readings, increased impact or shock counts, etc. The user interface may also highlight the achievement of goals.”; [0037]: “Examples of observations or measurements that can be made or interpreted using one or more of these sensors include step counts, movement repetitions, repetition of joint movements (e.g., joint rotations), type of movement or other activity being performed, stability or lack thereof, flexion angle or range of motion, movement speed, skin temperature, pulse or pulse profile or heart rate recovery time after activity, ultrasound imaging, flow or Doppler measurements, sonar imaging, flow or Doppler measurements, pressure or load bearing measurements, detection of limp or changes in body orientation (e.g., subluxation, posture, scoliosis) or body orientation, monitoring of joint impacts or influences, sleep profile or rest time, gait analysis, or body/limb/joint alignment. The system 100 may observe or measure one or more of these items, or any combination of these items.”).
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over the JP ‘382/Bajaj/Tanaka combination as applied to claims 1 and 11 above, and further in view of Donnelly (US PG Pub 20210282652).
Regarding claim 5, the JP ‘382/Bajaj/Tanaka combination teaches the system of claim 1, wherein one or more of the plurality of sensors are mounted on a wearable device (JP ‘382, [0022]: “The system includes a wearable device equipped with one or more sensors”).
However, the JP ‘382/Bajaj/Tanaka combination does not teach the wearable device that applies a therapy to the user.
Donnelly discloses a wearable medical monitoring device. Specifically, Donnelly teaches a wearable device that applies a therapy to the user ([0002]: “At least one embodiment of the present invention relates generally to a wearable therapeutic device, and more specifically, to a wearable therapeutic device configured to monitor or treat a subject.”; [0005]: “At least one aspect is directed to a wearable treatment device. The treatment device includes a cardiac sensing electrode, a treatment electrode, a user interface, and a sensor.”; [0006]: “The method determines that treatment is to be applied to the subject based upon the cardiac information, and adjusts the treatment based on at least one of the detected subject activity and wellness information and the quality of life information.”). JP ‘382, Bajaj, Tanaka, and Donnelly are analogous arts as they all relate to methods and devices using sensors for monitoring a health condition of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the device applying a therapy to the user from Donnelly into the system from the JP ‘382/Bajaj/Tanaka combination as it allows the device not only to monitor the patient’s health condition, but provide a treatment if needed.
Regarding claim 15, the JP ‘382/Bajaj/Tanaka combination teaches the method of claim 11.
However, the JP ‘382/Bajaj/Tanaka combination does not teach further comprising applying a therapy to the user with a device worn by the user.
Donnelly teaches further comprising applying a therapy to the user with a device worn by the user ([0002]: “At least one embodiment of the present invention relates generally to a wearable therapeutic device, and more specifically, to a wearable therapeutic device configured to monitor or treat a subject.”; [0005]: “At least one aspect is directed to a wearable treatment device. The treatment device includes a cardiac sensing electrode, a treatment electrode, a user interface, and a sensor.”; [0006]: “The method determines that treatment is to be applied to the subject based upon the cardiac information, and adjusts the treatment based on at least one of the detected subject activity and wellness information and the quality of life information.”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the device applying a therapy to the user from Donnelly into the method from the JP ‘382/Bajaj/Tanaka combination as it allows the method not only to monitor the patient’s health condition, but provide a treatment if needed.
Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over the JP ‘382/Bajaj/Tanaka combination as applied to claims 1 and 11 above, and further in view of Tran (US PG Pub 20130095459).
Regarding claim 8, the JP ‘382/Bajaj/Tanaka combination teaches the system of claim 1, wherein the communication unit, at a behest of the user, electronically and anonymously shares the personalized intervention recommendation with a network of devices (JP ‘382, [0034]: “The sensor processor 104 is configured to execute instructions given to it. The sensor processor 104 is configured to receive sensor data from the sensor(s) and communicate with a patient device 106, a network 60, a clinician device 108, or any combination thereof”).
However, the JP ‘382/Bajaj/Tanaka combination does not teach the controller via communication with the network of devices pairs the user with a second user having a similar personalized intervention recommendation.
Tran discloses a health monitoring system. Specifically, Tran teaches the controller via communication with the network of devices pairs the user with a second user having a similar personalized intervention recommendation ([0477]: “the one or more rules described herein can be defined based on the information received from the one or more content providers, electronic medical record of the user 1348, historic treatments provided to different patients having similar (or substantially similar or same habits)”; [0486]: “The rules specify the recommendations for the user 1348 based on the user habits information and based on the historic information of the one or more users having similar or same habits.”). JP ‘382, Bajaj, Tanaka, and Tran are analogous arts as they all relate to methods and devices using sensors for monitoring a health condition of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the pairing from Tran into the JP ‘382/Bajaj/Tanaka combination as it allows the system to compare the user’s information with another user that has similar conditions and recommendations, which can provide more information to the user about their health condition and a user similar to them.
Regarding claim 17, the JP ‘382/Bajaj/Tanaka combination teaches the method of claim 11, further comprising: at a behest of the user, electronically and anonymously sharing the personalized intervention recommendation with a network of devices (JP ‘382, [0034]: “The sensor processor 104 is configured to execute instructions given to it. The sensor processor 104 is configured to receive sensor data from the sensor(s) and communicate with a patient device 106, a network 60, a clinician device 108, or any combination thereof”).
However, the JP ‘382/Bajaj/Tanaka combination does not teach pairing the user with a second user having a similar personalized intervention recommendation based upon communication with the network of devices.
Tran discloses a health monitoring system. Specifically, Tran teaches pairing the user with a second user having a similar personalized intervention recommendation based upon communication with the network of devices ([0477]: “the one or more rules described herein can be defined based on the information received from the one or more content providers, electronic medical record of the user 1348, historic treatments provided to different patients having similar (or substantially similar or same habits)”; [0486]: “The rules specify the recommendations for the user 1348 based on the user habits information and based on the historic information of the one or more users having similar or same habits.”). JP ‘382, Bajaj, Tanaka, and Tran are analogous arts as they all relate to methods and devices using sensors for monitoring a health condition of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the pairing from Tran into the JP ‘382/Bajaj/Tanaka combination as it allows the method to compare the user’s information with another user that has similar conditions and recommendations, which can provide more information to the user about their health condition and a user similar to them.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over the JP ‘382/Bajaj/Tanaka combination as applied to claim 1 above, and further in view of Soykan (US PG Pub 20200352441).
Regarding claim 10, the JP ‘382/Bajaj/Tanaka combination teaches the system of claim 1, wherein the controller is configured with a machine learning model (JP ‘382, [0086]: “Utilizing machine learning tools to improve the accuracy of the sensor data evaluation over time provides a good measure of success, including updating the system with new training data sets”), and wherein the machine learning model that is based upon one or more of an overall health of the user including any morbidity conditions, a fatigue level of the user, a pain level identified by the user, a range of motion of the user, a user exercise score, a user progress towards a goal defined by the user, a body orientation of the user based upon at least the first sensor, or a measured physiological characteristic of the user (JP ‘382, [0039]: “One or more sensors (e.g., accelerometers, gyroscopes, magnetometers, or proximity sensors) may also sense, detect, or calculate the range of motion of a sensor, joint, or other body part of the patient, or the flexion of a joint. This can be used, for example, to monitor a patient's rehabilitation, their activity, monitor compliance with an exercise regimen, or monitor possible signs of pain or other conditions that may hinder or aid rehabilitation. These or other sensors may be used to monitor the impact or effect on the orthopedic device or on the tissue surrounding the orthopedic device. These sensor data can also be used to monitor changes in range of motion or flexion, or trends in range of motion or flexion”; [0109]: “the user interface allows the user to set a goal, such as a number of steps or exercise repetitions over a particular period of time (e.g., 1 hour, 2 hours, 4 hours, 6 hours, or 7 hours, or a day or a week). The user interface can also display the current status towards achieving these goals. The user interface may also highlight significant events such as maximum steps or maximum exercise repetitions, elevated temperature readings, increased impact or shock counts, etc. The user interface may also highlight the achievement of goals.”; [0037]: “Examples of observations or measurements that can be made or interpreted using one or more of these sensors include step counts, movement repetitions, repetition of joint movements (e.g., joint rotations), type of movement or other activity being performed, stability or lack thereof, flexion angle or range of motion, movement speed, skin temperature, pulse or pulse profile or heart rate recovery time after activity, ultrasound imaging, flow or Doppler measurements, sonar imaging, flow or Doppler measurements, pressure or load bearing measurements, detection of limp or changes in body orientation (e.g., subluxation, posture, scoliosis) or body orientation, monitoring of joint impacts or influences, sleep profile or rest time, gait analysis, or body/limb/joint alignment. The system 100 may observe or measure one or more of these items, or any combination of these items.”).
However, the JP ‘382/Bajaj/Tanaka combination is silent on what type of data makes up the training data.
Soykan discloses a method and device for monitoring physiological signals. Specifically, Soykan teaches the machine learning model is trained using training data collected from a plurality of patients other than the user ([0097]: “The machine learning algorithms may, in some implementations, use both patient-specific information and population-based data to generate recommendations of medication changes for the physician to consider and prescribe.”). JP ‘382, Bajaj, Tanaka, and Soykan are analogous arts as they all relate to methods and devices using sensors for monitoring a health condition of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to use the training data from Soykan in the JP ‘382/Bajaj/Tanaka combination as the combination is silent on what data is used in the training data, and Soykan discloses suitable training data in an analogous art.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over the JP ‘382/Bajaj/Tanaka combination as applied to claim 11 above, and further in view of Kabrams (WO 2020123954).
Regarding claim 13, the JP ‘382/Bajaj/Tanaka combination teaches the method of claim 11.
However, the JP ‘382/Bajaj/Tanaka combination is silent on the implantation procedure.
Kabrams discloses a system and method to monitor the brain of a user. Specifically, Kabrams teaches further comprising implanting the first sensor during a minimally invasive procedure (Page 4: “the sensor and the transducer are disposed on the head of the person in a minimally-invasive manner”). JP ‘382, Bajaj, Tanaka, and Kabrams are analogous arts as they all relate to methods and devices using sensors for monitoring a health condition of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the minimally invasive procedure from Kabrams into the method from the JP ‘382/Bajaj/Tanaka combination, as the JP ‘382/Bajaj/Tanaka combination is silent on the implantation procedure and Kabrams teaches a suitable implantation method in an analogous method.
Claims 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over JP ‘382 in further view of Bajaj.
Regarding independent claim 19, JP ‘382 teaches a system for monitoring health of a user ([0002]: “The present invention also relates to a system and method for monitoring joint physical therapy and rehabilitation.”), the system comprising:
a first sensor implanted in the user and generating first data ([0019]: “a sensor module including sensors for monitoring patient rehabilitation in accordance with the present invention”; [0024]: “one or more of the sensors 102, and optionally a sensor processor, may be implanted in the patient”);
a second sensor worn by the user and generating second data ([0022]: “The system includes a wearable device equipped with one or more sensors”);
a communication unit electronically coupled with the first sensor and the second sensor receiving the first data and the second data ([0009]: “The system includes at least one sensor unit configured and arranged to be placed on or in a patient, the sensor unit including at least two accelerometers in combination with a communication device”); and
a controller electronically coupled with the communication unit ([0021]: “The system includes one or more sensors that can communicate with a processor that can generate information based on the sensor readings and sensor data that allows the patient or another user, such as a clinician, physician, physical therapist, nurse, care coordinator, or other suitable person, to easily monitor the patient's activity, the condition of the orthopedic implant or surrounding tissue, or the effectiveness of rehabilitation or other therapy”), the controller configured to:
perform predictive analytics using at least one of the first data or the second data ([0012]: “The processor is configured and arranged to perform operations including: a) predicting an average pivot point distance for a plurality of movements; b) acquiring a pivot point distance using at least two accelerometers of at least one sensor unit while performing one of the movements; c) identifying the movement being performed using the predicted average pivot point distance; and d) refining a predicted average pivot point distance for the movement being performed using the acquired pivot point distance.”);
determine, based on the predictive analytics, information of the user for use by the user to preemptively address one or more health conditions prior to or in lieu of a surgical intervention for the user ([0019]: “1 is a flowchart of one embodiment of a method for identifying or classifying movement using pivot point distance, according to the present invention”; [0106]: “Pivot point determination can also enhance understanding of a patient's anatomy, for example by providing an estimate of femur or tibia length using pivot point measurements for the ankle, knee, or hip. In some embodiments, more accurate femur or tibia length may improve gait, stride length analysis, lameness detection, or fall detection/classification.”; [0113]: “These results may indicate the success of the exercise execution graphically or numerically (see, for example, area 1392d) as well as the degree of compliance with the rehabilitation activity (such as the number of exercise repetitions performed). The location of such information can facilitate monitoring of a patient's progress, identifying progress or lack of progress, identifying areas of concern (such as an increase in temperature or an increase in the number of shocks or impacts), and the like.”).
However, JP ‘382 does not teach determine if one of the first data or the second data comprises sufficient data to perform the predictive analytics thereon or if one of the first data or the second data is insufficient to perform the predictive analytics thereon, if one of the first data or the second data is insufficient, combine the first data with the second data to perform the predictive analytics, and determine a personalized intervention recommendation.
Bajaj discloses a method and device using sensors to measure health parameters for characterizing a circadian rhythm. Specifically, Bajaj teaches determine if one of the first data or the second data comprises sufficient data to perform the predictive analytics thereon or if one of the first data or the second data is insufficient to perform the predictive analytics thereon, if one of the first data or the second data is insufficient, combine the first data with the second data to perform the predictive analytics (Fig. 4; Column 11, lines 25-50: “The server may, on the other hand, analyze the initial data and determine that the initial data is insufficient to characterize the circadian rhythm of the wearer. In this respect, this analysis of the initial data can serve to calibrate the wearable device and the server to determine whether the metrics being monitored by the device are sufficient or appropriate for providing an indication of circadian rhythms of the wearer. In particular, the server may evaluate the data collected by the wearable device to determine the type, timing and frequency of data that the wearable device must collect in order to provide information on the wearer's circadian rhythms. Therefore, the one or more instructions determined by the server may be configured to cause the wearable device to collect additional data comprising one or more additional physiometric measurements over an additional period of time. The one or more instructions may further be configured to cause the wearable device to collect the one or more additional physiometric measurements using a different frequency of measurement as compared to the one or more initial physiometric measurements. Alternatively, the one or more instructions may further be configured to cause the wearable device to collect the one or more additional physiometric measurements using a revised set of one or more sensors of the wearable device. The revised set of one or more sensors may differ from the initial set of one or more sensors.”), and determine a personalized intervention recommendation (Column 8, lines 47-49: “the instructions may be for the controller 270 to provide one or more alerts or recommendations to the wearer via the interface”). JP ‘382 and Bajaj are analogous arts as they both relate to methods and devices using multiple sensors for monitoring a health condition of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the steps of determining which sensor data to use for the predictive analysis from Bajaj into the system from JP ‘382, as it allows the system to determine which sensor is providing the most sufficient, complete measurements so the predictive analysis is complete and accurate. Additionally, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the personalized intervention recommendation from Bajaj into the system from JP ‘382 as it allows the system to provide a recommendation to the user, which can help them improve their health condition.
Regarding claim 20, the JP ‘382/Bajaj combination teaches the system of claim 19, wherein the controller is configured to manage collection and transmission of the first data and the second data by prioritizing or commanding the first sensor or the second sensor to collect the first data or the second data based upon if one of the first data or the second data comprises sufficient data to perform the predictive analytics (Bajaj, Fig. 4; Column 11, lines 25-50: “The server may, on the other hand, analyze the initial data and determine that the initial data is insufficient to characterize the circadian rhythm of the wearer. In this respect, this analysis of the initial data can serve to calibrate the wearable device and the server to determine whether the metrics being monitored by the device are sufficient or appropriate for providing an indication of circadian rhythms of the wearer. In particular, the server may evaluate the data collected by the wearable device to determine the type, timing and frequency of data that the wearable device must collect in order to provide information on the wearer's circadian rhythms. Therefore, the one or more instructions determined by the server may be configured to cause the wearable device to collect additional data comprising one or more additional physiometric measurements over an additional period of time. The one or more instructions may further be configured to cause the wearable device to collect the one or more additional physiometric measurements using a different frequency of measurement as compared to the one or more initial physiometric measurements. Alternatively, the one or more instructions may further be configured to cause the wearable device to collect the one or more additional physiometric measurements using a revised set of one or more sensors of the wearable device. The revised set of one or more sensors may differ from the initial set of one or more sensors.”), wherein the first sensor has an active sensing component implanted at one of a location proximal of a knee joint of the user or distal of the knee joint of the user, wherein the first sensor includes a passive component that is implanted in another of the location proximal the knee joint or distal of the knee joint (JP ‘382, [0079]: “the sensor unit 422 may include a button, mechanical switch, or other mechanism for placing the sensor in an active mode, or an inactive or standby mode, or for switching modes, or for turning the sensor unit on or off”; [0006]: “The processor is configured and arranged to perform operations including determining the tilt angle of the patient device using a tilt angle sensor on the patient's thigh, obtaining the tilt angle of a sensor unit positioned on or within a portion of the patient's leg below the knee, and determining extension or flexion of the patient's knee using the tilt angle of the patient device and the tilt angle of the sensor unit.”), and wherein the first sensor measures a gap between the active sensing component and the passive component (JP ‘382, [0010]: “obtaining the pivot point distance includes obtaining at least one position value from each of two of the accelerometers and calculating the pivot point distance from the obtained position values.”).
Response to Arguments
All of applicant’s argument regarding the rejections and objections previously set forth have been fully considered and are persuasive unless directly addressed subsequently.
Applicant also argues that Bajaj does not teach the limitation of commanding the sensors to collect data based on whether the obtained data is sufficient to perform predictive analysis on, however, as stated in the 103 rejection above, Bajaj does teach this limitation (Fig. 4; Column 11, lines 25-50: “The server may, on the other hand, analyze the initial data and determine that the initial data is insufficient to characterize the circadian rhythm of the wearer. In this respect, this analysis of the initial data can serve to calibrate the wearable device and the server to determine whether the metrics being monitored by the device are sufficient or appropriate for providing an indication of circadian rhythms of the wearer. In particular, the server may evaluate the data collected by the wearable device to determine the type, timing and frequency of data that the wearable device must collect in order to provide information on the wearer's circadian rhythms. Therefore, the one or more instructions determined by the server may be configured to cause the wearable device to collect additional data comprising one or more additional physiometric measurements over an additional period of time. The one or more instructions may further be configured to cause the wearable device to collect the one or more additional physiometric measurements using a different frequency of measurement as compared to the one or more initial physiometric measurements. Alternatively, the one or more instructions may further be configured to cause the wearable device to collect the one or more additional physiometric measurements using a revised set of one or more sensors of the wearable device. The revised set of one or more sensors may differ from the initial set of one or more sensors.”). Additionally, the combination of JP ‘382 and Bajaj together lead to the limitation of collecting data from both the wearable sensor and implantable sensor from JP ‘382 and the process of determining if the data is sufficient for processing, and gathering more data if it is not sufficient from Bajaj. Bajaj also discusses collecting additional data so that proper analysis can be performed, which teaches on the limitation of combining the data for analysis.
Applicant’s arguments with respect to the Tran reference has been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's submission of an information disclosure statement under 37 CFR 1.97(c) with the timing fee set forth in 37 CFR 1.17(p) on 07/09/2025 prompted the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 609.04(b). 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.
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/E.K.M./ Examiner, Art Unit 3791
/ERIC J MESSERSMITH/ Primary Examiner, Art Unit 3791
