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 .
Response to Amendment
Applicant has submitted amendments to the claims on 07/21/2026.
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.
Claim(s) 1-24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mallon et al (US PUB. 20120259648, herein Mallon) in further view of Bissonnette et al (US PUB. 20220016485, herein Bissonnette).
Regarding claim 1, Mallon teaches A computer-implemented method comprising, using one or more processing devices:
receiving a plurality of sets of data corresponding to respective users of a plurality of users undergoing rehabilitation treatment, wherein the treatment uses the respective treatment devices, and wherein each of the sets of data includes sensor data obtained while the respective users perform respective treatment plans and the plurality of sets of data is received in real-time or near real-time as the respective users perform the respective treatment plans (0031 “motion sensors, physiologic sensors, or both are methodically tested and correlated with specific activities, exercises, movements, intended outcomes, etc., in order to determine optimized sensor combinations for each activity, exercise and/or movement, and in certain embodiments, for each individual or certain groups of individuals. The sensors are configured to wirelessly communicate with a gateway, which in turn communicates directly or via a smartphone with a remote server. The gateway can be a dedicated device, a multipurpose device such as a smartphone, or it can be a dedicated device configured to be attached to the smartphone. For example, in the physical therapy arena, the sensors can detect when a patient has properly setup and completed an exercise and can transmit such data to the remote server via the gateway”);
storing, as objective information, the plurality of sets of data in a queryable data format (0016 “a database configured to store prescription and treatment information, sensor data, subject information, reporting, and billing information for a plurality of subjects; a server coupled with the database and the communication interface, the server configured to: receive subject data via the communication interface, the subject data comprising a subject identifier identifying a particular subject and diagnosis information, create a record in the database for the subject using the subject identifier and the diagnosis information, receive sensor data via the communication interface, the sensor data including data related to certain activities, exercises or movements performed by the subject, analyze the sensor data to establish a baseline assessment, and develop at least partially a treatment plan based on the sensor data and the baseline assessment and subject data”);
receiving first sensor data associated with the performance, by a first user using a first treatment device, of a first treatment plan, wherein the first treatment plan corresponds to a standardized treatment plan assigned to the user based on a classification of the user, and wherein the first sensor data correlates with the objective information stored in the queryable data format (0047 “a patient or individual can be outfitted with one or more sensors designed to monitor the activities, exercises and movements they perform. The sensors can then communicate data related to the activities, exercises and movements, as well as other types of data, to server 102 where it can be stored and analyzed and where reports and messages can be generated, treatment plans assessed and modified, and billing performed as needed”);
providing the objective information and the first sensor data to a machine learning model trained to predict, based on the objective information and the first sensor data, a likelihood of the first user achieving a rehabilitation goal associated with the rehabilitation treatment (0124 “algorithms 116, which can increase the predictive ability of algorithms 116. Thus, algorithms 116 can be configured to mine data for a plurality of individuals and to assess patterns, trends, correlations, etc., and to apply them to the data being received from sensors 202 for a given patient or individual. Algorithms 116 can then use this analysis to predict or forecast results for the patient or individual, to predict or forecast required changes, modifications, etc., to the treatment plan; to determine progress; to determine whether the patient or individual is ready to advance to another level; etc.”);
generating, based on the objective information, the first sensor data, and the likelihood of the first user achieving the rehabilitation goal, a second treatment plan (0124 “algorithms 116, which can increase the predictive ability of algorithms 116. Thus, algorithms 116 can be configured to mine data for a plurality of individuals and to assess patterns, trends, correlations, etc., and to apply them to the data being received from sensors 202 for a given patient or individual. Algorithms 116 can then use this analysis to predict or forecast results for the patient or individual, to predict or forecast required changes, modifications, etc., to the treatment plan; to determine progress; to determine whether the patient or individual is ready to advance to another level; etc.”), wherein the second treatment plan specifies at least one modified operating parameter of the first treatment device (0089 “sensors 202 can provide movement data indicative of range of motion, number of movements, timing, etc.; strength, e.g., pressure or exertion; physiological function e.g., heart rate sensors, blood pressure sensors, perspiration rate sensors, temperature sensors, pain sensors and oxygen sensors to gateway 206. This information can be transmitted to server 102 where various algorithms described in more detail below can use the data to determine, e.g., compliance, technique, treatment progress, and even outcome”, 0123 “algorithms 116 can be trained to identify trends, patterns, etc., in the data, which can allow algorithms 116 to predict future results, problems, progression, etc. This can enable server 102 to suggest and even require and make changes, modifications, adjustments, advancements, etc., automatically in a timely fashion so that the treatment plan can constantly be optimized to meet the patient's or individual's needs and goals. It can also allow server 102 to predict and avoid problems”, 0124 “data for a large population of patients or individuals can be available to server 102 and algorithms 116, which can increase the predictive ability of algorithms 116. Thus, algorithms 116 can be configured to mine data for a plurality of individuals and to assess patterns, trends, correlations, etc., and to apply them to the data being received from sensors 202 for a given patient or individual. Algorithms 116 can then use this analysis to predict or forecast results for the patient or individual, to predict or forecast required changes, modifications, etc., to the treatment plan; to determine progress; to determine whether the patient or individual is ready to advance to another level; etc”);
and controlling, based on the second treatment plan, the first treatment device (0129 “a patient or individual at remote location 106 performing various activities, exercises or movements in accordance with his or her treatment plan can receive a feedback message in auditory, textual or image form on or through, e.g., gateway 207 that the patient or individual is not performing the exercises correctly. This feedback message can be provided directly from server 102 by comparing the patient's or individual's incoming data from sensors 202 against his or her currently prescribed treatment plan or historic or population data stored in storage system 110. In some embodiments, the sensors 202 themselves can be configured to provide an indication to the server 102 that the patient or individual is performing the exercises incorrectly--such as if the sensors 202 send satisfactory signals 204 intermingled with a certain threshold of unsatisfactory signals”, 0126 “a diagram illustrating an example process for analyzing the sensor data in accordance with one embodiment. First, in step 902 sensor data is received from sensors 202, e.g., via gateway 207. In step 904, the data can be analyzed to determine compliance with the associated treatment plan. In addition, a determination can be made as to whether the patient or individual is progressing, or regressing (step 910); whether the patient or individual is ready to advance to a next level within the treatment plan (step 912); whether the treatment plan needs to change (step 914); etc. As explained, this can be done by simply analyzing the data in step 906 or, as illustrated in step 908, by comparing the data to past data for the patient or individual, or a combination of steps 906 and 908.”).
The cited prior art do not teach wherein controlling the first treatment device causes the first treatment device to operate according to the at least one modified operating parameter.
Bissonnette teaches wherein controlling the first treatment device causes the first treatment device to operate according to the at least one modified operating parameter (0006 “a method is disclosed for using an artificial intelligence engine to perform a control action. The control action is based on one or more measurements from a wearable device. The method includes generating, by the artificial intelligence engine, a machine learning model trained to receive the one or more measurements as input, and outputting, based on the one or more measurements, a control instruction that causes the control action to be performed”, 0007 “a control instruction that causes the exercise device to modify, independently from each other, the resistance of the one or more pedals. The method includes, while a user performs an exercise using the exercise device, receiving the one or more measurements from the one or more sensors associated with the one or more pedals of the exercise device, and determining, based on the one or more measurements, a quantifiable or qualitative modification to the resistance provided by a pedal of the one or more pedals. In one embodiment, the resistance provided by another pedal of the one or more pedals is not modified. The method includes transmitting the control instruction to the exercise device to cause the resistance provided by the pedal to be modified”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to have modified the teachings of Mallon with the teachings of Bissonnette since Bissonnette teaches a means for “control system may be used to improve compliance with an exercise plan” (0096).
Regarding claim 2, the cited prior art teach The computer-implemented method of claim 1.
Mallon teaches wherein the objective information is stored based on diagnostic requirements associated with the rehabilitation treatment (0016 0017).
Regarding claim 3, the cited prior art teach The computer-implemented method of claim 1.
Mallon teaches wherein the objective information is stored in a standardized or canonical format (0090).
Regarding claim 4, the cited prior art teach The computer-implemented method of claim 1.
Mallon teaches further comprising: generating a prompt in response to a determination that a component of the sensor data exceeds a threshold; determining, based on the objective information and the sensor data, a cause of the component exceeding the threshold; and based on the determined cause, at least one of (i) controlling the first treatment device to mitigate the cause and (ii) providing an instruction to the first user (0129 “in some embodiments, system 100 includes the ability to provide feedback and or messaging to and/or between interested parties 104, 106, 112, 120, etc. For example, a patient or individual at remote location 106 performing various activities, exercises or movements in accordance with his or her treatment plan can receive a feedback message in auditory, textual or image form on or through, e.g., gateway 207 that the patient or individual is not performing the exercises correctly. This feedback message can be provided directly from server 102 by comparing the patient's or individual's incoming data from sensors 202 against his or her currently prescribed treatment plan or historic or population data stored in storage system 110. In some embodiments, the sensors 202 themselves can be configured to provide an indication to the server 102 that the patient or individual is performing the exercises incorrectly--such as if the sensors 202 send satisfactory signals 204 intermingled with a certain threshold of unsatisfactory signals”)
Regarding claim 5, the cited prior art teach The computer-implemented method of claim 4.
Mallon teaches further comprising providing the prompt to a user interface associated with a medical professional and receiving, via the user interface, at least one of (i) control parameters for the first treatment device and (ii) the instruction to the first user (0034 “"treatment plan" is intended to refer to any kind of activity, movement or exercise plan provided to an individual by a supervisor such as a physical therapist, occupational therapist, physician, personal trainer, coach, wellness expert, etc. Thus, while the terms "prescribed treatment" and "prescribed treatment plan" can refer to a treatment or plan that is associated with a prescription from a healthcare provider, it can also simply refer to an exercise regimen or routine provided by an, e.g., personal trainer.”, 0059-0060).
Regarding claim 6, the cited prior art teach The computer-implemented method of claim 1.
Mallon teaches further comprising: receiving qualitative information corresponding to the respective users; converting the qualitative information to second objective information; and storing the second objective information in the queryable data format (0098 “database 110 can store algorithms or applications that enable server 102 to generate feedback, instruction, intervention, or compliance messaging as well as targeted advertising that can be sent to the patient or individual, healthcare provider, insurance provider, etc., as well as to manage billing, payments and reimbursement. FIGS. 4-9 and the descriptions that follow describe some of the processes that can be carried out by server 102 using algorithms 116 as well as detailed examples of movement management and monitoring the can be performed within system 100”, 0099 “baseline assessment can involve the creation of an avatar that can then be used later on for visual instruction, feedback and motivation as well as final stage assessment. A patient or individual can be required to visit a supervised location 104 for baseline assessment. For example, in the case of physical therapy, a prescribing healthcare provider, e.g., a physician can provide a "prescription" for physical therapy to the patient in his or her office for a specific, individualized treatment plan for the patient or the patient can take it to another healthcare provider, e.g., a physical therapist, whom then develops a specific, individualized treatment plan for the patient. In other words, the, e.g., physician or physical therapist can evaluate a patient and develop a specific, individualized treatment plan based upon the patient's particular circumstances”).
Regarding claim 7, the cited prior art teach The computer-implemented method of claim 6.
Mallon teaches further comprising determining a correlation between the qualitative information and the second objective information and converting, based on the correlation, the qualitative information to the second objective information (0055 “device 205 can be configured to not only aggregate the data but to also correlate the data, e.g., from different sensors, based on time stamps or other information included in the data. Device 205 can also be configured to filter the data, and to identify critical information, such alarm conditions, most relevant data, etc. It should be noted that device 205 can comprise multiple antenna 220, receiver/transmitters 222, or both in order to aggregate data from multiple motion sensors 202 some of which can be transmitting in one particular frequency band, such as the ISM Band in the 902 to 928 MHz frequency band, while others are transmitting in another frequency band or using a different protocol, such as Bluetooth data, which operates in the 2.4 GHz range. It will also be understood that multiple antennas or receivers can be used to provide diversity to improve reception”, 0124 “algorithms 116 can be configured to mine data for a plurality of individuals and to assess patterns, trends, correlations, etc., and to apply them to the data being received from sensors 202 for a given patient or individual. Algorithms 116 can then use this analysis to predict or forecast results for the patient or individual, to predict or forecast required changes, modifications, etc., to the treatment plan; to determine progress; to determine whether the patient or individual is ready to advance to another level; etc”).
Regarding claim 8, the cited prior art teach The computer-implemented method of claim 7.
Mallon teaches wherein the qualitative information corresponds to pain levels reported by the respective users (0065 “combinations of sensors can be deployed for use in conjunction with the systems and methods described herein. For example, various motion sensors, strength sensors, physiologic sensors such as heart rate sensors, blood pressure sensors, skin conductance or perspiration rate sensors, temperature sensors, pain sensors and oxygen sensors, etc., e.g., for detecting blood oxygen level, cameras, etc., can be worn, held, attached or tethered to the patient or individual, or external to the patient or users”).
Claims 9-24 are rejected using similar reasoning as the rejection of claims 1-8 due to reciting similar limitations but directed towards a system and a computer readable non-transitory media.
Response to Arguments
Applicant’s arguments, filed 07/21/2026, with respect to the rejection(s) of claim(s) 1 under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Mallon et al (US PUB. 20120259648, herein Mallon) in further view of Bissonnette et al (US PUB. 20220016485, herein Bissonnette).
Applicant argues on page 9 that Mallon does not teach claimed input are provided to a trained machine learning model to predict a likelihood of achieving a rehab goal. Examiner disagrees. Mallon teaches a algorithms which use inputs such as sensor data to predict the future results, problems and progression of the user (0123-0124, 0089).
Applicant argues on page 10 that the cited prior art does not teach controlling the first treatment device causes the first treatment device to operate according to the at least one modified operating parameter. Examiner agrees. However, as a result of further search and consideration, Bissonnette has been introduced. Bissonnette teaches control instructions that change exercise device variables based on measurements which are used in an artificial intelligence to determine the control instructions (0006-0007).
Therefore, claim 1 and its dependent claims are rejected. Claims 9-24 are rejected using similar reasoning as the rejection of claims 1-8.
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.
/TAMEEM D SIDDIQUEE/
Primary Examiner
Art Unit 2116
Prosecution Insights