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 .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-8 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 20230123383 A1 to Choi et al.
Regarding claim 1, A method for generating exercise program recommendations, comprising: applying a vector search model to a plurality of exercise programs, wherein the vector search model identifies vector features within the plurality of exercise programs; (Choi et al. teaches In one implementation, data analytics platform 1220 may be configured to effectuate various AI/ML-based models or decision engines for purposes of some example embodiments of the present patent disclosure that may involve techniques such as support vector machines (SVMs) or support vector networks (SVNs), pattern recognition, fuzzy logic, neural networks (e.g., ANNs/CNNs), recurrent learning, and the like, as well as unsupervised learning techniques involving untagged data [0114]).
receiving a search query for an exercise program, the search query comprising an exercise program feature; (Choi et al. teaches In some example arrangements, data obtained via remote monitoring, background process(es), baseline queries and/or user-initiated data transfer mechanisms may be (pre)processed or otherwise conditioned in order to generate appropriate datasets that may be used for training, validating and testing one or more AI/ML-based models or engines for purposes of some embodiments [0112]).
applying, based at least in part on the exercise program feature, a machine learning (ML) personalization model to the plurality of exercise programs; (Choi et al. teaches The frequency bands (e.g., low theta, high theta, alpha, beta, and gamma) are employed to create multiple models (e.g., five models) to classify patients as healthy control subjects or patient subjects. Suitable machine learning processing is applied in this process. The machine learning may include application of random forest machine learning modeling. In accordance with some embodiments, machine learning model uses the cortical activity of healthy people and patient’s group as the input [0190]).
and providing a user with an exercise program recommendation from the plurality of exercise programs. (Choi et al. teaches As used herein, a network element, platform or node may be comprised of one or more pieces of network equipment, including hardware and software that communicatively interconnects other equipment on a network (e.g., other network elements, end stations, etc.), and is adapted to host one or more applications or services, more specifically healthcare applications and services, with respect to a plurality of end users (e.g., patients, clinicians, respective authorized agents, and associated client devices) as well as other endpoints such as medical- and/or health-oriented Internet of Medical Things (IoMT) devices/sensors and/or other Industrial loT-based entities [0051]).
Regarding claim 2, the method of claim 1, further comprising: implementing a recommended exercise program associated with the exercise program recommendation at an exercise device. (Choi et al. teaches Further, continued use of a stimulation program or setting over an extended period of time could result in habituation that may reduce the benefits of therapy. Some example embodiments herein may therefore relate to a system and method for providing recommendations/reconfigurations of program settings based on the patient’s usage of the IMD and clinical observations/recommendations, which may facilitate context-sensitive selection of neuromodulation programs/settings [0059]).
Regarding claim 3, the method of claim 1, wherein applying the ML personalization model comprises: applying the ML personalization model based at least in part on the exercise program feature. (Choi et al. teaches the frequency bands (e.g., low theta, high theta, alpha, beta, and gamma) are employed to create multiple models (e.g., five models) to classify patients as healthy control subjects or patient subjects. Suitable machine learning processing is applied in this process. The machine learning may include application of random forest machine learning modeling. In accordance with some embodiments, machine learning models use the cortical activity of healthy people and patient’s group as the input [0190]).
Regarding claim 4, the method of claim 1, wherein applying the ML personalization model comprises: applying the ML personalization model based at least in part on a user exercise program history associated with the user. (Choi et al. teaches In some example arrangements, a virtual clinic may be configured to provide patients and/or clinicians the ability to perform remote therapies using a secure telehealth session. To enhance clinician interaction and evaluation of a patient during a secure telehealth session, example embodiments herein may be configured to provide specific user interface (UI) layouts and controls for clinician programmer devices and/or patient controller devices for facilitating real-time kinematic and/or auditory data analysis, which may be augmented with suitable artificial intelligence (AI) and/or machine learning (ML) techniques (e.g., neural networks, etc.) in some arrangements [0057]).
Regarding claim 5, the method of claim 1, further comprising: generating, by the ML personalization model, the exercise program recommendation based at least in part on identifying a correlation between the vector features and a popularity associated with one or more other users. (Choi et al. teaches Upon use of the AI/ML process to correlate biosignals to pain levels of patients through supervised learning, the patient activity/pain model may be provided in a neurostimulation system such as the systems described herein. In FIG. 24, system 2400 includes an implantable pulse generator for providing a neurostimulation system for the patient. System 2400 includes a wearable device (e.g., a watch device) that generates biosignal data for the patient on an on-going basis [0186]).
Regarding claim 6, the method of claim 1, wherein the vector features are pre-determined vector features, and wherein applying the vector search model comprises: applying the vector search model based at least in part on the pre-determined vector features (Choi et al. teaches Upon use of the AI/ML process to correlate biosignals to pain levels of patients through supervised learning, the patient activity/pain model may be provided in a neurostimulation system such as the systems described herein. In FIG. 24, system 2400 includes an implantable pulse generator for providing a neurostimulation system for the patient. System 2400 includes a wearable device (e.g., a watch device) that generates biosignal data for the patient on an on-going basis [0186]).
Regarding claim 7, the method of claim 1, wherein receiving the search query comprises: receiving the search query from exercise information associated with the user (Choi et al. teaches Challenges involving “Big Data” may include capture, curation, storage, search, sharing, transfer, analysis, and visualization, etc. Because “Big Data” available with respect to patients’ health data, physiological/behavioral data, sensor data gathered from patients and respective ambient surroundings, daily activity data, therapy settings data, health data collected from clinicians, etc. can be on the order of several terabytes to petabytes to exabytes or more, it becomes exceedingly difficult to work with using most relational database management systems for optimizing, ranking and indexing search results in typical environments [0113]).
Regarding claim 8, the method of claim 1, further comprising: determining a set of one or more exercise programs from the plurality of exercise programs, wherein applying the ML personalization model comprises: applying the ML personalization model to the set of one or more exercise programs (Choi et al. teaches The ML/AI algorithms may be utilized to create or provide a predictive model that integrates the signals and patient data collected from sensors and video during performance of the one or more tasks by the patient. The model may output a value that provides an objective or sensor-driven score. A subjective or evaluator-driven score can also be determined based on clinician assessment and patient-reported outcomes (PROs) [0128]).
Claim Rejections - 35 USC § 103
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(s) 9-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US
20230123383 A1 to Choi et al. in view of US 20220068453 A1 to Simpson
Regarding claim 9, Choi et al. teaches the method of claim 8 in which Choi et al. teaches the ML/AI algorithms may be utilized to create or provide a predictive model that integrates the signals and patient data collected from sensors and video during performance of the one or more tasks by the patient. The model may output a value that provides an objective or sensor-driven score. A subjective or evaluator-driven score can also be determined based on clinician assessment and patient-reported outcomes (PROs) [0128]. However, Choi et al. does not teach determining the set of one or more exercise programs based at least in part on an intensity level, a trainer identity, exercise program availability, compatibility with a training plan, uncompleted exercise programs, or any combination thereof. Simpson teaches the method may further include receiving a selection of one of the respective subprograms, such that the user is instructed to perform an exercise activity of a predetermined intensity level and/or duration over a predetermined time period. If the receiving a selection of a program includes receiving a selection of an exercise program [0018]. It also further teaches additionally, where optimizing workouts includes the use of body-worn sensors as noted above with respect to FIG. 46, users may be further motivated by the displayed outputs to modify behavior to continue training regimens and achieve their goals. Thus, use of the sensors described in greater detail below may be advantageously employed to this end [0416]. It would have been obvious in the ordinary skill in the art before the effective date to include a consistent exercise regimen or plan as well as the intensity level of each exercise program in which the machine learning helps personalize a set of exercise programs.
Regarding claim 10, Choi et al. teaches the in one implementation, data analytics platform 1220 may be configured to effectuate various AI/ML-based models or decision engines for purposes of some example embodiments of the present patent disclosure that may involve techniques such as support vector machines (SVMs) or support vector networks (SVNs), pattern recognition, fuzzy logic, neural networks (e.g., ANNs/CNNs), recurrent learning, and the like, as well as unsupervised learning techniques involving untagged data [0114]. It also further teaches The frequency bands (e.g., low theta, high theta, alpha, beta, and gamma) are employed to create multiple models (e.g., five models) to classify patients as healthy control subjects or patient subjects. Suitable machine learning processing is applied in this process. The machine learning may include application of random forest machine learning modeling. In accordance with some embodiments, machine learning models use the cortical activity of healthy people and patient’s group as the input [0190]. However, Choi et al. does not teach receiving a plurality of exercise programs from an exercise program library, the plurality of exercise programs including video features of exercise programs associated with the plurality of exercise programs. Simpson teaches the outputs may be delivered to a user by way of a video graphics card or integrated graphics chipset coupled to a display that maybe seen by a user. Alternatively, a printer may be employed to output hard copies of the results. Given this teaching, any number of other tangible outputs will also be understood to be contemplated by the invention [0587]. It would have been obvious in the ordinary skill in the art before the effective filing date to include the input-to-output workflow of a visual software system.
Regarding claim 11, Choi et al. teaches the method of claim 10 in which the in one implementation, data analytics platform 1220 may be configured to effectuate various AI/ML-based models or decision engines for purposes of some example embodiments of the present patent disclosure that may involve techniques such as support vector machines (SVMs) or support vector networks (SVNs), pattern recognition, fuzzy logic, neural networks (e.g., ANNs/CNNs), recurrent learning, and the like, as well as unsupervised learning techniques involving untagged data [0114]. It also further teaches The frequency bands (e.g., low theta, high theta, alpha, beta, and gamma) are employed to create multiple models (e.g., five models) to classify patients as healthy control subjects or patient subjects. Suitable machine learning processing is applied in this process. The machine learning may include application of random forest machine learning modeling. In accordance with some embodiments, machine learning models use the cortical activity of healthy people and patient’s group as the input [0190]. However, Choi et al. does not teach generating a plurality of exercise program recommendations. Simpson teaches If the receiving a selection of a program includes receiving a selection of an exercise program, then the method may further include displaying one or more graphical elements representing respective subprograms pertaining to different exercise programs, the one or more subprograms configured to further guide a user in treating the disease [0018]. It would have been obvious in one of ordinary skill in the art before the effective filing date to use machine learning to generate a plurality of exercise program recommendations based on pre-determined responses to the person's requests.
Regarding claim 13, the method of claim 10, wherein the video features include video background features. (Choi et al. teaches In another aspect of exercising with a virtual reality or augmented reality experience according to the present disclosure, the VR or AR detects the intention of the subject, filters out the tremor, and displays undisturbed arm or leg movement [0171]).
Regarding 14, the method of claim 13, wherein applying the vector search model comprises: generating, by the vector search model, the one or more vector features for the video background features. (Choi et al. teaches In one implementation, data analytics platform 1220 may be configured to effectuate various AI/ML-based models or decision engines for purposes of some example embodiments of the present patent disclosure that may involve techniques such as support vector machines (SVMs) or support vector networks (SVNs), pattern recognition, fuzzy logic, neural networks (e.g., ANNs/CNNs), recurrent learning, and the like, as well as unsupervised learning techniques involving untagged data [0114]. It further teaches In another aspect of exercising with a virtual reality or augmented reality experience according to the present disclosure, the VR or AR detects the intention of the subject, filters out the tremor, and displays undisturbed arm or leg movement [0171]).
Regarding claim 15, the method of claim 10, wherein identifying the one or more top video features comprises: identifying the one or more top video features based at least in part on common video features associated with the user exercise program history. (Choi et al. teaches In one arrangement, example architecture 1260 may include one or more virtual clinic platforms 1214, remote data logging platforms 1216, patient/clinician report processing platforms 1218, as well as data analytics platforms 1220 and security platforms 1222, at least some of which may be configured and/or deployed as an integrated digital health infrastructure 1212 for effectuating some example embodiments of the present disclosure [0053]).
Regarding claim 16, the method of claim 10, wherein the user exercise program history comprises one or more negative video features (Choi et al. teaches FIG. 3 depicts a flowchart illustrative of known blocks, steps and/or acts that may be implemented for establishing a communication session with an implantable medical device. Additional details regarding establishing a communication session with an implantable medical device may be found in U.S. Pat. No. 11,007,370 which is incorporated herein by reference [0072]).
Regarding claim 17, the method of claim 16, wherein applying the ML personalization model comprises: excluding the one or more negative video features. (Choi et al. teaches Having this measurement in turn offers opportunities for the AI in the physical therapy application to offer feedback to the patient if the patient repetitively makes the wrong move or mistimes the move. The feedback can be achieved via actuators embedded into the fabrics of the patient’s clothing, or special clothing articles (such as gloves, socks, shoes) that have embedded actuators [0175]).
Regarding claim 18, The method of claim 10, wherein the exercise program recommendation comprises at least one of the one or more top video features. (Choi et al. teaches In one arrangement, example architecture 1260 may include one or more virtual clinic platforms 1214, remote data logging platforms 1216, patient/clinician report processing platforms 1218, as well as data analytics platforms 1220 and security platforms 1222, at least some of which may be configured and/or deployed as an integrated digital health infrastructure 1212 for effectuating some example embodiments of the present disclosure [0053]).
Regarding claim 19, Choi et al. teaches an exercise program recommendation system, comprising: an exercise device comprising a display; (Choi et al. teaches In addition, such network elements may typically include a set of one or more processors coupled to one or more other components, such as one or more storage devices (e.g., non-transitory machine-readable storage media) as well as storage database(s), user input/output devices (e.g., a keyboard, a touch screen, a pointing device, and/or a display), and network connections for effectuating signaling and/or bearer media transmission [0052]).
and a processor and memory, the memory including instructions executable by the processor to cause the exercise program recommendation system to: apply a vector search model to a plurality of exercise programs, wherein the vector search model identifies vector features within the plurality of exercise programs; (Choi et al. teaches In some aspects, remote care therapy may involve any care, programming, or therapy instructions that may be provided by a doctor, a medical professional or a healthcare provider, and/or their respective authorized agents, collectively referred to as a “clinician”, using a suitable clinician device, with respect to the patient’s IMD, wherein such therapy instructions may be mediated, proxied or otherwise relayed by way of a controller device associated with the patient. As illustrated, example remote care system 100A may include a plurality of patient controller devices exemplified by patient controller device 150 and a plurality of clinician programmer devices exemplified by a clinician programmer device 180 (also referred to as a clinician programmer or clinician device) that may interact with a network-based infrastructure via respective communication interfaces. Example patient and clinician devices may each include a corresponding remote care service application module, e.g., a patient controller application 152 and a clinician programmer/controller application 182, executed on a suitable hardware/software platform for supporting a remote care service that may be managed by a network entity 155 [0065]. It further teaches In one implementation, data analytics platform 1220 may be configured to effectuate various AI/ML-based models or decision engines for purposes of some example embodiments of the present patent disclosure that may involve techniques such as support vector machines (SVMs) or support vector networks (SVNs), pattern recognition, fuzzy logic, neural networks (e.g., ANNs/CNNs), recurrent learning, and the like, as well as unsupervised learning techniques involving untagged data [0114]).
receive a search query for an exercise program, the search query comprising an exercise program feature; (Choi et al. teaches Challenges involving “Big Data” may include capture, curation, storage, search, sharing, transfer, analysis, and visualization, etc. Because “Big Data” available with respect to patients’ health data, physiological/behavioral data, sensor data gathered from patients and respective ambient surroundings, daily activity data, therapy settings data, health data collected from clinicians, etc. can be on the order of several terabytes to petabytes to exabytes or more, it becomes exceedingly difficult to work with using most relational database management systems for optimizing, ranking and indexing search results in typical environments [0113]).
and provide, via the display, a user with an exercise program recommendation from the plurality of exercise programs. (Choi et al. teaches Some example embodiments herein may therefore relate to a system and method for providing recommendations/reconfigurations of program settings based on the patient’s usage of the IMD and clinical observations/recommendations, which may facilitate context-sensitive selection of neuromodulation programs/settings [0059]). However, Choi et al. does not teach apply, based at least in part on the exercise program feature, a machine learning (ML) personalization model to the plurality of exercise programs. Simpson teaches the outputs may be delivered to a user by way of a video graphics card or integrated graphics chipset coupled to a display that maybe seen by a user. Alternatively, a printer may be employed to output hard copies of the results. Given this teaching, any number of other tangible outputs will also be understood to be contemplated by the invention [0587]. It would have been obvious in the ordinary skill in the art before the effective filing date to include the input-to-output workflow of a visual software system.
Regarding claim 20, the exercise program recommendation system of the exercise program recommendation system of wherein the exercise device includes an operating feature, and wherein the plurality of exercise programs include exercise device controls configured to adjust the operating feature. (Choi et al. teaches Further, at least a portion of the components of the digital health infrastructure network 1212 operating as a system 1200, one or more patients’ IMDs and/or NIMI devices 1204, and one or more external devices 1206 may be configured to execute suitable medical/health software applications in a cooperative fashion (e.g., in a server-client relationship) for effectuating various aspects of remote patient monitoring, telemedicine/telehealth applications, remote care therapy, etc. [0056]).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20230123383 A1 to Choi et al. in view of US 20220068453 A1 to Simpson and in further view of US 20160250519 A1 to Watterson.
Regarding claim 12, Choi et al. teaches Some example embodiments herein may therefore relate to a system and method for providing recommendations/reconfigurations of program settings based on the patient’s usage of the IMD and clinical observations/recommendations, which may facilitate context-sensitive selection of neuromodulation programs/settings [0059]. Simpson further teaches the method of claim 11 If the receiving a selection of a program includes receiving a selection of an exercise program, then the method may further include displaying one or more graphical elements representing respective subprograms pertaining to different exercise programs, the one or more subprograms configured to further guide a user in treating the disease [0018]. However, they do not teach implementing the selected exercise program on an exercise device. Watterson teaches A selected exercise program may be sent to an exercise device of the selecting user. The system may provide relevant information regarding the exercise programs to allow users to identify exercise programs that meet desired criteria [0039]. It would have been obvious in the ordinary skill in the art before the effective filing date to facilitate playing or implementing an exercise program on the exercise device. The exercise program may include operating information for the exercise device and some interactive features. The interactive features may include videos, including videos of trainers and/or virtual environments. The exercise programs are often stored in an exercise program library.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 20150306459 A1 to Boyette et al. teaches the schedule data may include a plurality of workout sessions that are scheduled for different days. The workout sessions may include a single exercise or multiple exercises (e.g., as defined in the set table data). In some implementations, the schedule data is generated such that there is a minimum number of days between sessions that contain the same exercise, so as to provide recovery time for the muscle groups that are worked by the exercise. US 202000306589 A1 to Jabr teaches this is an on-the-cloud flow where trainer uploads/and or capture the exercise videos, labels the video and algorithms extract what are termed features from that video and store them for later when it is needed for user flow tracking. This is relevant since it includes the exercise program which may include operating information for the exercise device and some interactive features. The interactive features which may include videos of trainers environments.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SENIYA BAIG whose telephone number is (571)270-0447. The examiner can normally be reached Monday-Thursday 7:30am-5:00pm, Friday 7:30am-4:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Xuan Thai can be reached at (571) 272-7147. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/S.B./Examiner, Art Unit 3715
/WILLIAM H MCCULLOCH JR/Primary Examiner, Art Unit 3715
