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 .
Response to Amendment
In the amendment filed on January 21, 2026, the following has occurred: claim(s) 1, 14, 27 have been amended. Now, claim(s) 1-8, 10-21, 23-30 are pending.
Notice to Applicant
The Examiner has withdrawn the 35 U.S.C. 101 rejection(s) in light of the newly amended claims. The addition to the independent claims 1, 14, and 27 of the steps "obtaining, while the user uses the at least one treatment device to perform the treatment plan and using one or more sensors arranged to sense at least one of operating characteristics of the at least one treatment device and sensor data associated with the user, performance information associated with use of the at least one treatment device by the user;", and "based on the performance information obtained using the one or more sensors, generating an apparatus control signal to adjust an operating parameter of the at least one treatment device" combined with the previously presented steps and specifically the step of "based on the treatment plan, and while the user uses the at least one treatment device to perform the treatment plan, controlling, using the artificial intelligence engine, at least one pedal of the at least one treatment device to change a range of motion specified by the treatment plan and enabled by the at least one pedal;" recite a combination to improve the operations of the treatment device and integrates the abstract idea into a particular application. The 35 U.S.C. 101 rejection(s) withdrawn.
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 1-2, 4-8, 10-15, 17-21, 23-28, and 30 are rejected under 35 U.S.C. 103 as being unpatentable over Casey et al. (U.S. Patent Pre-Grant Publication No. 2022/0000556) in view of Khotilovich et al. (U.S. Patent Pre-Grant Publication No. 2023/0215552) in view of Ridgel et al. (U.S. Patent Pre-Grant Publication No. 2016/0166881) in further view of Avila-Hernandez et al. (U.S. Patent Pre-Grant Publication No. 2022/0165398).
As per independent claim 1, Casey discloses a method comprising: receiving, at a computing device, at least one user profile associated with a user, wherein the at least one user profile further indicates at least one condition of the user (See Paragraph [0034]: The client computing device is configured to receive a patient data set associated with a patient to be treated and the patient data set can include data representative of the patient's condition, anatomy, pathology, medical history, preferences, and/or any other information or parameters relevant to the patient, which the Examiner is interpreting the patient data set associated with a patient to encompass receiving at least one user profile associated with a user and patient's condition to encompass at least one condition of the user);
receiving, at the computing device, healthcare professional profile information associated with respective healthcare professionals which comprises a set of healthcare professionals capable of interacting with the user (See Paragraph [0040]: Each healthcare provider computing system can include at least one reference patient data set associated with reference patients treated by the corresponding healthcare provider, which the Examiner is interpreting to encompass the claimed portion as the multiple healthcare provider computing systems identify patients that can be treated by the healthcare provider);
identifying, by the computing device, treatment device information for each treatment device which comprises a set of treatment devices capable of being used by users having user profiles at least partially associated with the at least one user profile of the user (See Paragraph [0039]: The treatment data includes medical device design data for at least one medical device used to treat the reference patient, which the Examiner is interpreting the medical device design data to encompass treatment device information and the treatment data associated with a reference patient in a reference patient data set to encompass a set of treatment devices capable of being used by users having user profiles at least partially associated with the at least one user profile of the user);
generating, by the computing device and based on the at least one resource deployment prediction, the treatment plan for the user (See Paragraphs [0040]-[0042], [0046]-[0047]: The server can be configured with one or more algorithms that generate patient-specific treatment plan data (e.g., treatment procedures, medical devices) based on the reference data, and the treatment planning module is configured to generate develop and/or implement at least one predictive model for generating the patient-specific treatment model, which the Examiner is interpreting the predictive model to encompass based on the at least one resource deployment prediction as reference patient data sets can be selected based on patients that have similar conditions that can include hospital resources ([0046])); and
based on the treatment plan, and while the user uses the at least one treatment device to perform the treatment plan, controlling, using the artificial intelligence engine (See Paragraph [0064]: The treatment planning module generates control instructions configured to cause a surgical robot to partially or fully perform a surgical procedure, which the Examiner is interpreting the surgical robot to encompass the treatment device and the treatment planning module to encompass using the artificial intelligence engine as the treatment planning module can use artificial intelligence techniques, such as machine learning and artificial neural networks (See Paragraph [0050])), at least one pedal of the at least one treatment device to change a range of motion specified by the treatment plan and enabled by the at least one pedal.
While Casey discloses the method as described above, Casey may not explicitly teach generating, by the computing device and using an artificial intelligence engine that uses at least one machine learning model configured to generate resource deployment predictions, at least one resource deployment prediction, wherein the at least one machine learning model generates the at least one resource deployment prediction based on at least part of the at least one user profile, user profiles associated with other users, at least some of the healthcare professional profile information, at least some of the treatment device information, and at least one prioritization factor, wherein the at least one resource deployment prediction indicates that least one treatment device of the set of treatment devices to be used by the user to perform a treatment plan, wherein the prioritization factor is determined, at least in part, based on the at least one condition of the user.
Khotilovich teaches a method for generating, by the computing device and using an artificial intelligence engine that uses at least one machine learning model configured to generate resource deployment predictions, at least one resource deployment prediction, wherein the at least one machine learning model generates the at least one resource deployment prediction based on at least part of the at least one user profile, user profiles associated with other users, at least some of the healthcare professional profile information, at least some of the treatment device information, and at least one prioritization factor (See Paragraphs [0074], [0080]: Machine learning model utilized by patient eligibility predictor comprises one or more binary classification models, the machine learning model includes a plurality of separate binary classification models, each of which correspond to a clinical episode, the machine learning model uses a set of rule-based logic for determining whether a patient qualifies for a bundled payment and application of coordinated healthcare resources based on data extracted from the electronic health record (EHR) system, such as condition data, Medicare Severity-Diagnosis Related Group (MS-DRG) data, or procedure data, and feature importance using the metrics was considered on a class-by-class basis, which the Examiner is interpreting the application of coordinated healthcare resources based on data extracted from the electronic health record (EHR) system, such as condition data, MS-DRG data, or procedure data to encompass generate resource deployment predictions, at least one resource deployment prediction, wherein the at least one machine learning model generates the at least one resource deployment prediction based on at least part of the at least one user profile, user profiles associated with other users, at least some of the healthcare professional profile information, at least some of the treatment device information when combined with Casey, and prioritization factor when combined with Avila-Hernandez described below), wherein the at least one resource deployment prediction indicates at least one treatment device of the set of treatment devices to be used by the user to perform a treatment plan (See Paragraphs [0104]-[0105]: The application of coordinated healthcare resources may include allocating a particular treatment or treatment plan to the targeted patient or including the target patient in a group of patients who are to receive the particular treatment or treatment plan, which the Examiner is interpreting the application of coordinated healthcare resources to encompass indicates that least one treatment device to be used by the user to perform a treatment plan when combined with the treatment planning module of Casey in Paragraphs [0047]-[0048] as medical device designs can be identified), wherein the at least one prioritization factor is associated with information indicating access by the user to the at least one treatment device of the set of treatment devices, and wherein the prioritization factor is determined, at least in part, based on the at least one condition of the user.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Casey to include generating, by the computing device and using an artificial intelligence engine that uses at least one machine learning model configured to generate resource deployment predictions, at least one resource deployment prediction, wherein the at least one machine learning model generates the at least one resource deployment prediction based on at least part of the at least one user profile, user profiles associated with other users, at least some of the healthcare professional profile information, at least some of the treatment device information, and at least one prioritization factor as taught by Khotilovich. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Casey with Khotilovich with the motivation of improving patient eligibility identification (See Background of Khotilovich in Paragraph [0003]).
While Casey/Khotilovich disclose a method for based on the treatment plan, and while the user uses the at least one treatment device to perform the treatment plan, controlling, using the artificial intelligence engine, Casey/Khotilovich may not explicitly teach at least one pedal of the at least one treatment device to change a range of motion specified by the treatment plan and enabled by the at least one pedal;
obtaining, while the user uses the at least one treatment device to perform the treatment plan and using one or more sensors arranged to sense at least one of operating characteristics of the at least one treatment device and sensor data associated with the user, performance information associated with use of the at least one treatment device by the user; and
based on the performance information obtained using the one or more sensors, generating an apparatus control signal to adjust an operating parameter of the at least one treatment device.
Ridgel teaches a method for based on the treatment plan, and while the user uses the at least one treatment device to perform the treatment plan, controlling, using the artificial intelligence engine, at least one pedal of the at least one treatment device to change a range of motion specified by the treatment plan and enabled by the at least one pedal (See Fig. 4 and Paragraphs [0032]-[0037]: The controller is operably connected to the tandem device, the controller includes a logic processor, the logic processor determines the appropriate motor speed and load values and transmits this information to the motor drive controls, which the Examiner is interpreting the logic processor to encompass an artificial intelligence engine when combined with Casey/Khotilovich, and interpreting appropriate motor speed and load values and transmits this information to the motor drive controls to encompass change a range of motion specified by the treatment plan and enabled by the at least one pedal);
obtaining, while the user uses the at least one treatment device to perform the treatment plan and using one or more sensors arranged to sense at least one of operating characteristics of the at least one treatment device and sensor data associated with the user (See Paragraphs [0023]-[0024]: The controller in operating mode is used collect real-time performance data from the users (i.e., a trainer and a rider) using sensors and devices connected to, for example, bike pedals, the synchronized data samples are analyzed to determine the coupling characteristics (such as amplification, attenuation, drag, elasticity, and backlash, and the like) in the electrical coupling, which the Examiner is interpreting the coupling characteristics to encompass sense at least one of operating characteristics of the at least one treatment device and sensor data associated with the user), performance information associated with use of the at least one treatment device by the user (See Paragraphs [0023], [0028]: The controller, the servomotors and, the motor drive controls, and the sensors are couple to a data acquisition system, and the controller is programmed to collect performance data from each of the target patient and the second operator, which the Examiner is interpreting the performance data to encompass performance information); and
based on the performance information obtained using the one or more sensors, generating an apparatus control signal to adjust an operating parameter of the at least one treatment device (See Paragraphs [0039]-[0042]: The controller runs and controls the tandem bike during the rehabilitation session based on the information provided by the planning module, the adaptive exercise system also permits rapid identification of problems, rider fatigue, or unusual behavior for corrective control action and provides safely for the user(s), and the adaptive exercise system can include a model-based control model for providing the user with an experience similar to riding a tandem bicycle and a captain model (i.e., an expert model) for sensing capabilities of the rider and adjusting a process control for the tandem bike, which the Examiner is interpreting the adjusting a process control to encompass adjust an operating parameter of the at least one treatment device.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Casey/Khotilovich to include at least one pedal of the at least one treatment device to change a range of motion specified by the treatment plan and enabled by the at least one pedal; obtaining, while the user uses the at least one treatment device to perform the treatment plan and using one or more sensors arranged to sense at least one of operating characteristics of the at least one treatment device and sensor data associated with the user, performance information associated with use of the at least one treatment device by the user; and based on the performance information obtained using the one or more sensors, generating an apparatus control signal to adjust an operating parameter of the at least one treatment device as taught by Ridgel. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Casey/Khotilovich with Ridgel with the motivation of improving patient motor skills (See Background of the Invention of Ridgel in Paragraphs [0005]-[0006]).
While Casey/Khotilovich/Ridgel discloses a method for generating, by the computing device and using an artificial intelligence engine that uses at least one machine learning model configured to generate resource deployment predictions, at least one resource deployment prediction, wherein the at least one machine learning model generates the at least one resource deployment prediction based on at least part of the at least one user profile, user profiles associated with other users, at least some of the healthcare professional profile information, at least some of the treatment device information, and at least one prioritization factor, wherein the at least one resource deployment prediction indicates that least one treatment device of the set of treatment devices to be used by the user to perform a treatment plan, Casey/Khotilovich/Ridgel may not explicitly teach wherein the at least one prioritization factor is associated with information indicating access by the user to the at least one treatment device of the set of treatment devices, and wherein the prioritization factor is determined, at least in part, based on the at least one condition of the user.
Avila-Hernandez teaches a method for generating, by the computing device and using an artificial intelligence engine that uses at least one machine learning model configured to generate resource deployment predictions, at least one resource deployment prediction, wherein the at least one machine learning model generates the at least one resource deployment prediction based on at least part of the at least one user profile, user profiles associated with other users, at least some of the healthcare professional profile information, at least some of the treatment device information, and at least one prioritization factor (See Paragraphs [0061]-[0062]: The application may automatically put a particular health treatment plan in motion based on the preparedness numbers, and the preparedness rating and performance of the treatment plan tool can be evaluated, which the Examiner is interpreting the preparedness rating to encompass at least one prioritization factor), wherein the at least one resource deployment prediction indicates that least one treatment device of the set of treatment devices to be used by the user to perform a treatment plan, wherein the at least one prioritization factor is associated with information indicating access by the user to the at least one treatment device of the set of treatment devices (See Paragraph [0061]: The treatment plan tool may determine available health care providers and can provide variables such as locations, and equipment availability, and using these factors the treatment plan tool can determine preparedness number that ranks the health care providers for the patient, which the Examiner is interpreting the preparedness number to encompass the at least one prioritization factor, and location and equipment availability to encompass information indicating access by the user to the at least one treatment device of the set of treatment devices), and wherein the prioritization factor is determined, at least in part, based on the at least one condition of the user (See Paragraphs [0063]-[0064]: A treatment plan tool can use a machine learning model and its feedback to determine a health treatment plan, the patient’s condition can indicate the requirement an emergency necessitating an emergency vehicle, which the Examiner is interpreting the indication of patient’s condition necessitating an emergency factor to encompass the prioritization factor is determined, at least in part, based on the at least one condition of the user as the patient would be prioritized over a patient who does not require an emergency vehicle for their condition.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Casey/Khotilovich/Ridgel to include the at least one prioritization factor is associated with information indicating access by the user to the at least one treatment device of the set of treatment devices, and wherein the prioritization factor is determined, at least in part, based on the at least one condition of the user as taught by Avila-Hernandez. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Casey/Khotilovich/Ridgel with Avila-Hernandez with the motivation of improving patient outcomes (See Detailed Description of Avila-Hernandez in Paragraphs [0063]).
Claim(s) 14 and 27 mirror claim 1 only within different statutory categories, and are rejected for the same reason as claim 1.
As per claim 2, Casey/Khotilovich/Ridgel/Avila-Hernandez discloses the method of claim 1 as described above. Casey further teaches wherein, the user performs the treatment plan using at least one treatment device contained in the set of treatment devices (See Paragraph [0064]: One step of a surgical procedure can be manually performed by a surgeon and another step of the procedure can be performed by a surgical robot, which the Examiner is interpreting the surgical robot to encompass a treatment device.)
Claim(s) 15 and 28 mirror claim 2 only within different statutory categories, and are rejected for the same reason as claim 2.
As per claim 4, Casey/Khotilovich/Ridgel/Avila-Hernandez discloses the method of claim 1 as described above. Casey further teaches wherein, for a respective healthcare professional contained in the set of healthcare professionals, the healthcare professional profile information includes at least one of credential information associated with the respective healthcare professional, professional experience information associated with the respective healthcare professional, and availability information associated with the respective healthcare professional (See Paragraph [0046]: The one or more algorithms can be used to select the subset based on healthcare provider parameters (e.g., based on healthcare provider ranking/scores such as hospital/physician expertise, number of procedures performed, hospital ranking, etc.), which the Examiner is interpreting the healthcare provider parameters can be number of procedures performed to encompass the healthcare professional profile information includes professional experience information associated with the respective healthcare professional.)
Claim(s) 17 and 30 mirror claim 4 only within different statutory categories, and are rejected for the same reason as claim 4.
As per claim 5, Casey/Khotilovich/Ridgel/Avila-Hernandez discloses the method of claim 1 as described above. Casey further teaches wherein, for a respective treatment device of the set of treatment devices, the treatment device information includes at least one of identification information associated with the respective treatment device; location information associated with the respective treatment device; and availability information associated with the respective treatment device (See Paragraph [0059]: The patient-specific treatment plan includes a surgical procedure to implant a medical device, the treatment planning module can also store various types of implant surgery information, such as implant parameters, and availability of implants, which the Examiner is interpreting the availability of implants to encompass availability information associated with the respective treatment device.)
Claim 18 mirrors claim 5 only within a different statutory category, and is rejected for the same reason as claim 5.
As per claim 6, Casey/Khotilovich/Ridgel/Avila-Hernandez discloses the method of claim 1 as described above. Casey may not explicitly teach wherein the at least one resource deployment prediction defines a mapping of at least some of the healthcare professionals contained in the set of healthcare professionals, and at least some treatment devices contained in the set of treatment devices.
Khotilovich teaches a method wherein the at least one resource deployment prediction defines a mapping of at least some of the healthcare professionals contained in the set of healthcare professionals, and at least some treatment devices contained in the set of treatment devices (See Paragraph [0105]: Historical patient may be mapped to a standardized code associated with a clinical episode, which the Examiner is interpreting to encompass the claimed portion as the clinical episodes are utilized by the machine learning model to make a prediction (See Paragraph [0106]).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Casey to include the at least one resource deployment prediction defines a mapping of at least some of the healthcare professionals contained in the set of healthcare professionals, and at least some treatment devices contained in the set of treatment devices as taught by Khotilovich. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Casey with Khotilovich with the motivation of improving patient eligibility identification (See Background of Khotilovich in Paragraph [0003]).
Claim 19 mirrors claim 6 only within a different statutory category, and is rejected for the same reason as claim 6.
As per claim 7, Casey/Khotilovich/Ridgel/Avila-Hernandez discloses the method of claim 1 as described above. Casey may not explicitly teach wherein the at least one machine learning model generates the at least one resource deployment prediction further based on at least one insurance policy associated with the user.
Khotilovich teaches a method wherein the at least one machine learning model generates the at least one resource deployment prediction further based on at least one insurance policy associated with the user (See Paragraph [0037]: Determination of relevant features for the machine learning model can include demographics (e.g., age, gender, ethnicity, race, or insurance type, encounter history, and conditions, which the Examiner is interpreting to encompass the claimed portion.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Casey to include the at least one machine learning model generates the at least one resource deployment prediction further based on at least one insurance policy associated with the user as taught by Khotilovich. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Casey with Khotilovich with the motivation of improving patient eligibility identification (See Background of Khotilovich in Paragraph [0003]).
Claim 20 mirrors claim 7 only within a different statutory category, and is rejected for the same reason as claim 7.
As per claim 8, Casey/Khotilovich/Ridgel/Avila-Hernandez discloses the method of claim 1 as described above. Casey further teaches further comprising identifying, based at least in part on the at least one user profile, at least one cohort of users associated with the user, wherein the user profiles associated with the other users correspond to user profiles of the users of the at least one cohort of users (See Paragraph [0074]: The patient data set can be compared to a plurality of reference patient data sets in order to identify one or more similar patient data sets in the plurality of reference patient data sets, which the Examiner is interpreting the plurality of reference patient data sets to encompass the user profiles associated with the other users correspond to user profiles of the users of the at least one cohort of users.)
Claim 21 mirrors claim 8 only within a different statutory category, and is rejected for the same reason as claim 8.
As per claim 10, Casey/Khotilovich/Ridgel/Avila-Hernandez discloses the method of claim 1 as described above. Casey further teaches wherein at least one treatment device contained in the set of treatment devices has an orthopedic benefit or application (See Paragraph [0125]: The implant can be any orthopedic or other implant specifically designed to induce the patient's body to conform to the previously identified corrected anatomical configuration, which the Examiner is interpreting to encompass a treatment device contained in the set of treatment devices has an orthopedic application.)
Claim 23 mirrors claim 10 only within a different statutory category, and is rejected for the same reason as claim 10.
As per claim 11, Casey/Khotilovich/Ridgel/Avila-Hernandez discloses the method of claim 1 as described above. Casey may not explicitly teach wherein at least one treatment device contained in the set of treatment devices has a cardiovascular benefit or application.
Khotilovich teaches a method wherein at least one treatment device contained in the set of treatment devices has a cardiovascular benefit or application (See Paragraph [0072]: Clinical episodes represented by MS-DRG codes may include congestive heart failure, which the Examiner is interpreting to encompass the treatment device contained in the set of treatment devices has a cardiovascular benefit as the MS-DRG codes are utilized to identify the procedure, which combined with Casey's application of treatment device encompasses the claimed portion.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Casey to include at least one treatment device contained in the set of treatment devices has a cardiovascular application as taught by Khotilovich. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Casey with Khotilovich with the motivation of improving patient eligibility identification (See Background of Khotilovich in Paragraph [0003]).
Claim 24 mirrors claim 11 only within a different statutory category, and is rejected for the same reason as claim 11.
As per claim 12, Casey/Khotilovich/Ridgel/Avila-Hernandez discloses the method of claim 1 as described above. Casey further teaches wherein at least one treatment device contained in the set of treatment devices has a neurological benefit or application (See Paragraph [0094]: The virtual model of the patient's corrected anatomical configuration can include one or more regions of interest and may include some or all of the patient's anatomy within the regions of interest (e.g., nervous tissue), which the Examiner is interpreting to encompass the treatment device contained in the set of treatment devices has a neurological application as in Paragraph [0095] a surgical plan is generated from the virtual model.)
Claim 25 mirrors claim 12 only within different statutory category, and is rejected for the same reason as claim 12.
As per claim 13, Casey/Khotilovich/Ridgel/Avila-Hernandez discloses the method of claim 1 as described above. Casey further teaches wherein at least one treatment device contained in the set of treatment devices has an immunological benefit or application (See Paragraph [0127]: The systems and methods may also design patient-specific medical care based off disease progression for a particular patient, which the Examiner is interpreting specifying based off disease progression for a patient to encompass the treatment device contained in the set of treatment devices has an immunological benefit.)
Claim 26 mirrors claim 13 only within a different statutory category, and is rejected for the same reason as claim 13.
Claims 3, 16, and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Casey et al. (U.S. Patent Pre-Grant Publication No. 2022/0000556) in view of Khotilovich et al. (U.S. Patent Pre-Grant Publication No. 2023/0215552) in view of Ridgel et al. (U.S. Patent Pre-Grant Publication No. 2016/0166881) in view of Avila-Hernandez et al. (U.S. Patent Pre-Grant Publication No. 2022/0165398) in further view of Macoviak et al. (U.S. Patent Publication No. 10,468,131).
As per claim 3, Casey/Khotilovich/Ridgel/Avila-Hernandez discloses the method of claims 1-2 as described above. Casey/Khotilovich/Ridgel/Avila-Hernandez may not explicitly teach wherein, during a telemedicine session, the user performs the treatment plan using the at least one treatment device contained in the set of treatment devices.
Macoviak teaches a method wherein, during a telemedicine session, the user performs the treatment plan using the at least one treatment device contained in the set of treatment devices (See col. 13, ll. 45-49: A telemedicologist is a physician, surgeon, dentist, and/or veterinarian specialized in telemedicology and providing remote diagnosis and therapy via telemedicine technology and equipment, which the Examiner is interpreting therapy via telemedicine technology and equipment to encompass the user performs the treatment plan using the at least one treatment device contained in the set of treatment devices when combined with Casey/Khotilovich/Ridgel/Avila-Hernandez.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Casey/Khotilovich/Ridgel/Avila-Hernandez to include during a telemedicine session, the user performs the treatment plan using the at least one treatment device contained in the set of treatment devices as taught by Macoviak. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Casey/Khotilovich/Ridgel/Avila-Hernandez with Macoviak with the motivation of improving access to healthcare (See Background of the Invention of Macoviak in col. 1, ll. 25-28).
Claims 16 and 29 mirror claim 3 only within different statutory categories, and are rejected for the same reason as claim 3.
Response to Arguments
In the Remarks filed on January 21, 2026, the Applicant argues that the newly amended and/or added claims overcome the 35 U.S.C. 103 rejection(s). The Examiner does not acknowledge that the newly added and/or amended claims overcome the 35 U.S.C. 103 rejection(s).
The Applicant argues that:
(1) no combination of Casey, Khotilovich, Ridgel, and Avila-Hernandez teaches or suggests, inter alia, these features. The Examiner acknowledges that Casey, Khotilovich, and Ridgel do not teach or suggest wherein the at least one prioritization factor is associated with information indicating access by the user to the at least one treatment device of the set of treatment devices, as recited in claim 1. (See Page 12 of the Office Action). The Examiner relies on Avila-Hernandez to purportedly overcome the deficiencies of Casey, Khotilovich, and Ridgel. However, Avila-Hernandez does not teach or suggest, among other things, "generating, by the computing device and using an artificial intelligence engine that uses at least one machine learning model configured to generate resource deployment predictions, at least one resource deployment prediction, wherein the at least one machine learning model generates the at least one resource deployment prediction based on at least part of the at least one user profile, user profiles associated with other users, at least some of the healthcare professional profile information, at least some of the treatment device information, and at least one prioritization factor, wherein the at least one resource deployment prediction indicates at least one treatment device of the set of treatment devices to be used by the user to perform a treatment plan, wherein the at least one prioritization factor is associated with information indicating access by the user to the at least one treatment device of the set of treatment devices, and wherein the prioritization factor is determined, at least in part, based on the at least one condition of the user," as recited in claim 1. Avila-Hernandez describes that "the treatment plan tool 500 may determine available health care providers (e.g., St. Luke's ER and St. Al's Urgent Care) and can provide costs, locations, accepted insurance plans, and other variables such as expert reviews, equipment availability, specialist availability, and wait times, among others. Using these factors, the treatment plan tool 500 can determine a preparedness number that ranks the health care providers for the patient. Patient A may choose from the options, or the application may automatically put a particular health treatment plan in motion based on the preparedness numbers." (See Paragraph [0061] of Avila-Hernandez). Nothing in this, or any portion of Avila-Hernandez teaches or suggests, at least, "generating, by the computing device and using an artificial intelligence engine that uses at least one machine learning model configured to generate resource deployment predictions, at least one resource deployment prediction, wherein the at least one machine learning model generates the at least one resource deployment prediction based on at least part of the at least one user profile, user profiles associated with other users, at least some of the healthcare professional profile information, at least some of the treatment device information, and at least one prioritization factor, wherein the at least one resource deployment prediction indicates at least one treatment device of the set of treatment devices to be used by the user to perform a treatment plan, wherein the at least one prioritization factor is associated with information indicating access by the user to the at least one treatment device of the set of treatment devices, and wherein the prioritization factor is determined, at least in part, based on the at least one condition of the user," as recited in claim 1. Notably, a "preparedness number" is unrelated to and has neither an association nor any shared or similar definition or meaning with Applicant's "prioritization factor." Indeed, Casey, Khotilovich, and Ridgel also appear to be completely lacking in any teaching or suggestion of Applicant's claimed features. For at least the above reasons, Applicant respectfully asserts that claim 1 defines over the cited art. Independent claims 14 and 27 are allowable for at least similar reasons as claim 1. Dependent claims 2, 4-8, 10-13, 15, 17-21, 23-26, 28, and 30 ultimately depend from claims 1, 14, and 27 and are therefore allowable for at least similar reasons. Accordingly, Applicant requests that the Examiner reconsider and withdraw the rejections to claims 1-2, 4-8, 10-15, 17-21, 23-28, and 30 under 35 U.S.C. 103;
(2) claim 3 ultimately depends from claim 1. As described, no combination of Casey, Khotilovich, Ridgel, and Avila-Hernandez teaches or suggests, at least, the amended features of claim 1. Macoviak also does not teach or suggest, at least, these features. Thus, Applicant respectfully asserts that claim 3 defines over the cited art. Claim 16 ultimately depends from claim 13. As described, no combination of Casey, Khotilovich, Ridgel, and Avila-Hernandez teaches or suggests, at least, the amended features of claim 13. Macoviak also does not teach or suggest, at least, these features. Thus, Applicant respectfully asserts that claim 16 defines over the cited art. Claim 29 ultimately depends from claim 27. As described, no combination of Casey, Khotilovich, Ridgel, and Avila-Hernandez teaches or suggests, at least, the amended features of claim 27. Macoviak also does not teach or suggest, at least, these features. Thus, Applicant respectfully asserts that claim 29 defines over the cited art. For at least the above reasons, Applicant respectfully requests that the Examiner reconsider and withdraw the rejections to claims 3, 16, and 29 under 35 U.S.C. 103.
In response to argument (1), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner maintains that Avila-Hernandez when combined with Casey/Khotilovich/Ridgel teaches “wherein the prioritization factor is determined, at least in part, based on the at least one condition of the user” in Paragraphs [0063]-[0064] that a treatment plan tool can use a machine learning model and its feedback to determine a health treatment plan, the patient’s condition can indicate the requirement an emergency necessitating an emergency vehicle, which the Examiner is interpreting the indication of patient’s condition necessitating an emergency factor to encompass the prioritization factor is determined, at least in part, based on the at least one condition of the user as the patient would be prioritized over a patient who does not require an emergency vehicle for their condition. The Applicant’s Specification describes a “prioritization factor” as “The prioritization factor may include any value corresponding to a priority or urgency of treatment associated with a respective user. The prioritization factor may be characterized or described by an absolute number or number of units, by a percentage (e.g., "20% more,""15% less"), by a percentage point (e.g., "5.3 percentage points less than (or greater than)), by a parametrically ranked value (e.g., "2nd highest vs. 5th highest," therefore 3 ranks higher), by a qualitatively described value (e.g., "very different," "somewhat different," "equal," "more than," "better than," "less than," "worse than" and the like), or by any other means of expressing or describing a prioritization factor. The prioritization factor may be determined using any suitable technique including, but not limited to, analyzing (e.g., by a human or robot, as described herein) data associated with each respective user (e.g., including the URD, and/or any other suitable data). The prioritization factor may correspond to information corresponding to a health insurance policy associated with the respective user (e.g., such as an estimated co-pay and/or out-of-pocket expense, an estimated negotiated rate for the one or more resources, and the like), a medical urgency of the respective user, information corresponding to a schedule or personal requirement (e.g., such as a career requirement, for, e.g., a professional athlete or other suitable public figure), information corresponding to access to a treatment device by the respective user, other suitable information, or a combination thereof.” (Specification in Paragraph [0037]). The Examiner maintains that the “preparedness number” to teach on the “prioritization number” as the treatment plan tool can also determine a health care provider's ability to care for Patient A based on a preparedness-like number (e.g., an optimization score) and a cost to Patient A (Avila-Hernandez in Paragraph [0060]). The 35 U.S.C. 103 rejection(s) stand.
In response to argument (2), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner maintains the independent claims 1, 13, and 27 are rejected under 35 U.S.C. 103 rejection(s). The dependent claims 3, 16, and 29 are rejected due to their dependency on the independent claims and individually as described above in the 35 U.S.C. 103 rejection(s). The 35 U.S.C. 103 rejection(s) stand.
Conclusion
THIS ACTION IS MADE FINAL. 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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/Bennett Stephen Erickson/Primary Examiner, Art Unit 3683
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