Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in response to applicant arguments filled on 02/23/2026 for application 18/980812.
Claims 1-20 have been canceled.
Claims 21-39 have been added new and are currently pending.
Detailed Action
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 21-39 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1:
Claims 21-39 are drawn to a method and system, which is/are statutory categories of invention (Step 1: YES).
Step 2A Prong One:
Independent claims 21 and 35 recite assign an exercise program; prompt the patient to send feedback; and analyzing the feedback. The recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity by identifying and reporting events preceding a pattern in a set of user data. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea (Step 2A Prong One: YES).
Step 2A Prong Two:
This judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including a online portal; network and a ML model, which are additional elements that are recited at a high level of generality such that they amount to no more than mere instruction to apply the exception using generic computer components. See: MPEP 2106.05(f).
The claims recite the additional element of providing an online portal, which are considered limitations directed to insignificant extra-solution activity that does not amount to an inventive concept because the limitations do not impose meaningful limits on the claim such that is it not nominally or tangentially related to the invention. In the claimed context, the claimed providing limitation is incidental to the performance of the recited abstract idea of identifying and reporting events preceding a pattern in a set of user data. See: MPEP 2106.05(g).
The combination of these additional elements is no more than mere instructions to apply the exception using generic computer components. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Hence, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea (Step 2A Prong Two: NO).
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, using the additional elements to perform the abstract idea amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using a generic components cannot provide an inventive concept. See: MPEP 2106.05(f).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are not integrated into the claim because they are merely incidental or token additions to the claim that do not alter or affect how the process steps or functions in the abstract idea are performed. Therefore, the claimed additional elements do not add meaningful limitations to the indicated claims beyond a general linking to a technological environment. See: MPEP 2106.05(h).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are configured to perform well-understood, routine, and conventional activities previously known to the industry. See: MPEP 2106.05(d). Said additional elements are recited at a high level of generality and provide conventional functions that do not add meaningful limits to practicing the abstract idea. The originally filed specification supports this conclusion:
“machine learning (ML) model based on a gradient boosting algorithm is stored on or connected to the server, wherein the machine learning (ML) model is configured to analyse the feedback data of a patient and to predict the patient's compliance when performing the exercise program the next time after expiry of a number of time intervals to follow, preferably after expiry of the next time interval. According to a further aspect of the invention, the system is configured to implement an inventive method for remotely monitoring a patient. “
“he patient client is configured to send feedback data to the server via a communication network, wherein the server is configured to send requests to the patient client via the communication network, the requests prompting the patient to send feedback data to the server, preferably each time after having finished an exercise.”
Fig. 7 shows a schematic example of an inventive system for implementing the online therapy. The setup includes a server 3 containing a database and providing an online portal 1, to which a therapist can log on by means of a therapist client, and a patient client 2 to be used by a patient. Communication between each of the server, the therapist client and the patient-side client 2 is possible in both directions using preferably the WebSocket protocol, which is illustrated by double arrows. The WebSocket protocol is advantageous in that real-time communication between the apps installed on the clients and the server is possible. Moreover, latency is considerably reduced compared to a pure Hypertext Transfer Protocol (HTTP). Finally, WebSockets are efficient in terms of message size and header information and WebSockets enable bidirectional data transfer, allowing the server to push updates to the apps, and facilitate seamless back-and-forth communication.
The claims recite the additional element of providing an online portal to a medical professional, which amounts to extra-solution activity concerning mere data gathering. The specification (e.g., as excerpted above) does not provide any indication that the additional elements are anything other than well‐understood, routine, and conventional functions when claimed in a merely generic manner (as they are here). See: MPEP 2106.05(g).
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea with routine, conventional activity specified at a high level of generality in a particular technological environment.
Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea (Step 2B: NO).
Dependent claim(s) 22-34 and 36-39 when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea without significantly more. These claims fail to remedy the deficiencies of their parent claims above, and are therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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) 21-39 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ashely et al. (US 2022/0288462 A1) in view of Gamarnik et al. (US 2018/0330810 A1).
In claim 21, a computer-implemented method for remotely monitoring a patient, comprising the steps:
Ashley teaches:
providing an online portal to a medical professional, wherein the online portal is operative to enable the medical professional to (Para. 60 wherein a web based interface is taught):
assign an exercise program to the patient, wherein the exercise program consists of a number of exercises to be completed by the patient each time in regular time intervals (Fig. 2 and Para. 5 and 158 wherein generating a treatment and exercise plan is taught); and
prompt the patient to send feedback data via a communication network, whereby a patient’s compliance with the exercise program is predicted by a machine learning (ML) model (Para. 60 wherein feedback form the patient is taught. Para. 97 teaches “FIG. 6 shows an example block diagram of training a machine learning model 13 to output, based on data 600 pertaining to the patient and to the occupational task(s) associated with the patient, a treatment plan 602 for the patient and a predicted estimate 603 of when the patient performing treatment plan”. In addition, Para 118 teaches “Determining the patient is on track for the current treatment plan may cause the trained machine learning model 13 to adjust a parameter of the treatment apparatus 70. The adjustment may be based on a next step of the treatment plan to further improve the performance of the patient.” i.e. compliance of treatment plan and adjusting treatment plan is taught)
Ashley does not explicitly teach however Gamarnik teaches using a gradient boosting algorithm (Para. 71 wherein gradient boosting algorithm is used in modeling is taught); and
Ashely further teaches:
wherein the ML model analyses feedback data of the patient and predicts the compliance when performing the exercise program a next time after expiry of a number of time intervals to follow (Para. 49 teaches “the level of compliance with the treatment plans, and the results of the treatment plans may use correlations and other statistical or probabilistic measures to enable the partitioning of or to partition the treatment plans into different patient cohort-equivalent databases in the patient data store”).
It would have been obvious to one of ordinary skill in the art at the time of filling to combine the system and method for generating treatment plans to enhance patient recovery as taught in Ashely with the artificial intelligence that uses gradient boosting to interpret data as taught in Gamarnik. The well-known elements described are merely a combination of old elements, and in combination, each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 22, Ashley teaches the computer-implemented method of claim 21, wherein the compliance is predicted by determining the likelihood of a patient completing more than 50% of the number of exercises when performing the exercise program a next time after expiry of a number of time intervals to follow (Para. 145 wherein “Controlling the treatment apparatus 70 distally may include the server 30 transmitting, based on the treatment plan, a control instruction to change a parameter of the treatment apparatus 70 at a particular time to increase a likelihood of a positive effect of continuing to use the treatment apparatus or to decrease a likelihood of a negative effect of continuing to use the treatment apparatus.”).
As per claim 23, Ashley teaches the computer-implemented method of claim 21, further including the step, providing a motivation message to a patient contingent upon prediction of a low patient compliance (Para. 112 wherein “It may be useful and encouraging to provide a patient with information about an incentive such as an awardable product, object, activity, accolade, title, discount, coupon or some combination of these, and to communicate that the patient could earn the incentive by adhering to the selected treatment plan”).
As per claim 24, Ashley teaches the computer-implemented method of claim 21, wherein the feedback data includes one or more of data assocuated with: 1) a patient identifier; 2) a course identifier; 3) on which day of a sequential number of days of the course the feedback data are sent; 4) a date; 5) a patient's subjective evaluation whether the patient complied on the date; 6) a number of exercises assigned to the patient on the date; 7) a painfulness of at least one exercise; 8) a difficulty the patient had when carrying out at least one exercise; 8) a time needed for completion of at least one individual exercise; 9) information on whether at least one of the individual exercise has been completed; 10) a number of exercises completed per day; 11) an exercise identifier; 12) information whether an exercise targets an upper or lower body part of the patient; 13) a day of the week the feedback data was provided; 14) a therapy center to which the patient belongs to; 15) an actual number of exercises executed by the patient in a day; 16) which of a number of exercises are skipped; 17) an average time per exercise; 18) a patient's assessment of an intensity of one or more exercises; and 19) a patient's demographics (Para. 137 and 172).
As per claim 25, Ashley teaches the computer-implemented method of claim 21, wherein the ML model has been trained using one or more of data associated with: 1) time needed for at least one of the individual exercises making up the training program; 2) information on whether an exercise has been completed; 3) a number of exercises completed per day; 4) an exercise identifier; 5) information whether an exercise targets an upper or lower body part; 6) course that the patient is assigned to; 7) on which day of a sequential number of days of a course feedback data was sent; 8) a day of a week exercises were executed; 9) a therapy center to which the patient belongs to; 9) a patient identifier; (Para. 148).
As per claim 26, Ashley teaches the computer-implemented method of claim 21, including the step, training the ML model for predicting the level of patient compliance utilizing exemplary feedback data (Para. 49 wherein “level of compliance with the treatment plans” is taught).
As per claim 27, Ashley teaches the computer-implemented method of claim 21, further including the step, providing an alert to a medical professional responsive to allow compliance prediction (Para. 109).
As per claim 28, Ashley teaches the computer-implemented method of claim 21. Ashely does not explicitly teach however Gamarnik teaches, wherein the exercise program is directed to improving physical and/or mental capabilities of stroke patients (Para. 45 wherein exercise program are for stroke patients is taught). The motivation to combine references is the same as seen in claim 21
As per claim 29, Ashley teaches the computer-implemented method of claim 21. Ashley does not explicitly teach however Gamarnik teaches, wherein the ML model is an extended gradient boosting algorithm and has been configured by: setting a learning rate to a value, which is equal to or larger than 0.15 and/or equal to or smaller than 0.34; setting a maximum tree depth to 4; setting a number of trees in an ensemble to a value, which is equal to or larger than 200 and/or equal to or smaller than 350; setting a training dataset size to a value that is equal to or larger than 4000 (Para. 71 wherein gradient boosting algorithms is taught. Para. 107 wherein the model can be supervised or unsupervised learning. Both Ashley and Gamrnik do not explicitly teach the specific values that the gradient boosting algorithm however the Examiner notes that a limitation specifying specific numbers/parameters used in a model is drawn to non-functional descriptive material and is not functionally involved with the system. The recited system would perform the same regardless of the numbers used. Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability, see In re Gulack, 703 F.2d 1381, 1385, 217 USPQ 401, 404 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994); In re Ngai, 367 F.3d 1336, 70 USPQ2d 1862 (Fed. Cir. 2004). See also MPEP 2106. The motivation to combine references is the same as seen in claim 21.
As per claim 30, Ashley teaches the computer-implemented method as recited in claim 21, further including the step, prescribing therapeutic treatment of a patient having suffered a stroke or a patient suffering from Parkinson's disease or an elderly patient (Para. 39).
As per claim 31, Ashley teaches the computer-implemented method as recited in claim 21, further including the step, determining improvement to a level of patient compliance (Para. 49 and 112).
As per claim 32, Ashley teaches the computer-implemented method as recited in claim 31, wherein a level of compliance of a patient having suffered a stroke or a patient suffering from Parkinson's disease or an elderly patient is improved (Para. 39, 49 and 112).
As per claim 33, Ashley teaches the computer-implemented method as recited in claim 21. Ashely does not explicitly teach however Gamarnik teaches further including the step, determining therapeutic treatment of a patient having suffered from a stroke (Para. 45). The motivation to combine references is the same as seen in claim 1.
As per claim 34, Ashley teaches the computer-implemented method as recited in claim 21, further including the step, determining improvement for patient compliance, including improving a level of compliance of a patient having suffered a stroke or a patient suffering from Parkinson's disease or an elderly patient (Para. 49 and 112).
Claims 35-39 recite substantially similar limitations as seen above and hence are rejected for similar rationale as noted above.
Response to Arguments
The Applicant argues the 101 rejection. The Applicant states that the method of claim 21, which includes a ML model that analyses feedback data of the patient and predicts the compliance cannot be reasonably interpreted as being, for a method of filtering content, a method that considers historical usage information while inputting new data, or a mental process that rules or steps for one to follow while implementing a test or playing a game. The Applicant argues that the claim is not directed to an abstract idea. The Examiner respectfully disagrees. The clam is directed to managing exercise program. Specifically, the claim recites assigning an exercise program to a patient, prompting the patient o provide feedback, evaluating information regarding the patient’s participation, and predicting future compliance with the program. Such activities constitute managing interactions between a healthcare provider and a patient and therefore fall within the category of certain methods of organizing human activity. The recited machine learning model and gradient boosting algorithm merely automate the valuation of patient information used in managing the exercise program and do not change the fundamental character of the claim.
The Applicant argues that the claims recite significantly more and integrate the abstract idea into a practical application. The additional elements recited a specific improvement over prior art systems by allowing remote users to share information in real time in a standardized. The Examiner respectfully disagrees. The alleged improvement is not reflected in the claim language. The claims do not recite any particular improvement to data transmission, networking, technology, remote communications, or information standardization. Rather, the additional elements are invoked as tools to implement the abstract idea.
Applicants reliance of Example 42 is not persuasive. While Applicant asserts that the claimed machine learning model predicts future patient compliance rather than analyzing past compliance, the claim nevertheless recites collecting patient feedback data, analyzing the collected information, and generating a prediction regarding future patient behavior. Predicting future compliance based on collected information constitutes information analysis and decision making that falls within the abstract idea previously identified by the office which is not similar to Example 42.
The Applicant argues the art rejection. The Applicant argues that the reference does not teach ML model predicting compliance with the exercise program. The Examiner respectfully disagrees. Paragraphs 118 teaches “determining the patient is on track for the current treatment plan may cause the trained machine learning model 13 to adjust a parameter of the treatment apparatus 70. The adjustment may be based on a next step of the treatment plan to further improve the performance of the patient”
The Applicant argues that Gamarnik does not disclose applying the gradient boosting algorithm to the ML model for predicting a patient’s future compliance with a program. The Examiner respectfully disagrees. The rejection does not rely on Gamarnik for expressly disclosing the identical prediction of future compliance. Rather, Gamarnik is relied upon for teaching the use of machine learning algorithms, including gradient boosting algorithms, to analyze patient exercise related data and derive compline related determinations. One of ordinary skill in the art would have recognized that such predictive algorithms are capable of being trained to generate various compliance related outputs, including future compliance predictions, based on historical compliance data.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAROUN P KANAAN whose telephone number is (571)270-1497. The examiner can normally be reached Monday-Friday 8:00-5:00.
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MAROUN P. KANAAN
Primary Examiner
Art Unit 3687
/MAROUN P KANAAN/ Primary Examiner, Art Unit 3687
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