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 Arguments
Applicant’s arguments with respect to claim(s) 1, 10, and 19 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 112(a) as follows: The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 62/910232, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. Upon review of the 62/910232 disclosure, there appears to be no support for the use of an artificial intelligence engine to generate a unique data signature or a modified treatment plan. Accordingly, claims 1-20 are not entitled to the benefit of the prior application.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 1, 10, and 19, the limitations “…generating, using the artificial intelligence engine, one or more alternative treatment plans for the user” and “…selecting an alternative treatment plan from the one or more alternative treatment plans” renders the claims unclear. Specifically it is unclear if the claims require that multiple alternative treatment plans are generated so that an alternative treatment plan may be selected or if the claims only require one treatment plan to be generated and selected as an alternative. In order to further advance prosecution Examiner is interpreting the claim as requiring only one treatment plan to be generated and selected as an alternative. Dependent claims inherit the same deficiencies.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-5, 7, 10-14, 16, and 19-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Bissonnette et al (US 2022/0016484) hereinafter Bissonnette.
Regarding claims 1 and 10, Bissonnette discloses a computer-implemented method comprising:
receiving, from one or more of an electromechanical machine (exercise device 100), a sensor, and a computing device (processing device), data associated with a user that uses the electromechanical machine to perform a treatment plan ([0306] receive one or more measurements as input)
wherein the electromechanical machine (exercise device 100) comprises at least one pedal ([0306] one or more pedals);
correlating, using an artificial intelligence engine (intelligence engine 65), at least two of a pain measurement ([0310] plurality of pain levels), an indication of medication utilization, and a measurement of revolutions per minute [0306] to generate a unique data signature associated with a treatment gap associated with the treatment plan performed by the user ([0306] processing device may generate, by the artificial intelligence engine 65, a machine learning model 60 trained to receive one or more measurements as input),
wherein the at least two of the pain measurement, the indication of medication utilization, and the measurement of revolutions per minute each satisfies a respective threshold level ([0309] At 4608, the processing device may; determine whether the one or more measurements satisfy a trigger condition; [0311] based on the one or more characteristics of the user and the one or more measurements, the processing device may determine whether the trigger condition has been satisfied)
based on the unique data signature associated with the treatment gap associated with the treatment plan performed by the user, generating, using the artificial intelligence engine, one or more alternative treatment plans for the user ([0312] At 4610, responsive to determining that the one or more measurements and/or the one or more characteristics of the user satisfy the trigger condition, the processing device may transmit the control instruction to the exercise device 100; the control instruction may modify the resistance of the one or more pedals);
based on a threshold compliance level associated with each of the one or more alternative treatment plans ([0310] an elapsed time of using an exercise device, an amount of force exerted on a portion of the exercise device, a range of motion achieved on the exercise device, a duration of use of the exercise device, or some combination thereof), selecting an alternative treatment plan from the one or more alternative treatment plans ([0312] At 4610, responsive to determining that the one or more measurements and/or the one or more characteristics of the user satisfy the trigger condition, the processing device may transmit the control instruction to the exercise device 100; the control instruction may modify the resistance of the one or more pedals)
and controlling, while the user uses the electromechanical machine and based on the alternative treatment plan, one or more operating parameters (resistance) associated with the at least one pedal of the electromechanical machine ([0312] control instruction to the exercise device 100 may cause the exercise device 100 to modify the resistance of the one or more pedals),
wherein the one or more operating parameters comprise a range of motion, a resistance, a number of revolutions per minute, a pedal radius, or some combination thereof ([0312] modify the resistance of the one or more pedals).
Bissonnette further discloses a computer non-transitory computer readable medium used to store instructions that, when executed, cause a processor to perform the following steps or processes of the method 4600 ([0011] and [0304]).
Regarding claims 2, 11, and 20, Bissonnette discloses wherein the artificial intelligence engine selects the alternative treatment plan based on one or more available resources, psychographics of the user, demographics of the user, geographic data associated with the user, or some combination thereof ([0310] personal information may include, e.g., demographic, psychographic or other information; [0312] responsive to determining that the one or more measurements and/or the one or more characteristics of the user satisfy the trigger condition, the processing device may transmit the control instruction to the exercise device 100).
Regarding claims 3 and 12, Bissonnette discloses wherein the treatment gap represents a difference between what is prescribed in the treatment plan and a characteristic of the user, wherein the characteristic comprises performance, physical condition, medical condition or both ([0306] the one or more measurements may be associated with a force exerted on the one or more pedals, a revolution per minute of the one or more pedals, etc.; [0309] the trigger condition may include the one or more measurements being less than a threshold value, less than or equal to a threshold value).
Regarding claims 4 and 13, Bissonnette discloses wherein the computing device transmits, using an input peripheral of the computing device, the pain measurement that is input by the user ([0304];[0226] the indication of the level of pain of the user may be entered by the user using any
suitable peripheral device at a particular user interface 18 displayed on a computing device 12).
Regarding claims 5 and 14, Bissonnette discloses wherein the artificial intelligence engine uses one or more trained computer-implemented models to generate the unique data signature associated with the treatment gap ([0305] one or more machine learning models 60 may be
generated and trained by the artificial intelligence engine 65 and/or the training engine 50 to perform
one or more of the operations of the method 4600).
Regarding claims 7 and 16, Bissonnette discloses converting a format of the data to a standardized or canonical format ([0138] data source 67 may be a relational database, a pivot table, or any suitable type of data structure configured to store data used for any of the operations described herein) and generating training data from the converted data, wherein the training data is used to train one or more computer-implemented models executed by the artificial intelligence engine ([0138] data source 67 that stores the training data for the training engine 50 and/or the artificial intelligence engine 65 to use to train the one or more machine learning models 60).
Regarding claim 19, Bissonnette discloses a system (Fig. 1) comprising:
one or more memory devices storing instructions ([0128] computer instructions stored on the one or more memory devices of the computing device 12); and
one or more processing devices (computing device 12) communicatively coupled to the one or more memory devices [0128],
wherein the one or more processing devices executes the instructions to:
receive, from one or more of an electromechanical machine (exercise device 100), a sensor, and a computing device, data associated with a user that uses the electromechanical machine to perform a treatment plan ([0306] processing device may generate, by the artificial intelligence engine 65, a machine learning model 60 trained to receive one or more measurements as input),
wherein the electromechanical machine comprises at least one pedal ([0306] one or more pedals of an exercise device 100);
correlate, using an artificial intelligence engine, at least two of a pain measurement ([0310] plurality of pain levels), an indication of medication utilization, and a measurement of revolutions per minute [0306] to generate a unique data signature associated with a treatment gap associated with the treatment plan performed by the user ([0306] processing device may generate, by the artificial intelligence engine 65, a machine learning model 60 trained to receive one or more measurements as input),
wherein the at least two of the pain measurement, the indication of medication utilization, and the measurement of revolutions per minute each satisfies a respective threshold level ([0309] At 4608, the processing device may determine whether the one or more measurements satisfy a trigger condition; [0311] based on the one or more characteristics of the user and the one or more measurements, the processing device may determine whether the trigger condition has been satisfied);
based on the unique data signature associated with the treatment gap associated with the treatment plan performed by the user, generate, using the artificial intelligence engine, one or more alternative treatment plans for the user ([0312] 4610, responsive to determining that the one or more measurements and/or the one or more characteristics of the user satisfy the trigger condition, the processing device may transmit the control instruction to modify pedal resistance);
based on a threshold compliance level associated with each of the one or more alternative treatment plans ([0310] an elapsed time of using an exercise device, an amount of force exerted on a portion of the exercise device, a range of motion achieved on the exercise device, a duration of use of the exercise device, or some combination thereof), select an alternative treatment plan from the one or more alternative treatment plans ([0312] modify pedal resistance); and
control, while the user uses the electromechanical machine and based on the alternative treatment plan, one or more operating parameters (pedal resistance) associated with the at least one pedal of the electromechanical machine ([0312] processing device may transmit the control instruction to the exercise device 100 to modify pedal resistance),
wherein the one or more operating parameters comprise a range of motion, a resistance, a number of revolutions per minute, a pedal radius, or some combination thereof ([0312] pedal resistance).
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.
Claim(s) 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Bissonnette (US 2022/0016484).
Regarding claims 6 and 15, Bissonnette discloses the method/system of claims 1 and 10 as discussed above, but fails to disclose wherein the pain measurement comprises pain after a final session of the treatment plan, average pain after all of one or more sessions of the treatment plan, or some combination thereof. However, Bissonnette further discloses wherein the pain measurement comprises an indication of a plurality of pain levels after performing a modified treatment plan ([0310]; Fig. 46). It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to calculate an average pain measurement from the plurality of pain levels in order to remove outliers from the data when determining whether or not to further modify the treatment plan. Such a modification would provide the predictable results of effectively adjusting a user’s treatment plan in order to promote recovery.
Claim(s) 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Bissonnette (US 2022/0016484) in view of Lin et al (US 9,682,306) hereinafter Lin.
Regarding claims 8 and 17, Bissonnette discloses the method/system of claims 1 and 10 as discussed above, but fails to disclose wherein the measurement of revolutions per minute comprises an average revolutions per minute associated with one or more sessions of the treatment plan. However, Lin discloses wherein the measurement of revolutions per minute comprises an average revolutions per minute associated with one or more sessions of the treatment plan (Col. 13, lines 19-21: processing unit displays average rotational speed (RPM)). It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the method/system as taught by Bissonnette with the measurement of revolutions per minute comprises an average revolutions per minute associated with one or more sessions of the treatment plan as taught by Lin. Such a modification would provide the predictable results of using the measured rotational speed to gauge the user’s exhaustion level (Col. 8, lines 5-10).
Claim(s) 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Bissonnette (US 2022/0016484) in view of Obma (US 2016/0242646).
Regarding claims 9 and 18, Bissonnette discloses the method/system of claims 1 and 10 as discussed above, but fails to disclose based on the unique data signature associated with the treatment gap, initiating a telehealth session between the computing device and a second computing device associated with a second user. However, Obma discloses based on the unique data signature associated with the treatment gap, initiating a telehealth session between the computing device and a second computing device associated with a second user ([0096-0097] using an app on a smartphone to analyze data gathered by sensors and allow for telemedicine exams or home rehabilitation under the supervision of a therapist). It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the method/system as taught by Bissonnette with based on the unique data signature associated with the treatment gap, initiating a telehealth session between the computing device and a second computing device associated with a second user as taught by Obma. Such a modification would provide the predictable results of providing data directly to the physician which can decrease office time and allow for more efficient uses of time with patients (Obma, [0097]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLOW GRACE WELCH whose telephone number is (703)756-1596. The examiner can normally be reached Usually M-F 8:00am - 4:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Benjamin Klein can be reached at 571-270-5213. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WILLOW GRACE WELCH/Examiner, Art Unit 3792
/LYNSEY C Eiseman/Primary Examiner, Art Unit 3796
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