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.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 10, and 19 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 10, and 19 respectively of copending Application No. 19/363378 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the instant application are anticipated by the claims of application no. 19/363378.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Instant Application 19/363480
Co-pending Application 19/363378
Claim 1
A computer-implemented method comprising:
Claim 1
A computer-implemented method comprising:
receiving, from one or more of an electromechanical machine, a sensor, and a computing device, data associated with a user that uses the electromechanical machine to perform a treatment plan, wherein the electromechanical machine comprises at least one pedal;
receiving, from one or more of an electromechanical machine, a sensor, and a computing device, data associated with a user that uses the electromechanical machine to perform a treatment plan wherein the electromechanical machine comprises at least one pedal;
based on one or more correlations of one or more indicators included in the data, generating,
using an artificial intelligence engine, a unique data signature associated with a regression of the user's condition, wherein the unique data signature signal is generated when the one or more indicators satisfy a respective threshold indicator level, and the one or more indicators comprise a pain measurement, a measurement of revolutions per minute, and a session pedaling time,
correlating, using an artificial intelligence engine, at least two of a pain measurement, an indication of medication utilization, and a measurement of revolutions per minute to generate a unique data signature associated with a treatment gap associated with the treatment plan performed by the user, 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;
based on the unique data signature associated with the regression of the user's condition,
generating, using the artificial engine, a modified treatment plan at least by modifying a parameter or activity associated with at least one of the pain measurement, the measurement of revolutions per minute, and the session pedaling time, and
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;
controlling, while the user uses the electromechanical machine and using the modified treatment plan, one or more operating parameters associated with the at least one pedal of the electromechanical machine, 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,.
and controlling, while the user uses the electromechanical machine and based on the alternative treatment plan, one or more operating parameters associated with the at least one pedal of the electromechanical machine, 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.
Claim 10
One or more tangible, non-transitory computer-readable media storing computer instructions that, when executed, cause one or more processing devices to:
Claim 10
One or more tangible, non-transitory computer-readable media storing instructions that, when executed, cause one or more processing devices to:
receive, from one or more of an electromechanical machine, a sensor, and a computing device, data associated with a user that uses the electromechanical machine to perform a treatment plan, wherein the electromechanical machine comprises at least one pedal;
receive, from one or more of an electromechanical machine, a sensor, and a computing device, data associated with a user that uses the electromechanical machine to perform a treatment plan wherein the electromechanical machine comprises at least one pedal;
based on one or more correlations of one or more indicators included in the data, generate,
using an artificial intelligence engine, a unique data signature associated with a regression of the user's condition, wherein the unique data signature is generated when the one or more indicators satisfy a respective threshold indicator level, and the one or more indicators comprise a pain measurement, a measurement of revolutions per minute, and a session pedaling time;
correlate, using an artificial intelligence engine, at least two of a pain measurement, an indication of medication utilization, and a measurement of revolutions per minute to generate a unique data signature associated with a treatment gap associated with the treatment plan performed by the user, 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;
based on the unique data signature associated with the regression of the user's condition,
generate, using the artificial intelligence engine, a modified treatment plan at least by modifying a parameter or activity associated with at least one of the pain measurement, the measurement of revolutions per minute, and the session pedaling time; and
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;
control, while the user uses the electromechanical machine and using the modified treatment plan, one or more operating parameters associated with the at least one pedal of the electromechanical machine, 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.
and control, while the user uses the electromechanical machine and based on the alternative treatment plan, one or more operating parameters associated with the at least one pedal of the electromechanical machine, 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.
Claim 19
A system comprising:
Claim 19
A system comprising:
one or more memory devices storing instructions; and
one or more memory devices storing instructions; and
one or more processing devices communicatively coupled to the one or more memory devices, wherein the one or more processing devices execute the instructions to:
one or more processing devices communicatively coupled to the one or more memory devices, wherein the one or more processing devices executes the instructions to:
receive, from one or more of an electromechanical machine, a sensor, and a computing device, data associated with a user that uses the electromechanical machine to perform a treatment plan, wherein the electromechanical machine comprises at least one pedal;
receive, from one or more of an electromechanical machine, a sensor, and a computing device, data associated with a user that uses the electromechanical machine to perform a treatment plan, wherein the electromechanical machine comprises at least one pedal;
based on one or more correlations of one or more indicators included in the data, generate,
using an artificial intelligence engine, a unique data signature associated with a regression of the user's condition, wherein the unique data signature is generated when the one or more indicators satisfy a respective threshold indicator level, and the one or more indicators comprise a pain measurement, a measurement of revolutions per minute, and a session pedaling time;
correlate, using an artificial intelligence engine, at least two of a pain measurement, an indication of medication utilization, and a measurement of revolutions per minute to generate a unique data signature associated with a treatment gap associated with the treatment plan performed by the user, 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;
based on the unique data signature associated with the regression of the user's condition,
generate, using the artificial intelligence engine, a modified treatment plan at least by modifying a parameter or activity associated with at least one of the pain measurement,
the measurement of revolutions per minute, and the session pedaling time; and
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;
control, while the user uses the electromechanical machine and using the modified treatment plan, one or more operating parameters associated with the at least one pedal of the electromechanical machine, 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.
control, while the user uses the electromechanical machine and based on the alternative treatment plan, one or more operating parameters associated with the at least one pedal of the electromechanical machine, 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.
Claim Objections
Claims 1 and 20 are objected to because of the following informalities:
The last line of newly amended claim 1 recites “…combination thereof,.” Examiner suggests removing the comma following “thereof”.
The last line of newly amended claim 20 recites, “…comprises an action of associated with readmitting…”. Examiner suggests amended the claim to recite, “…comprising an action [[of]] associated with readmitting…”.
Appropriate correction is respectfully requested.
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)(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, 3-4, 6-7, 10, 12-13, 16, and 19 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 [0304-0317] comprising:
receiving, 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] receive one or more measurements as input),
wherein the electromechanical machine comprises at least one pedal ([0306] one or more pedals);
based on one or more correlations of one or more indicators included in the data, generating,
using an artificial intelligence engine, a unique data signature associated with a regression of the user's condition ([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 unique data signature signal is generated when the one or more indicators satisfy a respective threshold indicator 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), and
the one or more indicators comprise a pain measurement ([0310] plurality of pain levels), a measurement of revolutions per minute [0306], and a session pedaling time ([0310] elapsed time of using an exercise device),
based on the unique data signature associated with the regression of the user's condition, generating, using the artificial engine, a modified treatment plan at least by modifying a parameter or activity associated with at least one of the pain measurement, the measurement of revolutions per minute, and the session pedaling time ([0312] modify the resistance of the one or more pedals in real-time or near real-time), and
controlling, while the user uses the electromechanical machine and using the modified treatment plan, one or more operating parameters associated with the at least one pedal of the electromechanical machine ([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),
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] control instruction to the exercise device 100 may cause the exercise device 100 to modify the resistance of the one or more pedals).
Bissonnette further discloses a computer readable medium used to store instructions that, when executed, cause a processor to perform the following steps or processes of the method 4600 [0304].
Regarding claims 3 and 12, Bissonnette discloses wherein the computing device transmits, using an input peripheral of the computing device, the pain measurement that has been 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 4 and 13, Bissonnette discloses wherein the artificial intelligence engine uses one or more trained computer-implemented models to generate the unique data signature associated with the regression of the user's condition ([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 claim 6, Bissonnette discloses wherein the pain measurement comprises a pain level at onset of a treatment session, an average beginning pain for one or more sessions of the treatment plan, a pain level after a final session, an average pain after the one or more sessions, or some combination thereof ([0226-0227] receive input including an indication of pain at 2702 and then determining an exercise level of the user at 2704).
Regarding claims 7 and 16, Bissonnette discloses wherein the session pedaling time is determined based on an average of one or more sessions of the treatment plan ([0310] The performance information may include, e.g., an elapsed time of using an exercise device or a duration of use of the exercise device).
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 execute the instructions to:
receive, from one or more of an electromechanical machine (exercise machine 100), a sensor, and a computing 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 comprises at least one pedal ([0306] one or more pedals of an exercise device 100);
based on one or more correlations of one or more indicators included in the data, generate, using an artificial intelligence engine, a unique data signature associated with a regression of the user's condition ([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 unique data signature is generated when the one or more indicators satisfy a respective threshold indicator 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), and
the one or more indicators comprise a pain measurement ([0310] plurality of pain levels), a measurement of revolutions per minute [0306], and a session pedaling time ([0310] elapsed time of using an exercise device);
based on the unique data signature associated with the regression of the user's condition, generate, using the artificial intelligence engine, a modified treatment plan at least by modifying a parameter or activity associated with at least one of the pain measurement, the measurement of revolutions per minute, and the session pedaling time ([0312] modify the resistance of the one or more pedals in real-time or near real-time); and
control, while the user uses the electromechanical machine and using the modified treatment plan, one or more operating parameters associated with the at least one pedal of the electromechanical machine ([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),
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] control instruction to the exercise device 100 may cause the exercise device 100 to modify the resistance of the one or more pedals).
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) 2, 5, 11, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bissonnette (US 2022/0016484) in view of Mafhouz (US 2019/0090744).
Regarding claims 2, 11, and 20, Bissonnette discloses the method/system of claims 1, 10, and 19 as discussed above, but fails to disclose wherein, based on the unique data signature associated with the regression of the user's condition, generating the modified treatment plan comprises an action of readmitting the user to a healthcare facility. However, Mafhouz discloses a treatment plan of readmitting the user to a healthcare facility ([0070] the data outputs may comprise, without limitation, reported patient outcomes, physical therapy metrics, and warning indicators indicative of readmission to perform surgical intervention). 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 a treatment plan of readmitting the user to a healthcare facility as taught by Mafhouz. Such a modification would provide the predictable results of allowing for surgical intervention to improve the patient’s mobility (Mafhouz, [0070]).
Regarding claims 5 and 14, Bissonnette discloses the method/system of claims 1 and 10 as discussed above, but fails to disclose wherein the regression results in a hospital readmission. However, Mafhouz discloses wherein the regression results in a hospital readmission ([0080] In a post-operative or rehabilitation setting, as part of the PT aspect 140, the data may be analyzed to optimize patient exercise routines or warn of potential issues or setback that may lead to readmission). 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 regression resulting in a hospital readmission as taught by Mahfouz. Such a modification would provide the predictable results of allowing for surgical intervention to improve the patient’s mobility (Mafhouz, [0070]).
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 13 as discussed above, but fails to disclose based on the unique data signature associated with the regression of the user's condition, initiating a telehealth session between the computing device and a second computing device associated with a second user. However, Obma discloses based on a unique data signature associated with a regression of the user's condition, initiating a telehealth session between a 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 a unique data signature associated with a regression of the user's condition, initiating a telehealth session between a 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]).
Claim(s) 15 is rejected under 35 U.S.C. 103 as being unpatentable over Bissonnette (US 2022/0016484).
Regarding claim 15, Bissonnette discloses the system of claim 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.
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.
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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
/Benjamin J Klein/Supervisory Patent Examiner, Art Unit 3792
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