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 claim(s)
Claims 1-20 have been examined. Claims 1 and 11 have been amended.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sahin (US 20150099946A1) in view of Shavit (US 20170368413A1)
With respect to claim 1, Sahin teaches a system comprising:
a trainer computing device configured for operation by a trainer providing training to a trainee, the trainee having a developmental disability, the trainer computing device being configured for communication with a server that stores data for at least one task having a plurality of task steps, each task step having associated instructions for performing the task step (‘946; Para 0038: by disclosure, Sahin describes, as in Fig. 1A, evaluating an individual 102 for autism spectrum disorder includes a wearable data collection device 104 worn by the individual 102 (trainee) and/or a wearable data collection device 108 worn by a caregiver 106 (trainer), such that data 116 related to the interactions between the individual 102 and the caregiver 108 are recorded by at least one wearable data collection device 104, 108 and uploaded to a network 110 for analysis, archival, and/or real-time sharing with a remotely located evaluator 114; Para 0082: the session and evaluation data may be uploaded to long term storage in a server farm or cloud storage area.; Para 0087: as in Fig. 4, an example method 400 for conducting an evaluation session using a wearable data collection device donned by a caregiver of an individual being evaluated for Autism Spectrum Disorder; upon powering and donning the wearable data collection device, or launching an evaluation session application, the evaluation session may be initiated. Initiation of the evaluation session may include, in some embodiments, establishment of a communication channel between the wearable data communication device and a remote computing system: Para 0089: upon powering and donning the wearable data collection device, or launching an evaluation session application, the evaluation session may be initiated. Initiation of the evaluation session may include, in some embodiments, establishment of a communication channel between the wearable data communication device and a remote computing system);
a trainee computing device configured for operation by the trainee, the training computing device being configured for communication with the server and configured to output the associated instructions for each task step of the plurality of task steps while the trainee is performing the at least one task (946; Para 0038: by disclosure, Sahin describes, as in Fig. 1A, evaluating an individual 102 for autism spectrum disorder includes a wearable data collection device 104 worn by the individual 102 (trainee) and/or a wearable data collection device 108 worn by a caregiver 106 (trainer), such that data 116 related to the interactions between the individual 102 and the caregiver 108 are recorded by at least one wearable data collection device 104, 108 and uploaded to a network 110 for analysis, archival, and/or real-time sharing with a remotely located evaluator 114; Para 0050: the wearable data collection device 104, in some implementations, is configured to monitor physiological functions of the individual 102. In some examples, the wearable data collection device 104 may collect heart and/or breathing rate data 116 e (or, optionally, electrocardiogram (EKG) data), electroencephalogram (EEG) data 116 f, and/or Electromyography (EMG) data 116 i). The wearable data collection device 104 may interface with one or more peripheral devices, in some embodiments, to collect the physiological data. For example, the wearable data collection device 104 may have a wired or wireless connection with a separate heart rate monitor, EEG unit, or EMG unit. In other embodiments, at least a portion of the physiological data is collected via built-in monitoring systems. Unique methods for non-invasive physiological monitoring are described in greater detail in relation to FIG. 11. Optional onboard and peripheral sensor devices for use in monitoring physiological data are described in relation to FIG. 12.).
Shavit discloses
wherein:
the trainer computing device is further configured to: enable the trainer to: monitor progress of the trainee operating the trainee computing device while the trainee is performing the plurality of task steps of the at least one task; and assign a task step score from a plurality of task step scores for each task step of the plurality of task steps after the trainee has completed each task step, the task step score indicating a level of guidance provided by the trainer to the trainee while the trainee was completing each task step; receive the task step scores for the plurality of task steps assigned by the trainer; and communicate the task steps scores for the plurality of task steps assigned by the trainer to the server; and (‘413; Para 0338: Shavit describes Monitor correct performance of an exercise. This may include: correct movements, correct sequence of movement, correct pace, correct range of movement, and alike.; Para 0753: Shavit further discloses guiding the user to perform a step in the exercise, receiving feedback on the user's performance—for example from the motion sensors or camera, correcting the user performance while explaining the correction, then guiding the user to the next step. This guidance may repeat every session as long as the system sees it is necessary. The necessity of teaching the exercise can be set by: a predefined number of times after the first performance (this number of times can be set according to the user's experience level), user's request, or as long as the user's exercise performance level is not above a certain threshold.); and
receive the task step scores for the plurality of task steps assigned by the trainer; and communicate the task steps scores for the plurality of task steps assigned by the trainer to the server (‘413; Para 0431: Shavit describes Filtering can be based on grade at each step even if there is no captured frame from the sensors that gives the 2D/3D body representation or other sensor data at this step. This can be based on the probability or other metrics calculated for each of the steps body models. Probability or metrics calculation can bring previous steps body models probability into account. A grade for filtering can also be some other weighted average or a result of a filter or a calculation based on the previous step or previous steps. )
the server is configured to select a complexity level from a plurality of complexity levels to be used by the trainer during a subsequent training session when the trainee performs the at least one task based on the task step scores for the plurality of task steps, the complexity level indicating training conditions and an amount of oversight to be provided by the trainer to the trainee during the subsequent training session (‘413; Para 0870: Shavit describes, as in FIG 38, the Trail and Error method for setting difficulty level also described above. Some definitions of difficulty levels can be considered common to many skill based exercises. Difficulty level can be determined by the difficulty of the sequence or routine that need to be performed or repeated, the difficulty of the puzzle or problem needed to be solved, and the like. A specific problem's difficulty level can be determined by its complexity which may sometimes be measured by the number of variables or object participants in the problem/game/sequence. Giving less time for the problem/activity or stages in it or exposing the hints or problem; Para 0989: the complexity of generating a training program based on the selected rule sets (Can be measured by computations required, steps required, time required and alike); The time it took the resulting program based on the rule sets to bring the trainee to his goals) .
It would have been obvious to one of ordinary skill in the art before the effective filing date of claimed invention to modify the system of diagnosis and treatment of neurology evaluation and management of autism spectrum disorder of Sahin with the technique of training system for designing, monitoring and providing feedback training as taught by Shavit and the motivation is to provide a complexity level from a plurality of complexity levels to be used by the trainer during a subsequent training session.
Claim 11 is rejected as the same reason with claim 1.
With respect to claim 2, the combined art teaches the system of claim 1, wherein the associated instructions for each of the plurality of tasks steps output by the trainee computing device to the trainee while the trainee is performing the at least one task include at least one of: video output; audio output; image output; and textual output (‘946; Paras0135- 0136, 0205-0206).
Claim 12 is rejected as the same reason with claim 2.
With respect to claim 3, the combined art teaches the system of claim 1, wherein the plurality of task step scores include an independent score indicating that the trainee was able to independently complete the associated task step without assistance (‘946; Para 0055).
Claim 13 is rejected as the same reason with claim 3.
With respect to claim 4, the combined art teaches the system of claim 3, wherein the server is configured to select the complexity level based on a number of task steps from the plurality of task steps that were assigned with the independent score by the trainer (‘946; Para 0257).
Claim 14 is rejected as the same reason with claim 4.
With respect to claim 5, the combined art teaches the system of claim 3, wherein the plurality of task step scores additionally include at least one of: a full physical score indicating that the trainer provided full physical assistance to the trainee while the trainee was performing the associated task step; a partial physical score indicating that the trainer provided partial physical assistance to the trainee while the trainee was performing the associated task step; and a gesture score indicating that the trainer provided a gesture to the trainee to assist the trainee while the trainee was performing the associated task step (‘413; Para 053, 0341,0).
Claim 15 is rejected as the same reason with claim 5.
With respect to claim 6, the combined art teaches the system of claim 1, wherein the trainer computing device is configured to display the associated instructions that are also being output to the trainee with the trainee computing device (‘946; Paras 0039-0040).
Claim 16 is rejected as the same reason with claim 6.
With respect to claim 7, the combined art teaches the system of claim 1, wherein the trainer computing device is configured to direct the trainee computing device, through communication with the server, to move to a different task step within the plurality of task steps based on input received from the trainer with the trainer computing device (‘946; Para 0019).
Claim 17 is rejected as the same reason with claim 7.
With respect to claim 8, the combined art teaches the system of claim 1, wherein each of the plurality of complexity levels indicate at least one of: a trainer-to-trainee ratio; whether the trainer is in close proximity to the trainee; whether the trainer is in a room with the trainee; and whether verbal praise is provided after each step (‘946; Para 0169).
Claim 18 is rejected as the same reason with claim 8.
With respect to claim 9, the combined art teaches the system of claim 1, wherein the trainee computing device is configured to provide additional instructions to the trainee in response to the trainee encountering a problem during performance of a task step (‘946; Para 0069).
Claim 19 is rejected as the same reason with claim 9.
With respect to claim 10, the combined art teaches the system of claim 1, wherein the trainer computing device and the trainee computing device are each one of a laptop, a tablet, and a smartphone (‘946; Para 0107).
Claim 20 is rejected as the same reason with claim 10.
Response to Arguments
Applicant’s arguments with respect to claim(s) have been considered but are moot because the new ground of rejection does not rely on any reference of Shavit applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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/HIEP V NGUYEN/Primary Examiner, Art Unit 3686
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