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 .
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 1-5, 10-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The invention is directed to analyzing motion data captured by an imaging system and analyzing the movements to assess motor function of a patient. To that end, independent claim 1 recites outputting a prediction model. This limitation falls under the grouping of Mental Processes because a person can mentally observe the motion data a make a prediction or assessment of motor or executive function. Claim 1 further recites “training, based on a first portion of the motion data, the predictive model according to the plurality of features; testing, based on a second portion of the motion data, the predictive model”. This limitation merely describes training and testing a machine learning model using training and testing dataset. The stipulation of using a machine learning model is nothing more than an instruction to implement an abstract idea on a generic computer. Moreover, applying a machine learning model merely indicates a field of use or technological environment in which the judicial exception is performed, and this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning) described at a high level of generality and thus fails to add an inventive concept to the claims. Finally, the limitation “determining motion data associated with a plurality of movements, wherein the plurality of movements include one or more series of movements, wherein each series of movements of the plurality of movements is labeled according to a predefined feature of a plurality of predefined features” merely appears to be a data gathering step, i.e., image data of motion performed by a person. Data gathering is an insignificant extra-solution activity.
The claim does not recite any additional limitations that would integrate the abstract idea into a practical application nor does it prove an inventive concept.
Dependent claim 2 recites “wherein determining the motion data associated with the plurality of movements comprises retrieving the motion data from a public data source” which is merely a data gathering step which is an insignificant extra-solution activity.
Dependent claim 3 recites “wherein the plurality of movements comprise one or more of a walking set, a balancing set, a reflex set, or a motor speed set” which merely describes the data that is gathered.
Dependent claim 4 recites “determining, based on the plurality of movements, one or more movement data sets that comprise at least one movement of the plurality of movements; and generating, based on the one or more movement data sets, the motion data” which appears to fall under the grouping of Mental Processes because a person can visually inspect image or video data to determine the plurality of movements and identify the motion data.
Dependent claim 5 recites “wherein the motion data is comprised of movement data from a plurality of different movement data sets” which merely describes the gathered data.
Dependent claim 10 recites “wherein the plurality of features include at least one neurological assessment of one or more of motor function, balancing, reflex movement, sensory function, coordination, or gait” which merely describes the motion data that is gathered.
Independent claim 11 recites “wherein the plurality of movements are determined from a plurality of observed movements and determining, based on the predictive model, a neurological assessment of the subject” which falls under the grouping of Mental Processes because a person can mentally observe the motion data a make a prediction or assessment of motor or executive function. Claim 11 further recites “receiving baseline feature data associated with a plurality of movements of a subject and providing, to a predictive model, the baseline feature data” which are merely data gathering and data output steps which are insignificant extra solution activities. The claim stipulates using a machine learning model, which is nothing more than an instruction to implement an abstract idea on a generic computer. Moreover, applying a machine learning model merely indicates a field of use or technological environment in which the judicial exception is performed, and this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning) described at a high level of generality and thus fails to add an inventive concept to the claims.
Dependent claim 12 recites “wherein the neurological assessment comprises at least one of motor function, balancing, reflex movement, sensory function, coordination, or gait” which falls under the grouping of Mental Processes because a person or doctor can visually observe the motion data and make an assessment of motor function.
Dependent claim 13 recites “further comprising training the predictive model.” This limitation merely describes training and testing a machine learning model using training and testing dataset. The stipulation of using a machine learning model is nothing more than an instruction to implement an abstract idea on a generic computer. Moreover, applying a machine learning model merely indicates a field of use or technological environment in which the judicial exception is performed, and this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning) described at a high level of generality and thus fails to add an inventive concept to the claims
Claim Rejections - 35 USC § 112(b)
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 7-8, 18-19 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 pre-AIA the applicant regards as the invention. Claim 7 recites “determining, from the motion data, features present in two or more of the plurality of different movement data sets as a first set of candidate movements”. It is not clear what these features are which are present in two different movement data sets. Dependent claim 8 does not clarify what these features are. The specification does not appear to provide any clarification.
Furthermore, claim 7 recites “determining, from the motion data, features of the first set of candidate movements that satisfy a first threshold value as a second set of candidate movements; and determining, from the motion data, features of the second set of candidate movements that satisfy a second threshold value as a third set of candidate movements, wherein the plurality of features comprises the third set of candidate movements”. It is not clear what is being evaluated or what the threshold refers to.
Claims 18-19 are rejected for the same reasons as claims 7-8.
Claim Rejections - 35 USC § 102
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 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 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(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.
Claims 1-4, 6, 9-15, 17, 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by D1.1
With regard to claim 1, D1 teach determining motion data associated with a plurality of movements, wherein the plurality of movements include one or more series of movements, wherein each series of movements of the plurality of movements is labeled according to a predefined feature of a plurality of predefined features (see abstract, § 1 ¶ 3, § 3 ¶ 2: motion data comprising plurality of movements, § 3 ¶ 3: ground truth labels); determining, based on the motion data, a plurality of features for a predictive model (see § 3 ¶ 2, § 4 ¶ 1: features extracted); training, based on a first portion of the motion data, the predictive model according to the plurality of features (see abstract, § 4 ¶¶ 2-3: training based on the motion data; see table 1: dataset split into training and testing data); testing, based on a second portion of the motion data, the predictive model (see table 1, § 4 ¶ 2: testing based on second portion of data); and outputting, based on the testing, the predictive model (see abstract, § 4 ¶¶ 2-3, fig. 1: trained model).
With regard to claim 2, D1 teach wherein determining the motion data associated with the plurality of movements comprises retrieving the motion data from a public data source (see abstract, § 1 ¶ 3, § 4 ¶ 3: dataset).
With regard to claim 3, D1 teach wherein the plurality of movements comprise one or more of a walking set, a balancing set, a reflex set, or a motor speed set (see fig. 3, abstract: walking or gait movement).
With regard to claim 4, D1 teach wherein determining the motion data associated with the plurality of movements comprises: determining, based on the plurality of movements, one or more movement data sets that comprise at least one movement of the plurality of movements; and generating, based on the one or more movement data sets, the motion data (see § 3 ¶ 2, § 4 ¶ 1: motion data include plurality of movement segments).
With regard to claim 6, D1 teach wherein determining the motion data associated with the plurality of movements comprises: determining baseline feature levels for each series of movements of the plurality of movements (see § 4 ¶ 1: baseline features are extracted); labeling the baseline feature levels for each series of movements of the plurality of movements as at least one predefined feature of the plurality of predefined features; and generating, based on the labeled baseline feature levels, the motion data (see § 4 ¶ 1-3, fig. 1: features for each movement segment extracted and labeled) .
With regard to claim 9, D1 teach wherein training, based on the first portion of the motion data, the predictive model according to the plurality of features results in determining a feature signature indicative of at least one predefined feature of the plurality of predefined features (see § 4 ¶¶ 1-3: features are extracted and used as signatures in the motion prediction model).
With regard to claim 10, D1 teach wherein the plurality of features include at least one neurological assessment of one or more of motor function, balancing, reflex movement, sensory function, coordination, or gait (see abstract: motor function assessment).
With regard to claim 11, D1 teach receiving baseline feature data associated with a plurality of movements of a subject, wherein the plurality of movements are determined from a plurality of observed movements (see abstract, § 1 ¶ 3, § 3 ¶ 2: motion data comprising plurality of movements, see § 4 ¶ 1: baseline features are extracted); providing, to a predictive model, the baseline feature data; and determining, based on the predictive model, a neurological assessment of the subject (see fig 1. § 4 ¶¶ 1-3: baseline features are extracted and input into a prediction model to assess the motor function, see also abstract).
With regard to claim 12, D1 teach wherein the neurological assessment comprises at least one of motor function, balancing, reflex movement, sensory function, coordination, or gait (see abstract: motor function).
With regard to claim 13, D1 teach further comprising training the predictive model (see fig. 1: prediction model).
With regard to claim 14, D1 teach method of claim 13, wherein training the predictive model comprises: determining motion data associated with the plurality of movements, wherein the plurality of movements include one or more series of movements, wherein each series of movements of the plurality of movements is labeled according to a predefined feature of a plurality of predefined features (see abstract, § 1 ¶ 3, § 3 ¶ 2: motion data comprising plurality of movements, § 3 ¶ 3: ground truth labels); determining, based on the motion data, a plurality of features for the predictive model (see § 3 ¶ 2, § 4 ¶ 1: features extracted); training, based on a first portion of the motion data, the predictive model according to the plurality of features (see abstract, § 4 ¶¶ 2-3: training based on the motion data; see table 1: dataset split into training and testing set); testing, based on a second portion of the motion data, the predictive model; and outputting, based on the testing, the predictive model (see table 1, § 4 ¶ 2: testing based on second portion of data; see abstract, § 4 ¶¶ 2-3, fig. 1: trained model).
With regard to claim 15, D1 teach method of claim 14, wherein determining the motion data associated with the plurality of movements comprises: determining, based on the plurality of movements, one or more movement data sets that comprise at least one movement of the plurality of movements; and generating, based on the one or more movement data sets, the motion data (see § 3 ¶ 2, § 4 ¶ 1: motion data include plurality of movement segments).
With regard to claim 17, see discussion of claim 6.
With regard to claim 20, see discussion of claim 9.
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 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.
Claims 5 and 16 is rejected under 35 U.S.C. 103 as being unpatentable over D1 and further in view of D2.2
With regard to claim 5, D1 fails to explicitly teach wherein the motion data is comprised of movement data from a plurality of different movement data sets, however D2 teach the missing features (see abstract, §4: movement data collected for different tasks including sailor step and ball drop).
One skilled in the art would have found it obvious to combine the teachings to arrive at the claimed invention. D1 is related to analyzing gait movement to assess executive function. Separately, D2 teaches analyzing sailor step and ball drop movements to assess executive functioning. One skilled in the art would have been motivated to incorporate additional tasks as taught by D2 into the configuration of D1 yielding predictable results. In particular, it would have been obvious to incorporate additional tasks such as sailor step or ball drop tasks for evaluating executive function in addition to the gait analysis described in D1. The motivation would have been to enhance assessment of executive function by evaluating a plurality of movement tasks.
With regard to claim 16, see discussion of claim 5.
Conclusion
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/AVINASH YENTRAPATI/Primary Examiner, Art Unit 2672
1 Zaki Zadeh, Mohammad, et al. "Automated system to measure Tandem Gait to assess executive functions in children." Proceedings of the 14th Pervasive Technologies Related to Assistive Environments Conference. 2021.
2 Dillhoff, Alex, et al. "An automated assessment system for embodied cognition in children: from motion data to executive functioning." Proceedings of the 6th international Workshop on Sensor-based Activity Recognition and Interaction. 2019.
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