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 Objections
Claim 16 objected to because of the following informalities:
Claim 16, line 1, “completed training program” should read “the one or more completed training programs”.
Appropriate correction is required.
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 1-20 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. The claims are directed to at least one of abstract idea groupings, according to the 2019 Revised Patent Subject Matter Guidelines (Mathematical Concepts, Mental Processes and/or Certain Methods of Organizing Human Activity). Further, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception as discussed below.
Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance
More specifically, regarding Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, the claims are directed to a system and/or process, which is are statutory categories of invention.
Step 2A-1 of the 2019 Revised Patent Subject Matter Eligibility Guidance
Next, the claims are analyzed to determine whether it is directed to a judicial exception.
Independent claim 1 recites the following, with the abstract ideas highlighted in bold, including an indication as to the abstract idea grouping(s) to which the indicated limitations belong to, according to the 2019 Revised Patent Subject Matter Guidelines. Independent claims 8 and 17, having substantially similar features, were also analyzed and to which the following conclusion is also applicable:
A system, comprising: a user device; a data collection device communicatively coupled to the user device; a processor configured with instructions stored on non-transitory memory that, when executed, cause the processor to: receive user characterization input of a subject; receive, from the data collection device, one or more of one or more evaluation movements of the subject captured by the data collection device and measured movement features; automatically output one or more biomechanical scores, each biomechanical score based on more than one measured movement features; output a comprehensive score based on the one or more biomechanical scores, the comprehensive score indicative of a physical ability of the subject; and output a training program, the training program individualized to the subject and configured to increase the comprehensive score of the subject.
The limitations in claim 1 (as well as claims 8 and 17) recites an abstract idea included in the groupings of mental processes, connected to technology only through application thereof using generic computing elements (e.g., user device such as a computer, data collection device such as a smartwatch, etc.) and/or insignificant extra-solution activity. According to the 2019 Revised Patent Subject Matter Guidelines:
Mental Processes include concepts performed in the human mind (including an observation, evaluation, judgement, opinion);
Specifically, the instant claims include functions/limitations, as highlighted in the independent claim above, that constitute at least:
D. Concepts performed in the human mind (e.g., “receive user characterization input of a subject, receive one or more evaluation movements of the subject, output one or more biomechanical scores, etc.”), which is an abstract idea included in the grouping of Mental Processes. These limitations are interpreted as at least Mental Processes insomuch as the claim limitations are directed to steps/concepts which are capable of being performed in the human mind, while only generically connected to interaction with a computer utilizing non-special purpose generic computing elements and/or insignificant extra-solution activity as set forth in the claims.
Regarding dependent claims 2-7, 9-16, and 18-20:
Each claim is dependent either directly or indirectly from the independent claim identified above and includes all the limitations of said independent claim. Therefore, each dependent claim recites the same abstract idea as identified above. Each of the dependent claim further describes additional aspects of the abstract idea, i.e., additional aspects to the Mental Processes. For example, some dependent claims merely provide additional Mental Processes to be performed and/or additional insignificant extra-solution activity, without anything more significant to establish eligibility under 35 U.S.C. 101.
Step 2A-2 of the 2019 Revised Patent Subject Matter Eligibility Guidance
The second prong of step 2a is the consideration if the claim limitations are directed to a practical application.
Limitations that are indicative of integration into a practical application:
-Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
-Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
-Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
-Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
-Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo
Limitations that are not indicative of integration into a practical application:
-Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)
-Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)
-Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
Claims 1-20 clearly do not improve the functioning of a computer, as they only incorporate generic computing elements, do not effect a particular treatment, and do not transform or reduce a particular article to a different state or thing. Similarly, there is no improvement to a technical field. In addition the claims do not apply the judicial exception with, or by use of a particular machine. The claims do not apply or use the judicial exception in a meaningful way. The claimed invention does not suggest improvements to the functioning of a computer or to any other technology or technical field (see MPEP 2106.05 (a)).
This judicial exception is not integrated into a practical application because the claimed invention merely applies the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform the abstract idea (MPEP 2106.05 (f)) and/or generally links the use of the judicial exception to a particular technology or field of use (MPEP 2106.05 (h)). The claimed computer components are recited at a level of generality and are merely invoked as tool to perform the abstract idea. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea.
For the reasons as discussed above, the claim limitations are not integrated to a practical application.
Step 2b of the 2019 Revised Patent Subject Matter Eligibility Guidance
Next, the claims as a whole are analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because no element or combination of elements is sufficient to ensure any claim of the present application as a whole amounts to significantly more than one or more judicial exceptions, as described above. For example, the recitations of utilization of “user device such as a computer, data collection device such as a smartwatch”, etc. used to apply the abstract idea merely implements the abstract idea at a low level of generality and fail to impose meaningful limitations to impart patent-eligibility. These elements and the mere processing of data using these elements do not set forth significantly more than the abstract idea itself applied on general purpose computing devices. The recited generic elements are a mere means to implement the abstract idea. Thus, they cannot provide the “inventive concept” necessary for patent-eligibility. “[I]f a patent’s recitation of a computer amounts to a mere instruction to ‘implement]’ an abstract idea ‘on ... a computer,’... that addition cannot impart patent eligibility.” Alice, 134 S. Ct. at 2358 (quoting Mayo, 132 S. Ct. at 1301). As such, the significantly more required to overcome the 35 U.S.C. 101 hurdle and transform the claimed subject matter into a patent-eligible abstract idea is lacking. Accordingly, the claims are not patent-eligible.
Further, in order to be eligible the claims would require structure that is beyond generic. See Alice Corp. v. CLS Bank International, 134 S. Ct. at 2358-59. The elements of user device such as a computer and data collection device such as a smartwatch are well known conventional devices used to electronically manage data as evidence by LEAHY et al. (US 20150309696 A1; hereinafter Leahy). Leahy discloses that a conventional computers is used to store, read, and modify data (¶25). See Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018). In regards to wearable devices, such as smartwatches, TEN KATE et al. (US 20210378550 A1) shows that conventional wearable devices (such as smartwatches) use sensors to collect data (¶32).
The dependent claims do not add “significantly more” for at least the same reasons as directed to their respective independent claims, at least based on the position, as discussed above, that each of the dependent claims merely provide additional limitations to further expand the abstract idea of the independent claims, without adding anything which would establish eligibility under 35 U.S.C. 101.
Consequently, consideration of each and every element of each and every claim, both individually and as an ordered combination, leads to the conclusion that the claims are not patent-eligible under 35 USC §101.
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)(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.
Claims 1, 2, 6-11, and 14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Dunn et al. (US 20160300347 A1; hereinafter Dunn).
Regarding claim 1, Dunn discloses a system, comprising: a user device (smartphone, tablet, laptop; ¶21); a data collection device communicatively coupled to the user device (image capturing device; ¶21); a processor configured with instructions stored on non-transitory memory (¶74) that, when executed, cause the processor to: receive user characterization input of a subject (system receives input of a subject, such as sex, age, and height; ¶77); receive, from the data collection device, one or more of one or more evaluation movements of the subject captured by the data collection device and measured movement features (monitoring user during multiple tests that include movement; ¶78-79); automatically output one or more biomechanical scores (scoring movement patterns on a scale of 0 to 3; ¶56), each biomechanical score based on more than one measured movement features (the system itself may monitor a subject’s angles for different movement; ¶88); output a comprehensive score based on the one or more biomechanical scores (overall composite score in ¶59 based on scoring from movement patterns in ¶56), the comprehensive score indicative of a physical ability of the subject (indicates overall power output of each subject; ¶59); and output a training program, the training program individualized to the subject and configured to increase the comprehensive score of the subject (specific correctives applied to correct these deficits of the user, which includes exercises; ¶12-13).
Regarding claim 2, Dunn discloses wherein the data collection device is one or more of a native camera of a smartphone or tablet, a motion capture system, and a computer vision system (¶73), and the instructions include to automatically identify and measure movement features (automatic; ¶32-33) from the one or more evaluation movements received from the data collection device (setup for monitoring a subject and measures movement features because it monitors subject's hip height in ¶100 and angles in ¶88).
Regarding claim 6, Dunn discloses wherein the one or more biomechanical scores correspond to a strategy (strategy is being calculated by how a body part is being used or positioned while performing the movement in the specification of the present invention, which maps to how movement of trunk/lumbar rotation and pelvic lateral shift is considered along with the magnitude of the deviations from these; ¶55-56) and a stability of an anatomical feature (stability of an anatomical feature by providing some indication of the balance of the muscles during a plank test; ¶49 and ¶51).
Regarding claim 7, Dunn discloses wherein each of the one or more biomechanical scores further includes a left score and right score (the Side Plank Test for each side is scored separately; ¶52).
Regarding claim 8, Dunn discloses a method, comprising: assigning test scores to an athlete within one or more wellbeing categories based on inputs from one or more of movements recorded by a data collection device, survey evaluations, and third party data (scores assigned from scoring movement patterns on a scale of 0 to 3; ¶56); determining one or more wellbeing categorical scores from the test scores (cumulative score with every repetition of a movement; ¶57-58), wherein each of the one or more wellbeing categorical scores are determined from the test scores of a corresponding category (these are determined from movement patterns on a scale of 0 to 3 acting as biomechanical scores; ¶56); calculating a comprehensive score based on the one or more wellbeing categorical scores (scores from FST, SUT, SLST, SLHT, PT, and SPT may be summed into an overall composite score; ¶59), the comprehensive score indicating a physical ability of the athlete (indicates overall power output of each subject; ¶59); outputting the comprehensive score to a display of a user device (providing scores for the user in ¶11 and this is done through a display in ¶21).
Regarding claim 9, Dunn discloses wherein the one or more wellbeing categorical scores (cumulative score with every repetition of a movement; ¶57-58) includes one or more of a biomechanical score, a performance score, a skill score, a wellness score, a medical score, and a demographic score (movement patterns on a scale of 0 to 3 acting as biomechanical scores or a medical score related to Pathokinematics; ¶55-56).
Regarding claim 10, Dunn discloses wherein determining the biomechanical score includes automatically identifying and measuring a plurality of movement features (automatically identifying and measuring in ¶32-33 and setup for monitoring a subject and measures movement features because it monitors subject's hip height in ¶100) from a video or picture recorded by the data collection device (image capturing step is automatically collected; ¶32).
Regarding claim 11, Dunn discloses wherein identifying and measuring the plurality of movement features includes identifying one or more of an anatomical feature, measuring an angle, rates of change and ranges of lengths and angles (monitoring angles in ¶88 and change in height in ¶100).
Regarding claim 14, Dunn discloses further comprising outputting one or more training programs each of the one or more training programs based on one of the one or more wellbeing categorical scores (specific correctives applied to correct these deficits of the user, which includes exercises in ¶12-13 and this is all done based on all other input from the program in ¶12).
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.
Claims 3 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Dunn in view of FLACTION et al. (US 20160051858 A1; hereinafter Flaction).
Regarding claim 3, Dunn discloses wherein instructions further include to receive one or more of third party data (links to third party websites for accessing data; ¶22).
Dunn does not explicitly disclose wherein instructions further include to receive one or more of survey evaluations. However, Flaction teaches wherein instructions further include to receive one or more of survey evaluations (state of the art shows that questionnaires are used in order to get create training plans for users; ¶12).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Dunn to implement the teachings of Flaction for the benefit of implementing data from different sources that will contribute to an improved distribution or determination of scores.
Regarding claim 4, Dunn discloses wherein the user characterization input includes physical traits of the subject and/or a survey evaluation (system receives input of a subject, such as sex, age, and height; ¶77).
Dunn does not explicitly disclose wherein instructions further include to output a list of evaluation movements based on the received user characterization input. However, Flaction teaches wherein instructions further include to output a list of evaluation movements based on the received user characterization input (proposing several initial exercises to the user based on personal user data entered by the user; ¶102-103).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Dunn to implement the teachings of Flaction for the benefit of personalizing the evaluation. The system will be improved because it will be able to evaluate movements that are more relevant to the user’s specific characteristics.
Claims 5, 13, 15-17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Dunn in view of Omid-Zohoor et al. (WO 2023039185 A1; hereinafter Omid).
Regarding claim 5, Dunn does not disclose wherein instructions to output the training program include a machine learning algorithm configured to output the training program based on the one or more biomechanical scores.
However, Omid teaches wherein instructions to output the training program include a machine learning algorithm configured to output the training program based on the one or more biomechanical scores (machine learning algorithm to train motion scoring models based on data in ¶232 and an exemplary algorithm uses data such as biomechanical parameters in ¶219-220).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Dunn to implement the teachings of Omid for the benefit integrating multiple sources of data in a more efficient way. Furthermore, machine learning algorithms will be able to determine scores and how they relate to multiple parameters, while also training the scoring models through the data collected.
Regarding claim 13, Dunn discloses determining the one or more wellbeing categorical scores (cumulative score with every repetition of a movement; ¶57-58).
Dunn does not disclose determining the one or more wellbeing categorical scores using a machine learning and/or artificial intelligence algorithm. However, Omid teaches determining the one or more wellbeing categorical scores using a machine learning and/or artificial intelligence algorithm (machine learning techniques used for scoring; ¶232 and ¶253).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Dunn to implement the teachings of Omid for the benefit integrating multiple sources of data in a more efficient way. Furthermore, machine learning algorithms will be able to determine scores and how they relate to multiple parameters, while also training the scoring models through the data collected.
Regarding claim 15, Dunn does not explicitly disclose further comprising assigning new test scores after the athlete completes the one or more training programs.
However, Omid teaches further comprising assigning new test scores after the athlete completes the one or more training programs (calculating output score after user performs one or more training exercises; ¶208).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Dunn to implement the teachings of Omid for the benefit of training the scoring model to improve its efficiency. Machine learning algorithms will be able to determine scores and how they relate to multiple parameters, while also training the scoring models through the data collected through repetition.
Regarding claim 16, Dunn does not explicitly disclose wherein the new test scores and completed training program are used to train a machine learning and/or artificial intelligence algorithm configured to output new training programs based on the one or more wellbeing categorical scores.
However, Omid teaches wherein the new test scores and completed training program are used to train a machine learning and/or artificial intelligence algorithm (training is done to update the algorithm because each exercise that was performed add this change in output score to the corresponding element of the exercise vector in ¶211-212 and it trains motion scoring models based on data in ¶232) configured to output new training programs based on the one or more wellbeing categorical scores (by updating the exercise vector, it will provide different programs; ¶212).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Dunn to implement the teachings of Omid for the benefit of training the scoring model to improve its efficiency. Machine learning algorithms will be able to determine scores and how they relate to multiple parameters, while also training the scoring models through the data collected through repetition.
Regarding claim 17, Dunn discloses a method, comprising: determining one or more biomechanical scores from user evaluation movements of an athlete (scoring movement patterns on a scale of 0 to 3; ¶56); calculating a comprehensive score from the weighted biomechanical scores (overall composite score in ¶59 based on scoring from movement patterns in ¶56); storing the comprehensive score (scores need to be stored in order to be reported or displayed later; providing scores for the user in ¶11 and this is done through a display in ¶21); and determining a progress of the athlete based on a change in the comprehensive score (composite score can determine whether a subject has the green light to return or if the composite score doesn’t show enough progress then they get a red light; ¶64).
Dunn does not explicitly disclose weighting the one or more biomechanical scores. However, Omid teaches weighting the one or more biomechanical scores (there is weighted consideration for all relevant aspects of the motion in ¶141, which includes biomechanical parameters because it corresponds to motion data of body segments in ¶140 and ¶145).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Dunn to implement the teachings of Omid for the benefit of properly determining a different level of importance for different scores. There will be some scores that will be more important than others and therefore, they need a proper level of importance added by weighing them.
Regarding claim 19, Dunn does not disclose further comprising determining a relative ability of the athlete relative to a second athlete based on comparing the comprehensive score of the athlete to a comprehensive score of the second athlete.
However, Omid teaches further comprising determining a relative ability of the athlete relative to a second athlete based on comparing the comprehensive score of the athlete to a comprehensive score of the second athlete (comparing how a participant's score ranks against the range of scores for other players; ¶272).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Dunn to implement the teachings of Omid for the benefit of properly determining different level of strengths and deficiencies for a user. By comparing scores between different users, the system can determine if certain scores are above or below comparable users.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Dunn in view of Prinsen et al. (US 20240404102 A1; hereinafter Prinsen).
Regarding claim 12, Dunn does not disclose determining if each of the plurality of movement features are identified and measured, and response to each of the plurality of movement features not being identified and measured, requesting additional input from a user.
However, Prinsen teaches determining if each of the plurality of movement features are identified and measured, and response to each of the plurality of movement features not being identified and measured, requesting additional input from a user (measuring angles with respect to landmarks in ¶30 and a user needs to provide more input by adjusting their pose when landmarks are not visible in ¶50).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Dunn to implement the teachings of Omid for the benefit of collecting the data properly. If input was taken by the system, but the input is not complete or identified, then the user needs to provide the input again. If input is not provided, then the measurements won’t be accurate.
Claims 18 is rejected under 35 U.S.C. 103 as being unpatentable over Dunn in view of Omid as applied to claim 17 above, and further in view of Mobbs et al. (US 20210228111 A1; hereinafter Mobbs).
Regarding claim 18, Dunn does not disclose wherein weighting the one or more biomechanical scores is based on a user characterization input, and wherein the user characterization input includes one or more of a weight, sex, height, and sport of the athlete.
However, Mobbs teaches wherein weighting the one or more biomechanical scores (scores for step count, gait velocity, step distance, and posture and these are weighted; ¶8 and ¶82) is based on a user characterization input, and wherein the user characterization input includes one or more of a weight, sex, height, and sport of the athlete (system can include one or more models that utilize personal characteristics to determine model-based metrics; ¶68).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Dunn to implement the teachings of Mobbs for the benefit of properly determining a different level of importance for different scores. There will be some scores that will be more important than others and therefore, they need a proper level of importance added by weighing them.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Dunn in view of Omid as applied to claim 17 above, and further in view of Robbins et al. (WO 2022103406 A1; hereinafter Robbins).
Regarding claim 20, Dunn does not disclose wherein weighting the one or more biomechanical scores includes weighting based on an output of a machine learning algorithm.
However, Robbins teaches wherein weighting the one or more biomechanical scores (postures corresponding to the movement captured including distance and angles; ¶15-17) includes weighting based on an output of a machine learning algorithm (input distances and angles into a weighted algorithm used to perform the weighing; ¶43).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Dunn to implement the teachings of Robbins for the benefit of properly determining a different level of importance for different scores. There will be some scores that will be more important than others and therefore, they need a proper level of importance added by weighing them. Furthermore, using a machine learning algorithm to determine the weighing will be more efficient because the system will be able to determine the importance of each metric through the data received.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSE ANGELES whose telephone number is (703)756-5338. The examiner can normally be reached Mon-Thu 8am-5pm.
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, Dmitry Suhol can be reached at (571) 272-4430. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JOSE ANGELES/Examiner, Art Unit 3715 /Jay Trent Liddle/Primary Examiner, Art Unit 3715
Prosecution Insights