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 .
Notice to Applicant
This communication is in response to the amendment filed 7/3/25. Claims 1-4, 7, 9-12, 19, and 20 have been amended. Claims 6 and 8 are canceled. Claims 1-5, 7, and 9-20 are pending.
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, 7, and 9-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-5, 7, and 9-18 are directed to a method (i.e., a process), claim 19 is directed to an apparatus (i.e., a machine), and claim 20 is directed to a non-transitory computer-readable storage medium (i.e., a machine). Accordingly, claims 1-5, 7, and 9-20 are all within at least one of the four statutory categories.
Step 2A - Prong One:
Regarding Prong One of Step 2A, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and/or c) mathematical concepts.
Representative independent claim 1 includes limitations that recite at least one abstract idea. Specifically, independent claim 1 recites:
1. A method performed by a processor of an apparatus for providing exercise load information of a target entity, comprising: obtaining, by the processor, a target data set of the target entity for a target exercise session including a plurality of time units, wherein the target data set includes a sequence of kinematic information of the target entity for each of the plurality of time units; inputting, by the processor, the target data set to an artificial neural network (ANN); and determining, by the processor, based on an output from the ANN, an estimated load index reflecting a level of exercise load of the target entity for the target exercise session, wherein the kinematic information comprises first kinematic information related to a velocity of the target entity and second kinematic information related to an acceleration of the target entity, and wherein the first kinematic information comprises information related to a magnitude of velocity in a target time unit, and information related to an angular change between a direction of velocity in a time unit prior to the target time unit and a direction of velocity in the target time unit.
The Examiner submits that the foregoing underlined limitations constitute “a mental process” because obtaining a target data set of the target entity for a target exercise session including a plurality of time units, wherein the target data set includes a sequence of kinematic information of the target entity for each of the plurality of time units; determining based on an output an estimated load index reflecting a level of exercise load of the target entity for the target exercise session, wherein the kinematic information comprises first kinematic information related to a velocity of the target entity and second kinematic information related to an acceleration of the target entity, and wherein the first kinematic information comprises information related to a magnitude of velocity in a target time unit, and information related to an angular change between a direction of velocity in a time unit prior to the target time unit and a direction of velocity in the target time unit amount to observations/evaluations/judgments/analyses that can, at the currently claimed high level of generality, be practically performed in the human mind or via pen and paper.
Accordingly, the claim recites at least one abstract idea.
Step 2A - Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. It must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The limitations of claims 1, 19, and 20, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting a processor, an apparatus, a memory, and a non-transitory computer-readable storage medium used to perform the limitations, nothing in the claim elements precludes the steps from practically being performed in the mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the processor, apparatus, memory, and non-transitory computer-readable storage medium are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of obtaining data, inputting data, and determining data) such that it amounts no more than mere instructions to apply the exception using generic computer components. The limitations regarding an “artificial neural network (ANN)“ simply recites a judicial exception with the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use 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 not more than a drafting effort designed to monopolize the exception (see MPEP § 2106.05). Their collective functions merely provide conventional computer implementation.
Claims 2-5, 7, and 9-18 are ultimately dependent from Claim(s) 1 and include all the limitations of Claim(s) 1. Therefore, claim(s) 2-5, 7, and 9-18 recite the same abstract idea. Claims 2-5, 7, and 9-18 describe further limitations regarding determining a representative load index of the sports team, training data sets, measuring position information, what the kinematic information comprises, wherein the kinematic information is expressed based on a field- based coordinate system, wherein the kinematic information is expressed based on an entity- based coordinate system, wherein the estimated load index is an estimate of a ratio of perceived exertion (RPE), and wherein the target data set is configured to have a predetermined length by performing zero-padding. These are all just further describing the abstract idea recited in claim 1, without adding significantly more.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Step 2B:
Regarding Step 2B, independent claims 1, 19, and 20 do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
Regarding the additional limitations directed to a processor obtaining a data set and determining an estimated load index, all of which the Examiner submits merely add insignificant extra-solution activity to the abstract idea or are claimed in a merely generic manner (e.g., at a high level of generality), the Examiner further submits that such steps are not unconventional as they merely consist of receiving and transmitting data over a network, storing and retrieving information in memory, and performing repetitive calculations. See MPEP 2106.05(d)(II).
The dependent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
Therefore, claims 1-5, 7, and 9-20 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 10 and 13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 13 recite the limitation "the magnitude of jerk" in line 2. There is insufficient antecedent basis for this limitation in the claims.
Claims 10 and 13 recite “a direction of velocity in the time unit prior to the target time unit and a direction of velocity in the target time unit.” However, independent claim 1 recites “a direction of velocity in a time unit prior to the target time unit and a direction of velocity in the target time unit.” As such, it is unclear if the “direction” recited in claims 10 and 13 is the same “direction” recited in claim 1, or different.
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) 1-5, 9, 10, 16, 17, 19, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Omid-Zohoor et al. (US 2019/0224528 A1) in view of Toyooka et al. (US 2016/0198956 A1).
(A) Referring to claim 1, Omid-Zohoor discloses A method performed by a processor of an apparatus for providing exercise load information of a target entity, comprising (Fig. 2, para. 9-11, 228, 244, & 299 of Omid-Zohoor):
obtaining, by the processor, a target data set of the target entity for a target exercise session including a plurality of time units, wherein the target data set includes a sequence of kinematic information of the target entity for each of the plurality of time units (para. 60, 326, 341, 195, 229, 355, & 362 of Omid-Zohoor; note a human motion monitoring system capable of generating, displaying on a user interface, and storing (in the cloud) display graphs and data of previously captured motions, time series of kinematic sequence (rotational velocities), pelvic bend, pelvis angles, upper body angles, spine rotation, etc. Using specific algorithms, the system may generate markers of motion-specific key frames, such as impact, for each of the generated graphs. The prescribed workout (e.g., regime file) may be pushed to each user's participant device or provided on a website accessible by a web browser. The prescribed workout may be identical for each user, or individually customized to each user based on performance data associated with each user. In other words, each user may be prescribed the same exercise at the same time (e.g., squats for a one minute time period); however, the prescribed workout for each of the users may be customized based on performance data associated with that particular user (e.g., advanced user may be prescribed 15 squats in the one minute time period, while a novice user may be prescribed 10 squats—in this way all users in the workout are performing the same exercise at the same time). FIG. 33 shows an Activity screen 3300 that is available by pressing a UI element labeled Activity 3305, which is a tab located on the display. The Activity screen 3300 displays a list of UI elements consisting of any Training Programs 3310 (regimes) trained by a participant during a given time period (e.g., week or year).);
inputting, by the processor, the target data set to an artificial neural network (ANN) (para. 109 & 308-312 of Omid-Zohoor; FIG. 22A is a block diagram of scoring motion data inputs using trained classification or regression models trained using a traditional machine learning approach which leverages hand-engineered feature extraction. As shown in exemplary FIG. 21A, the motion scoring model training may use a traditional machine learning algorithm that leverages a hand-engineered feature extraction technique. The hand-engineered feature may include, but is not limited to, summary statistics, such as maximum rotational velocities, maximum accelerations, maximum body angles, average rotational velocities, average accelerations, average body angles, minimum rotational velocities, minimum accelerations, minimum body angles, etc. With such an approach, motion data training templates or examples with corresponding training labels (e.g., ground truth class labels for each training example when training a classification model, or ground truth numerical labels for each training example when training a regression model) are employed. FIGS. 22A and 22B are block diagrams of exemplary scoring motion data inputs using trained classification or regression models.); and
determining, by the processor, based on an output from the ANN, an index reflecting a level of exercise of the target entity for the target exercise session (para. 183, 304, 306-312, 321, & 327 of Omid-Zohoor; motion instruction system 1900 may be configured so that during exercise routines, real-time feedback or analysis may be provided to the user based on sensed data, including image data, about the user. In this manner, the system 1900 may function as a “virtual coach” to the user to help make exercising more interactive and help achieve results and goals of the user faster. In other words, such real time feedback may be based on any of a number of data inputs, such as personal data of the user, real-time exercise parameters of a current exercise session, and/or archived exercise parameters of past exercise sessions. The classification model may output discrete class categories, e.g., classifying an input motion as “expert”, “novice”, or “beginner”. The classification model may include, but is limited to, logistic regression, decision trees, decision forests, support vector machines, naïve bayes, k-nearest neighbors, and convolutional neural networks. The regression model may output continuous values, e.g., assigning a numerical score to an input motion. The regression model may include, but is not limited to, linear regression, polynomial regression, k-nearest neighbors, and convolutional neural networks. Trained classification and regression models can then be used to score input motions.).
wherein the kinematic information comprises first kinematic information related to a velocity of the target entity and second kinematic information related to an acceleration of the target entity (para. 56, 259, & 309 of Omid-Zohoor; measures a particular signature of acceleration that occurs when a batter strikes a baseball may result in a particular signature of at least one of acceleration data, body segment orientation data, and rotational velocity data. The motion scoring model training may use a traditional machine learning algorithm that leverages a hand-engineered feature extraction technique. The hand-engineered feature may include, but is not limited to, summary statistics, such as maximum rotational velocities, maximum accelerations, maximum body angles, average rotational velocities, average accelerations, average body angles, minimum rotational velocities, minimum accelerations, minimum body angles, etc.), and
wherein the first kinematic information comprises information related to a magnitude of velocity in a target time unit, and information related to an angular change between a direction of velocity in a time unit prior to the target time unit and a direction of velocity in the target time unit (para. 207-209, 180, & 35 of Omid-Zohoor; parameters calculated at time of impact, such as position and orientation of the club face relative to the ball and velocity vector and face angle of the club, can be used by the system to predict the forces on the ball and thus predict its trajectory. Knowledge of the terrain can allow determination of the distance and path of the struck golf ball, or alternatively the calculation can predict distance assuming the terrain is flat. Such predictions based purely on force calculations can be supplemented with information about the behavior of the ball in atmosphere, such as through testing of particular types of golf balls, to adjust for air resistance. In a further variation, wind direction and velocity can be taken into account. The stepped frame animation is a useful device for illustrating the plane, path or arc of a motion or component of motion, and is a further enhancement of the presentation. Selected positions of a point or object or portion of the video screen are retained as the video progresses so as to show the path leading up to the present position. The stepped aspect of the presentation can be done as function of time, or of linear or angular displacement of the object or point of interest, whichever better serves to illustrate the path of motion best for the viewer. Club Performance: This parameter relates to the linear acceleration of the club, added to peak angular release velocity. The benchmark is 300 mph (miles per hour) for linear acceleration and 400 degrees/second of angular velocity. The simple sum, 700, is equated to a maximum performance index of 50, and the measured value scored accordingly. Club Accuracy: This parameter relates to the three dimensional movement of the club on the ball and is graded on the velocity of the straight-on axis less the velocities in each of the orthogonal axis, in miles per hour. The total is compared to a benchmark and the result scaled to a maximum performance index of 50.).
Omid-Zohoor does not expressly disclose an estimated load index reflecting a level of exercise load. However, this feature is old and well-known, as evidenced by Toyooka (see para. 56 & 61 of Toyooka which discloses information about the exercise load of the subject and the current rating of perceived exertion (RPE value)).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify the index of Omid-Zohoor to include the aforementioned features of Toyooka. The motivation for doing so would have been to indicate how tough the current exercise is and accurately determine whether an exercise load is appropriate or not (abstract & para. 56 of Toyooka).
(B) Referring to claim 2, Omid-Zohoor discloses wherein the target entity represents a sports team, and the method further comprises: determining, by the processor, a representative index of the sports team for the target exercise session based on estimated indices for some sports players belonging to the sports team (para. 299, 303, & 346 of Omid-Zohoor).
Omid-Zohoor does not expressly disclose load index/indices. However, this feature is old and well-known, as evidenced by Toyooka (see para. 56 & 61 of Toyooka which discloses information about the exercise load of the subject and the current rating of perceived exertion (RPE value)).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify the index of Omid-Zohoor to include the aforementioned features of Toyooka. The motivation for doing so would have been to indicate how tough the current exercise is and accurately determine whether an exercise load is appropriate or not (abstract & para. 56 of Toyooka).
(C) Referring to claim 3, Omid-Zohoor discloses wherein the ANN is trained based on a plurality of training data sets including a first training data set, and wherein the first training data set includes a sequence of kinematic information of a first entity for a first exercise session, and is labeled with a score collected from the first entity for exercise load of the first exercise session (para. 50, 306-311, and 313 of Omid-Zohoor).
(D) Referring to claim 4, Omid-Zohoor discloses further comprising: measuring, by the processor, position information of the target entity for the target exercise session using a positioning device corresponding to the target entity, wherein the kinematic information is generated based on the position information (para. 5, 173, & 174 of Omid-Zohoor).
(E) Referring to claim 5, Omid-Zohoor discloses wherein the kinematic information is measured using an inertial sensor corresponding to the target entity (para. 24, 39, & 147 of Omid-Zohoor).
(F) Referring to claim 9, Omid-Zohoor discloses wherein the second kinematic information comprises: information related to a magnitude of acceleration in the target time unit; and information related to an angular change between a direction of acceleration in the time unit prior to the target time unit and a direction of acceleration in the target time unit (para. 155, 180, 181, 207, 258, & 309 of Omid-Zohoor).
(G) Referring to claim 10, Omid-Zohoor discloses wherein the second kinematic information comprises: information related to a magnitude of acceleration in the target time unit; and information related to the angular change between a direction of velocity in the time unit prior to the target time unit and a direction of velocity in the target time unit (para. 155, 180, 181, 207, 209, 258, & 309 of Omid-Zohoor).
(H) Referring to claim 16, Omid-Zohoor discloses wherein the kinematic information comprises: information related to at least one of a rotational movement in a roll direction, a rotational movement in a pitch direction, or a rotational movement in a yaw direction of the target entity (para. 285 of Omid-Zohoor); and information related to at least one of angular velocity, angular acceleration, or angular jerk of the target entity (para. 207 of Omid-Zohoor).
(I) Referring to claim 17, Omid Zohoor does not disclose wherein the estimated load index is an estimate of a ratio of perceived exertion (RPE) of the target entity for the target exercise session.
Toyooka discloses wherein the estimated load index is an estimate of a ratio of perceived exertion (RPE) of the target entity for the target exercise session (para. 56 & 61 of Toyooka).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Omid-Zohoor to include the aforementioned features of Toyooka. The motivation for doing so would have been to indicate how tough the current exercise is and accurately determine whether an exercise load is appropriate or not (abstract & para. 56 of Toyooka).
(J) Claim 19 differs from claim 1 by reciting “An apparatus for providing exercise load information of a target entity, comprising: a memory; and a processor configured to” (Fig. 2, para. 9-11, 228, 299, 257, & 374 of Omid-Zohoor) and claim 20 differs from claim 1 by reciting “A non-transitory computer-readable storage medium having processor-executable instructions stored thereon, wherein the instructions, when executed by a processor, cause the processor to perform a method for providing exercise load information of a target entity, the method comprising“ (Fig. 2, para. 257, 374, & 376, 9-11, 228, 244, & 299 of Omid-Zohoor).
The remainder of claims 19 and 20 repeat substantially similar limitations as claim 1, and are therefore rejected for the same reasons given above.
Claim(s) 7 and 11-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Omid-Zohoor et al. (US 2019/0224528 A1) in view of Toyooka et al. (US 2016/0198956 A1), and further in view of Bartsch et al. (US 2020/0312461 A1).
(A) Referring to claim 7, Omid-Zohoor and Toyooka do not expressly disclose wherein the kinematic information further comprises third kinematic information related to a jerk of the target entity.
Bartsch discloses wherein the kinematic information further comprises third kinematic information related to a jerk of the target entity (para. 72 & 84 of Bartsch).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned feature of Bartsch within Omid-Zohoor and Toyooka. The motivation for doing so would have been for sensing and filtering impact data, analyzing the filtered impact data, and assessing the result of the impacts (para. 2 of Bartsch).
(B) Referring to claims 11-13, Omid-Zohoor and Toyooka do not disclose wherein the third kinematic information comprises: information related to a magnitude of jerk in the target time unit; and information related to an angular change between a direction of jerk in the time unit prior to the target time unit and a direction of jerk in the target time unit; wherein the third kinematic information comprises: information related to a magnitude of jerk in the target time unit; and information related to an angular change between a direction of acceleration in the time unit prior to the target time unit and a direction of acceleration in the target time unit; wherein the third kinematic information comprises: information related to the magnitude of jerk in the target time unit; and information related to the angular change between a direction of velocity in the time unit prior to the target time unit and a direction of velocity in the target time unit.
Bartsch discloses wherein the third kinematic information comprises: information related to a magnitude of jerk in the target time unit; and information related to an angular change between a direction of jerk in the time unit prior to the target time unit and a direction of jerk in the target time unit (para. 72, 73, & 84 Bartsch) wherein the third kinematic information comprises: information related to a magnitude of jerk in the target time unit; and information related to an angular change between a direction of acceleration in the time unit prior to the target time unit and a direction of acceleration in the target time unit (para. 72, 73, 84, 90, 91, & 124 Bartsch); and wherein the third kinematic information comprises: information related to the magnitude of jerk in the target time unit; and information related to the angular change between a direction of velocity in the time unit prior to the target time unit and a direction of velocity in the target time unit (para. 72, 73, 84, 90, 91, & 125 Bartsch).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned features of Bartsch within Omid-Zohoor and Toyooka. The motivation for doing so would have been for sensing and filtering impact data, analyzing the filtered impact data, and assessing the result of the impacts (para. 2 of Bartsch).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Omid-Zohoor et al. (US 2019/0224528 A1) in view of Toyooka et al. (US 2016/0198956 A1), and further in view of Gloudemans et al. (US 2009/0128568 A1).
(A) Referring to claim 14, Omid-Zohoor and Toyooka do not disclose wherein the kinematic information is expressed based on a field- based coordinate system, comprising: a first axis for a length of a field in which the exercise session is performed; and a second axis for a width of the field in which the exercise session is performed.
Gloudemans discloses wherein the kinematic information is expressed based on a field- based coordinate system, comprising: a first axis for a length of a field in which the exercise session is performed; and a second axis for a width of the field in which the exercise session is performed (para. 119, 122, & 134 of Gloudemans).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned features of Gloudemans within Omid-Zohoor and Toyooka. The motivation for doing so would have been to assist in building a model (para. 134 of Gloudemans).
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Omid-Zohoor et al. (US 2019/0224528 A1) in view of Toyooka et al. (US 2016/0198956 A1), and further in view of Chang et al. (US 2017/0188894 A1).
(A) Referring to claim 15, Omid-Zohoor and Toyooka do not disclose wherein the kinematic information is expressed based on an entity- based coordinate system, comprising: a forward-backward axis corresponding to a heading direction of the target entity; and a left-right axis corresponding to a side direction of the target entity.
Chang discloses wherein the kinematic information is expressed based on an entity- based coordinate system, comprising: a forward-backward axis corresponding to a heading direction of the target entity; and a left-right axis corresponding to a side direction of the target entity (para. 65-68 & 81 of Chang).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned features of Chang within Omid-Zohoor and Toyooka. The motivation for doing so would have been to detect anomalies in kinematic data (para. 81 of Chang).
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Omid-Zohoor et al. (US 2019/0224528 A1) in view of Toyooka et al. (US 2016/0198956 A1), and further in view of Srivastava et al. (US 2020/0075167 A1).
(A) Referring to claim 18, Omid-Zohoor and Toyooka do not disclose wherein the target data set is configured to have a predetermined length by performing zero-padding prior to the sequence of the kinematic information.
Srivastava discloses wherein the target data set is configured to have a predetermined length by performing zero-padding prior to the sequence of the kinematic information (see para. 93-95 and 101 of Srivastava).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned feature of Srivastava within Omid-Zohoor and Toyooka. The motivation for doing so would have been to make all input equal-sized (para. 101 of Srivastava).
Response to Arguments
Applicant's arguments filed 7/3/25 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed hereinbelow in the order in which they appear in the response filed 7/3/25.
(1) Reconsideration and withdrawal of the rejection under 35 U.S.C. § 101 are respectfully requested.
(2) Applicant argues that the cited references fails to disclose, suggest, or otherwise render obvious the claimed combination of features presently set forth in amended independent claim 1, including the combination of features of "the kinematic information comprises first kinematic information related to a velocity of the target entity and second kinematic information related to an acceleration of the target entity" and the “first kinematic information comprises information related to a magnitude of velocity in a target time unit, and information related to an angular change between a direction of velocity in a time unit prior to the target time unit and a direction of velocity in the target time unit."
(A) As per the first argument, see 101 rejection above. The Examiner submits that the foregoing underlined limitations in the 101 rejection above constitute “a mental process” because obtaining a target data set of the target entity for a target exercise session including a plurality of time units, wherein the target data set includes a sequence of kinematic information of the target entity for each of the plurality of time units; determining based on an output an estimated load index reflecting a level of exercise load of the target entity for the target exercise session, wherein the kinematic information comprises first kinematic information related to a velocity of the target entity and second kinematic information related to an acceleration of the target entity, and wherein the first kinematic information comprises information related to a magnitude of velocity in a target time unit, and information related to an angular change between a direction of velocity in a time unit prior to the target time unit and a direction of velocity in the target time unit amount to observations/evaluations/judgments/analyses that can, at the currently claimed high level of generality, be practically performed in the human mind or via pen and paper.The limitations of claims 1, 19, and 20, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting a processor, an apparatus, a memory, and a non-transitory computer-readable storage medium used to perform the limitations, nothing in the claim elements precludes the steps from practically being performed in the mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea. This judicial exception is not integrated into a practical application. In particular, the processor, apparatus, memory, and non-transitory computer-readable storage medium are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of obtaining data, inputting data, and determining data) such that it amounts no more than mere instructions to apply the exception using generic computer components. The limitations regarding an “artificial neural network (ANN)“ simply recites a judicial exception with the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Regarding the additional limitations directed to a processor obtaining a data set and determining an estimated load index, all of which the Examiner submits merely add insignificant extra-solution activity to the abstract idea or are claimed in a merely generic manner (e.g., at a high level of generality), the Examiner further submits that such steps are not unconventional as they merely consist of receiving and transmitting data over a network, storing and retrieving information in memory, and performing repetitive calculations. See MPEP 2106.05(d)(II). Furthermore, “analyzing and managing training sessions of sports players” is not a technical improvement.(B) As per the second argument, it is unclear how the language of the claim differs from the applied prior art. See 103 rejection of claim 1 above which explains the applied prior art. Furthermore, in response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., angular change between velocity vectors and the degree of load by changing the direction in place without movement by considering the angle change between the directions of the velocity at each point in time, and the exercise load can be determined by considering that the athlete feels different even when the forward or backward movement and the lateral movement are of the same size.) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
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.
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/LENA NAJARIAN/Primary Examiner, Art Unit 3687