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 .
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.
Information Disclosure Statement
The information disclosure statement filed 07 April 2025 fails to comply with 37 CFR 1.98(a)(2), which requires a legible copy of each cited foreign patent document; each non-patent literature publication or that portion which caused it to be listed; and all other information or that portion which caused it to be listed. It has been placed in the application file, but the information referred to therein has not been considered. In particular, the copy of the cited foreign patent document only includes the abstract/cover page and the international search report. It is missing the body of the document.
Specification
The disclosure is objected to because of the following informalities:
Para. 40 improperly places a comma after the term “and”.
The specification recites multiple initialisms without the fully written term. Examples include “GBM” and “BMI”. The first instance of an abbreviation, acronym, or initialism should be accompanied by the fully written term.
Para. 112 recites “horizontal plain”. This should be “horizontal plane”.
Appropriate correction is required.
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Objections
Claims 1-20 are objected to because of the following informalities:
Claims 1, 2, 5-7, 13, and 16-19 improperly place a comma after the term “and” at the end of the penultimate limitation of each of these claims.
Claim 14 is missing the term “and” at the end of the penultimate limitation.
Dependent claims 2-15 and 17-20 inherit the deficiencies of their respective parent claims, and are thus objected to under the same rationale.
Appropriate correction is required.
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.
Claims 1-15 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 1 recites the limitation "the input unit" in line 9 of the claim. There is insufficient antecedent basis for this limitation in the claim. Dependent claims 2-15 inherit the deficiencies of their respective parent claims, and are thus rejected under the same rationale.
Claim 4 recites the limitation "the housing" in line 2 of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 9 recites the limitation "the weights" in line 1 of the claim. Independent claim 1 recites the singular “a weight”, not plural. Therefore, there is insufficient antecedent basis for this limitation in the claim.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claims 1-3, 7, 8, 16, 17, and 19, the disclosure fails to provide sufficient written description for “analyze the workout data and the user data to assess a user workout according to predetermined workout parameters; generate a feedback notification” in claims 1 and 16, “analyze the historical workout data to determine trends; recognize patterns in the historical workout data; generate a personalized recommendation message based on the patterns recognized in the historical workout data” in claims 2 and 17, “wherein the personalized recommendation message describes a personalized workout recommendation” in claim 3, “adjust the personalized recommendation for the user” in claims 7 and 19, “wherein the server analyzes the workout data in real-time and provides real-time feedback notifications” in claim 8 to show one of ordinary skill in the art that Applicant had possession of the claimed invention. Claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See MPEP 2161.01(I). The specification, at best, merely recites that this function is performed without providing sufficient, if any, description of the steps, calculations, or formulas necessary to perform the claimed functionality. See, for example, at least para. 65 which recites that feedback “can include adjustments to form, pace, or intensity to maximize efficacy, safety or the like” while para. 73 recites that recommendations “can include adjustments to exercise regimen, intensity levels, rest intervals, or the like”. However, the disclosure is silent regarding any analysis of collected data with respect to form, pace, or intensity to maximize efficacy, safety or the like while para. 72 summarily recites that “[p]atterns are recognized using time series techniques combined with machine learning for classification to a set of known progress patterns such as regression, stagnation, mild improvement, significant improvement, consistency, recovery, overtraining, injury risk or the like” without any meaningful description, let alone disclosure of the machine learning itself. Similarly, at least para. 83 merely recites in results-based language that user feedback and performance from benchmark data are integrated back into the algorithm to refine future recommendations and improve accuracy, but the disclosure is silent regarding the algorithm itself. For instance, the closest language is found in para. 48 which recites that the “algorithm to construct the benchmark workout can be performed by using regression analysis and prediction of the workout ability of the user. The algorithm, for example, random forest, GBM or a neural network, or the like, would predict the weight based on historical workouts and would use classification models to select the class of number of sets and repetitions.” Thus, the disclosure does not provide the algorithm. Dependent claims 2-15 and 17-20 inherit the deficiencies of their respective parent claims, and are thus rejected under the same rationale.
Regarding claim 14, the disclosure fails to provide sufficient written description for “calculate a tilt angle relative to the horizontal plane; designate a tilt classification of the tilt angle; generate a feedback notice according to the tilt classification” to show one of ordinary skill in the art that Applicant had possession of the claimed invention. Claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See MPEP 2161.01(I). The specification, at best, merely recites that this function is performed without providing sufficient, if any, description of the steps, calculations, or formulas necessary to perform the claimed functionality. In particular, the claim and disclosure recite the use of a singular sensor moved space. However, the disclosure is silent regarding any analysis of the orientation of the sensor as it relates to proper motion of the weight for a particular exercise to identify improper tilt, let alone tilt in any particular axis. Only that it is based on a horizontal plane that is defined according to the gravity vector. Para. 110 does recite that angular velocity is used to assess the barbell’s orientation. However, this is only one end of the barbell and does not account for, let alone collect, data regarding the opposite end of the barbell and thus does not accurately reflect barbell orientation. Thus, the disclosure fails to provide sufficient written description under 35 USC 112(a).
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 including additional elements that are sufficient to amount to significantly more than the judicial exception itself.
Step 1
The claims are directed to a method and a product which fall under the four statutory categories (STEP 1: YES).
Step 2A, Prong 1
Independent claim 1 recites:
A system for assessing a strength resistance workout, the system comprising:
a sensor device configured to:
measure at least one workout signal relating to measurement data associated with a movement of a weight;
associate time data with the at least one workout signal;
a user device configured to:
receive workout data from the sensor device, the workout data comprising the at least one workout signal and the time data;
receive user data provided from the input unit;
a server configured to:
obtain the workout data and the user data from the user device;
analyze the workout data and the user data to assess a user workout according to predetermined workout parameters;
generate a feedback notification; and,
provide the feedback notification to the user device to facilitate presenting the feedback notification to a user.
Independent claim 16:
A method for assessing in real-time a user workout, the method comprising using at least one processor for:
obtaining workout data and user data from a user device;
analyzing the workout data and the user data to assess a user workout according to predetermined workout parameters;
generating a feedback notification; and,
providing the feedback notification to the user device to facilitate presenting the feedback notification to a user.
All of the foregoing underlined elements amount to the abstract idea grouping of a certain method of organizing human activity because it is managing personal behavior or interactions between people (including social activities, teaching, and following rules or instructions) by collecting information, analyzing the information, and outputting the results of the collection and analysis. They all also amount to the abstract idea grouping of mental processes as the claims, under their broadest reasonable interpretation, cover performance of the limitations in the mind with the aid of pen and paper because the claims, under their broadest reasonable interpretation, cover performance of the limitations in the mind but for the recitation of generic computer components. See MPEP 2106.04(a)(2)(III)(C) - A Claim That Requires a Computer May Still Recite a Mental Process.
The dependent claims, except for claim 4 and 9-12, amount to merely further defining the judicial exception.
Therefore, the claim recites a judicial exception. (STEP 2A, PRONG 1: YES).
Step 2A, Prong 2
This judicial exception is not integrated into a practical application because the claim does not include additional elements that are sufficient to integrate the exception into a practical application under the considerations set forth in MPEP 2106.04(d). The elements of the claims above that are not underlined constitute additional elements.
The following additional elements, both individually and as a whole, merely generally link the judicial exception to a particular technological environment or field of use: a system comprising a sensor device, a user device, and server (claim 1); the input unit (claim 1); a connection element for attaching the housing to a weight machine (claim 4); reciting the weights are part of a weight stack of a weight machine or of a free weight (claim 9); at least one sensor (claim 10); a distance measuring sensor (claim 11); an accelerometer (claim 12); at least one processor (claim 16); a user device (claim 16); a sensor device (claim 20); and an input unit of the user device (claim 20). This is evidenced by the manner in which these elements are disclosed. See, for example, at least Fig. 1-6 which illustrate the components as either non-descript black boxes or stock symbols in a conventional arrangement, and at least para. 139-147 in the specification which identify that the functions may be implemented in any combination of hardware, software, and/or firmware. The claims do not recite any limitations that improve the functionality of the computer system because the claimed steps are merely performing the steps of processing data but are not tied to improving any functionality of the computer system. Additionally, the claims do not recite any specific rules with specific characteristics that improve the functionality of the computer system. The system is merely recited to be used, not improved. For instance, the sensor device and user device, as claimed and organized, merely add insignificant extra solution activity to the judicial exception (e.g., mere data gathering and outputting in conjunction with a law of nature or abstract idea). Similarly, the weights recited to be part of a weight stack of a weight machine or of a free weight is merely nonfunctional descriptive material as the sensor device would perform the same regardless of the moveable object it is secured to. In the event that applying a Riemann Sum integration and the associated calculations are construed as additional elements, these amount to conventional calculations for processing sensor data and are not, in any way, a novel improvement to computer functionality. Thus, the components, identified above, are merely an attempt to link the abstract idea to a particular technological environment, but do not result in an improvement to the technology or computer functions employed. Additionally, the claims do not apply or use a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition. For instance, the preambles of the independent claims recite that the claimed invention is for “assessing a strength resistance workout”, but the claims and disclosure are silent regarding any particular treatment or prophylaxis for any disease or medical condition. Accordingly, based on all of the considered factors, these additional elements do not integrate the abstract idea into a practical application. Therefore, the claims are directed to the judicial exception. (STEP 2A, PRONG 2: YES).
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception under the considerations set forth in MPEP 2106.05. As identified in Step 2A, Prong 2, above, the claimed process does not require the use of a particular machine, nor does it result in the transformation of an article. The claims do not involve an improvement in a computer or other technology. Although the claims recite components (identified in Step 2A, Prong 2, above) for performing at least some of the recited functions, these elements are recited at a high level of generality and are not tied to performing any of the steps of the claimed method. This is at least evidenced by the manner in which this is disclosed that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 USC 112(a) as identified in Step 2A, Prong 2, above. Furthermore, this also evidences that the components are merely an attempt to link the abstract idea to a particular technological environment, but do not result in an improvement to the technology or computer functions employed, which the courts have held does not amount to significantly more. The lack of improvement to the computer or other technology is evidenced by the lack of incorporation of specific rules which enable the automation of a computer-implemented task that previously could only be performed subjectively by humans. In contrast, the focus of the claimed invention is on the analysis of the collected data, which is itself at best merely an improvement within the abstract idea. See pg. 2-3 in SAP America Inc. v. lnvestpic, LLC (890 F.3d 1016, 126 USPQ2d 1638 (Fed. Cir. 2018) which proffered “[w]e may assume that the techniques claimed are groundbreaking, innovative, or even brilliant, but that is not enough for eligibility. Nor is it enough for subject-matter eligibility that claimed techniques be novel and nonobvious in light of prior art, passing muster under 35 U.S.C. §§ 102 and 103. The claims here are ineligible because their innovation is an innovation in ineligible subject matter. Their subject is nothing but a series of mathematical calculations based on selected information and the presentation of the results of those calculations.” Viewed as a whole, these additional claim elements do not provide meaningful limitation to transform the abstract idea into a patent eligible application of the abstract idea such that the claim amounts to significantly more than the abstract idea of itself (STEP 2B: NO).
Therefore, the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter.
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.
(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 1-12 and 15-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Walke et al. (US 9,125,620 B2, hereinafter referred to as Walke).
Regarding claim 1, Walke teaches a system for assessing a strength resistance workout (Walke, Title, Method and Device for Mobile Training Data Acquisition and Analysis of Strength Training), the system comprising:
a sensor device (Walke, Col. 16, lines 26-28, “The mobile device 1 contains an accelerometer 6a, a rate sensor (gyroscope) 6b and a magnetometer 6c”) configured to:
measure at least one workout signal relating to measurement data associated with a movement of a weight (Walke, Col. 5, lines 4-7, “determining raw sensor values using said mobile device in said set movement patterns of said strength-training exercise X using said training utensil Y”);
associate time data with the at least one workout signal (Walke, Col. 7, lines 11-13, “precise displacement/time profile of the force contact point of the training load along the X-axis and/or Y-axis and/or Z-axis”);
a user device (Walke, Fig. 1, mobile device 1) configured to:
receive workout data from the sensor device, the workout data comprising the at least one workout signal and the time data (Walke, at least Col. 7, lines 8-41 provide examples of this.);
receive user data provided from the input unit (Walke, Col. 12, “the user enters personal user data”);
a server (Walke, Fig. 1, training data server 24) configured to:
obtain the workout data and the user data from the user device (Walke, Col. 12, lines 37-40, “continuous analysis of said multiple training data and/or of said reworked measurement values and/or of said raw sensor values and/or of personal user data and/or of the further training data on said training data server”);
analyze the workout data and the user data to assess a user workout according to predetermined workout parameters (Walke, Col. 12, lines 37-40, “continuous analysis of said multiple training data and/or of said reworked measurement values and/or of said raw sensor values and/or of personal user data and/or of the further training data on said training data server; said continuous analyses being based on a training model”);
generate a feedback notification (Walke, Col. 17, lines 44-49, “On the basis of the first submode! 26, several measurement variables for describing the performance 59 of the user 2 in strength training can be used as output 59 of the training model. On the basis of the second submode! 27, the training model 25 is configured to control 58 the strength training of the user 2, i.e. to generate training recommendations.”); and,
provide the feedback notification to the user device to facilitate presenting the feedback notification to a user (Walke, Fig. 1, display unit 4, graphical user interface 7; Col. 10, lines 57-62, “The method contains optical and/or acoustic and/or haptic signals being provided to said user by means of said mobile device for the purposes of support when carrying out the predetermined movement pattern, wherein said signals contain information about e.g. the rhythm and/or the amplitude and/or the direction of the predetermined movement pattern.” Col. 15, lines 41-42, “receives these on his computer as action recommendation.”).
Regarding claim 11, Walke teaches a method for assessing in real-time a user workout, the method comprising using at least one processor for:
obtaining workout data and user data from a user device (Walke, Col. 12, lines 37-40, “continuous analysis of said multiple training data and/or of said reworked measurement values and/or of said raw sensor values and/or of personal user data and/or of the further training data on said training data server”);
analyzing the workout data and the user data to assess a user workout according to predetermined workout parameters (Walke, Col. 12, lines 37-40, “continuous analysis of said multiple training data and/or of said reworked measurement values and/or of said raw sensor values and/or of personal user data and/or of the further training data on said training data server; said continuous analyses being based on a training model”);
generating a feedback notification (Walke, Col. 17, lines 44-49, “On the basis of the first submode! 26, several measurement variables for describing the performance 59 of the user 2 in strength training can be used as output 59 of the training model. On the basis of the second submode! 27, the training model 25 is configured to control 58 the strength training of the user 2, i.e. to generate training recommendations.”); and,
providing the feedback notification to the user device to facilitate presenting the feedback notification to a user (Walke, Fig. 1, display unit 4, graphical user interface 7; Col. 10, lines 57-62, “The method contains optical and/or acoustic and/or haptic signals being provided to said user by means of said mobile device for the purposes of support when carrying out the predetermined movement pattern, wherein said signals contain information about e.g. the rhythm and/or the amplitude and/or the direction of the predetermined movement pattern.” Col. 15, lines 41-42, “receives these on his computer as action recommendation.”).
Regarding claims 2 and 17, Walke teaches the system according to claim 1 and the method according to claim 16, further comprising:
obtaining historical workout data (Walke, Col. 12, lines 11-16, “training experience in a specific time unit, performance values,… habitual bodily exercise,… previously used training methods and training contents, date of the last carried out training unit”);
analyzing the historical workout data to determine trends (Walke, Col. 12, lines 37-40, “continuous analysis of said multiple training data and/or of said reworked measurement values and/or of said raw sensor values and/or of personal user data and/or of the further training data on said training data server; said continuous analyses being based on a training model”; Col. 12, lines 47-49, “training model predicting the performance of said user in strength training on the basis of said first submodel”);
recognizing patterns in the historical workout data (Walke, Col. 14, lines 48-56, “Artificial neural networks or the optimized model trees, which have previously not been used in strength training, are integrated in the first submodel and are combined with the second submodel in the training model. On the basis of the first submodel, the training model can image the interaction between training load (input of the training model) and performance (output of the training model) in the strength training process with several attributes, i.e. for example several load parameters (scope of training, training duration, training intensity, training frequency, ratio between activity and training duration) and, as a result, it is possible to analyze training effects (modified performance values) and, as a result, predict these.”);
generating a personalized recommendation message based on the patterns recognized in the historical workout data (Walke, line 65 of Col. 14 – line 2 of Col. 15, “Integrated into the second submodel is a knowledge-based fuzzy model and state transition modeling in the form of a finite automaton for algorithmic control of the strength training, resulting in specific training recommendations for the user.”); and,
providing the personalized recommendation message to the user device (Walke, Fig. 1, display unit 4, graphical user interface 7; Col. 10, lines 57-62, “The method contains optical and/or acoustic and/or haptic signals being provided to said user by means of said mobile device for the purposes of support when carrying out the predetermined movement pattern, wherein said signals contain information about e.g. the rhythm and/or the amplitude and/or the direction of the predetermined movement pattern.” Col. 15, lines 41-42, “receives these on his computer as action recommendation.”).
Regarding claim 3, Walke teaches the system according to claim 2, wherein the personalized recommendation message describes a personalized workout recommendation (Walke, line 65 of Col. 14 – line 2 of Col. 15, “Integrated into the second submodel is a knowledge-based fuzzy model and state transition modeling in the form of a finite automaton for algorithmic control of the strength training, resulting in specific training recommendations for the user.”).
Regarding claim 4, Walke teaches the system according to claim 1, wherein the sensor device comprises:
a connection element for attaching the housing to a weight machine (Walke, Col. 17, lines 15-16, “the sensor module can be attached directly on the training utensil (not depicted).”).
Regarding claims 5 and 18, Walke teaches the system according to claim 1 and the method according to claim 16, further comprising:
generating an initial benchmark workout (Walke, Col. 15, lines 32-36, “strength-training exercises, the selection, sequence and form of organization of which are made depending on the multiple training data and/or the further training data and/or the reworked measurement values and/or the raw sensor values and/or the personal user data.”);
providing the initial benchmark workout to the user device to be presented to the user (Walke, Col. 15, lines 12-13, “a new training method”); and,
collecting the workout data associated with the initial benchmark workout (Walke, Col. 15, lines 12-17, “new training method, which, for example, was carried out after specific temporal training duration with a specific scope of training, a specific training intensity, ratio between activity and training duration and training frequency.”).
Regarding claims 6, 7, and 19, Walke teaches the system according to claim 1 and the method according to claim 16, further comprising:
generating a periodic benchmark workout (Walke, line 63 of Col. 12 – line 6 of Col. 13, “These measurement variables of the performance in strength training can relate to strength-training exercises and/or muscles and/or muscle groups and/or body segment movements. The training model can make the selection of a measurement variable of the performance in strength training dependent on the training method which is applied by the user and/or use the latter in combination. The training methods can be established on the basis of a subset of the multiple training data, for example on the basis of the number of movement repetitions and/or the time under tension and/or the training load.”);
providing the periodic benchmark workout to the user device to be presented to the user (Walke, Col. 15, lines 17-30, “Within the second submodel, process states in the strength training process are diagnosed for this; state changes are modeled (e.g. change of training methods or strength-training exercises) and a finite automaton (state transition model) controls this process. The control can occur on different training process planes (temporal depth of the control, e.g. movement repetition, training set, strength-training exercise, training unit, micro-cycle, mesocycle, macro-cycle etc.). Furthermore, information of said interaction between training load (input of the training model) and performance (output of the training model) in the strength training process, in the first submodel, can be combined with the control of the second submodel, in the training model.”);
collecting the workout data associated with the periodic benchmark workout (Walke, Col. 15, lines 17-30, “Within the second submodel, process states in the strength training process are diagnosed for this; state changes are modeled (e.g. change of training methods or strength-training exercises) and a finite automaton (state transition model) controls this process. The control can occur on different training process planes (temporal depth of the control, e.g. movement repetition, training set, strength-training exercise, training unit, micro-cycle, mesocycle, macro-cycle etc.). Furthermore, information of said interaction between training load (input of the training model) and performance (output of the training model) in the strength training process, in the first submodel, can be combined with the control of the second submodel, in the training model.”);
obtaining feedback from the user device, wherein the feedback is provided by the user to the user device (Walke, Col. 17, lines 36-39, “As input data 57, the training model 25 obtains the multiple training data and/or the reworked measurement values and/or the personal user data and/or the further training data and/or the raw sensor values.”);
analyzing the feedback (Walke, Col. 12, lines 37-40, “continuous analysis of said multiple training data and/or of said reworked measurement values and/or of said raw sensor values and/or of personal user data and/or of the further training data on said training data server”); and,
adjusting the personalized recommendation for the user (Walke, Col. 17, lines 44-49, “On the basis of the first submodel 26, several measurement variables for describing the performance 59 of the user 2 in strength training can be used as output 59 of the training model. On the basis of the second submodel 27, the training model 25 is configured to control 58 the strength training of the user 2, i.e. to generate training recommendations.”).
Regarding claim 8, Walke teaches the system according to claim 1, wherein the server analyzes the workout data in real-time and provides real-time feedback notifications to provide to the user device (Walke, Col. 10, lines 57-62, “The method contains optical and/or acoustic and/or haptic signals being provided to said user by means of said mobile device for the purposes of support when carrying out the predetermined movement pattern, wherein said signals contain information about e.g. the rhythm and/or the amplitude and/or the direction of the predetermined movement pattern.”).
Regarding claim 9, Walke teaches the system according to claim 1, wherein the weights for which measurement data is received are part of a weight stack of a weight machine or of a free weight (Walke, Col. 5, lines 60-62, “By way of example, the training utensils can include ‘dumbbells’, ‘barbells’, ‘cable machines’, ‘machines’ or the ‘own body weight’”).
Regarding claim 10, Walke teaches the system according to claim 1, wherein the sensor device comprises at least one sensor (Walke, Col. 16, lines 26-28, “The mobile device 1 contains an accelerometer 6a, a rate sensor (gyroscope) 6b and a magnetometer 6c”).
Regarding claim 11, Walke teaches the system according to claim 10, wherein the at least one sensor is a distance measuring sensor (Walke, Col. 18, lines 6-9, “The mobile device 1 calculates the path 45 covered and the displacement/time profile of the force contact point of the training load of the training utensil 11”. The mobile device calculating the path covered and the displacement amounts to being a distance measuring sensor.).
Regarding claim 12, Walke teaches the system according to claim 10, wherein the at least one sensor includes an accelerometer (Walke, Col. 16, lines 26-28, “The mobile device 1 contains an accelerometer 6a”).
Regarding claim 15, Walke teaches the system according to claim 1, wherein the personalized recommendation message is provided to the user device in real-time during the workout of the user (Walke, Col. 10, lines 57-62, “The method contains optical and/or acoustic and/or haptic signals being provided to said user by means of said mobile device for the purposes of support when carrying out the predetermined movement pattern, wherein said signals contain information about e.g. the rhythm and/or the amplitude and/or the direction of the predetermined movement pattern.”.
Regarding claim 20, Walke teaches the method according to claim 16, further comprising:
receiving workout data from a sensor device, the workout data comprising the at least one workout signal and the time data (Walke, Col. 12, lines 37-40, “continuous analysis of said multiple training data and/or of said reworked measurement values and/or of said raw sensor values and/or of personal user data and/or of the further training data on said training data server”); and
receiving the user data provided from an input unit of the user device (Walke, Col. 12, lines 37-40, “continuous analysis of said multiple training data and/or of said reworked measurement values and/or of said raw sensor values and/or of personal user data and/or of the further training data on said training data server”).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 13 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Walke et al. (US 9,125,620 B2, hereinafter referred to as Walke) as applied to claim 12, in view of Djuriatno et al.1 (hereinafter referred to as Djuriatno).
Regarding claim 13, Walke teaches the system according to claim 12, wherein the server is further configured to:
collect acceleration data from the accelerometer (Walke, Col. 6, lines 49-52, “transform the acceleration data from the local system (mobile device) into the global coordinate system. It is possible to determine accelerations in the global coordinate system.”);
determine whether a velocity should be zero (Walke, Col. 6, lines 52-54, “as a result of the conversion of the accelerations is it possible to calculate movement vectors and all variables resulting therefrom.”);
perform a noise correction to the velocity when noise is detected in the velocity (Walke, Col. 6, lines 40-43, “Combining an accelerometer and a rate sensor, and fusing the raw sensor values and the predetermined movement data, for example by means of a Kalman filter/direction cosine matrix”. One of ordinary skill in the art understands that the Kalman filter performs the noise correction.);
provide an output velocity (Walke, Col. 6, lines 52-54, “as a result of the conversion of the accelerations is it possible to calculate movement vectors and all variables resulting therefrom.” Col. 10, lines 57-67, “The method contains optical and/or acoustic and/or haptic signals being provided to said user by means of said mobile device for the purposes of support when carrying out the predetermined movement pattern, wherein said signals contain information about e.g. the rhythm and/or the amplitude and/or the direction of the predetermined movement pattern. By way of example, the user can more easily maintain the cadence (movement rhythm), which e.g. is predetermined by a training method and/or training plan, and/or the movement direction and/or the movement amplitude of a predetermined movement pattern of a strength-training exercise”);
read acceleration data received from the accelerometer (Walke, Col. 6, lines 49-52, “transform the acceleration data from the local system (mobile device) into the global coordinate system. It is possible to determine accelerations in the global coordinate system.”);
detect a rest phase according to the acceleration data (Walke, Col. 8, lines 13-15, “rest times between movement repetitions and/or training sets and/or strength-training exercises and/or training units;” Col. 11, lines 4-5, “acquire pause times between movement repetitions”);
calculate a gravity vector (Walke, Col. 6, lines 40-45, “Combining an accelerometer and a rate sensor, and fusing the raw sensor values and the predetermined movement data, for example by means of a Kalman filter/direction cosine matrix, allows the alignment of the mobile device in space (roll angle, pitch angle, yaw angle) to be calculated relative to the Earth.”); and,
apply the gravity vector to acceleration data (Walke, Col. 6, lines 45-52, “Calculating the alignment in the moved state by means of acceleration data only is not possible, since, in addition to the gravitational acceleration, further accelerations that cannot be separated occur. As a result of the known alignment, it is possible to transform the acceleration data from the local system (mobile device) into the global coordinate system. It is possible to determine accelerations in the global coordinate system.”).
Walke does not explicitly teach divide the acceleration data into a predetermined number of intervals; apply a Riemann Sum integration to the acceleration data.
However, in a related art, Djuriatno teaches divide the acceleration data into a predetermined number of intervals (Djuriatno, pg. 1900, “This method estimates the derivative at the interval points”); apply a Riemann Sum integration to the acceleration data (Djuriatno, pg. 1899, “Integrate angle velocity value towards time by Midpoint Riemann Sum method to obtain angle position value. Calculate the dynamic acceleration value of a vehicle parallel to the earth's surface by using angle position value and acceleration sensor value. Integrate dynamic acceleration value towards time by Midpoint Riemann Sum method to obtain velocity value.”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date for Walke to include preparing the acceleration data for Riemann Sum integration and applying the Riemann Sum integration because “Midpoint Riemann Sum is a single step method that is more accurate than the Euler method [19, 20]. This method estimates the derivative at the interval points, which are then summarized as shown in Figure 4. The Midpoint Riemann Sum method is the embryo of the integral. which integral can certainly be done by the approximation of the sum of the multiplicity of f(x) multiplied by Δt. The smaller the value Δt results in more accurate readings [21, 22].” Thus, it is merely the user of a known technique to improve similar methods in the same way.
Regarding claim 14, Walke in view of Djuriatno teaches the system according to claim 13, wherein the at least one sensor includes a gyroscope configured to record angular velocity (Walke, Col. 6, lines 52-54, “as a result of the conversion of the accelerations is it possible to calculate movement vectors and all variables resulting therefrom.” Col. 16, lines 26-28, “The mobile device 1 contains… a rate sensor (gyroscope) 6b” Col. 5, lines 39-40, “rate sensor for determining… angular speed values”);
wherein the server is further configured to:
receive the angular velocity from the at least one sensor (Walke, Col. 5, lines 39-41, “rate sensor for determining… angular speed values, which are transmitted to the processor.”);
define a horizontal plane according to the gravity vector (Walke, Col. 6, lines 45-54, “Calculating the alignment in the moved state by means of acceleration data only is not possible, since, in addition to the gravitational acceleration, further accelerations that cannot be separated occur. As a result of the known alignment, it is possible to transform the acceleration data from the local system (mobile device) into the global coordinate system. It is possible to determine accelerations in the global coordinate system. Only as a result of the conversion of the accelerations is it possible to calculate movement vectors and all variables resulting therefrom.”);
calculate a tilt angle relative to the horizontal plane (Walke, Col. 18, lines 64-65, “An optimum work angle emerges”);
designate a tilt classification of the tilt angle (Walke, Col. 10, lines 23-24, “This angle range around the maximum torque is also referred to as "optimum work angle”);
generate a feedback notice according to the tilt classification (Walke, Col. 19, lines 2-4, “The user can be supported in maintaining the optimum work angle by optical and/or acoustic and/or haptic signals of the mobile device 1.”);
provide the feedback notice to the user device (Walke, Col. 19, lines 2-4, “The user can be supported in maintaining the optimum work angle by optical and/or acoustic and/or haptic signals of the mobile device 1.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Brann (US 6,059,576) discloses monitoring and training an individual on proper motion during physical movement.
Walke et al. (US 9,750,454 B2) is closely related to the primary reference.
McMillan et al. (US 2016/0038788) discloses the use of Riemann sum is known in the art.
Huston et al. (US 2018/0081455) also discloses the use of Riemann sum is known in the art.
Ehlert Taylor (US 10,220,577 B1) also discloses the use of Riemann sum is known in the art.
Lafrance et al. (US 2020/0054914) explicitly discloses calculating tilt and generating a feedback notice based on the tilt.
Smith et al. (US 2023/0277892) explicitly discloses calculating tilt and generating a feedback notice based on the tilt.
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/DANIEL LANE/ Examiner, Art Unit 3715
1 Djuriatno, W., Maulana, E., Hasan, H., Arisandi, E. D., & Wijono, W. (2019). Velocity measurement based on inertial measuring unit. TELKOMNIKA (Telecommunication Computing Electronics and Control), 17(4), 1898–1906. https://doi.org/10.12928/telkomnika.v17i4.11826
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