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 .
Continued Examination
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10/17/2025 has been entered.
Status of the Claims
The status of the claims as of the response filed 10/17/2025 is as follows:
Claims 1-2 and 8-12 are pending.
Claims 3-7 are canceled.
Claims 10-12 are new.
Claims 1, 8 and 9 were amended.
All pending claims have been considered below.
Respond to Arguments:
35 USC 101 Rejection
Applicant’s arguments, filed 10/17/2025, with respect to amended claims 1, 8, and 9 and new claims 10-12 have been fully considered and are not persuasive. The rejection under 35 U.S.C. § 101 is maintained for claims 1, 2, and 8-12.
Applicant argues that amending claims 1, 8, and 9 to recite limitations for "correcting the cause of a problem by searching from an uppermost layer... to the lowermost layer..." (Spec. 27, 28, 31) transforms the claim into a "practical application in the field of exercise science."
Examiner respectfully disagrees because the amendments do not overcome the rejection. The new limitations in amended claims 1, 8, and 9, which detail the logic of the search (from uppermost to lowermost, finding the "most divergent index"), do not integrate the abstract idea into a practical application. Instead, these amendments merely recite the abstract idea... with greater specificity. This detailed hierarchical search is still a "Mental process"—it codifies the exact evaluation and judgment a human coach would perform. The claims do not recite an improvement to computer functionality itself or an improvement to any other technology; they merely use generic computers to "apply it" (MPEP § 2106.05(f)). Therefore, the claims remain directed to the abstract idea. “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements.” See also 2106.05(a)(II): “it is important to keep in mind that an improvement in the abstract idea itself… is not an improvement in technology.”
Applicant contends that the amended claims, by "identifying the cause of the problem within the index of the lowermost layer," impose a "meaningful limit on the judicial exception" and are thus eligible under Step 2A, citing the 2019 PEG (84 Fed. Reg. 4 at 54-55).
Examiner respectfully disagrees. The citation to the 2019 PEG is not persuasive as the amendments do not provide the type of "meaningful limit" that constitutes integration. The amendments, including new claims 10-12 which specify the indexes ("landing impact," "ground contact position," etc.), merely limit the abstract idea to a particular field-of-use (running) and add specificity to the abstract idea itself. This is not an "integration" (MPEP § 2106.05(d)). The claims still do not improve the functioning of the computer (MPEP § 2106.05(f)). The generic server, processor, and "trained model" (which is only defined by the abstract logic it performs) are used to automate the mental process. The claim as a whole does not amount to "significantly more" than the abstract idea it recites (Step 2B).
New dependent claims 10-12 have been added, which recite specific examples of the indexes in the different layers (e.g., "landing impact," "vertical motion," "ground contact position").
The addition of new dependent claims 10-12 does not overcome the § 101 rejection. These limitations only add further specificity to the abstract idea of the mental evaluation itself (MPEP § 2106.05(d)). Reciting the specific content or topics of the mental process (what the coach is thinking about) does not make the process itself patent-eligible. These claims still depend on the ineligible subject matter of the independent claims and add no inventive concept or integrating step.
For these reasons, claims 1, 2, and 8-12 remain rejected under 35 U.S.C. § 101.
Rejection Under 35 U.S.C. § 103
Applicant’s arguments, filed 10/17/2025, with respect to amended claims 1, 8, and 9 and new claims 10-12 have been fully considered and are not persuasive. The rejections under 35 U.S.C. § 103 are maintained.
Applicant argues that amending independent claims 1, 8, and 9 to recite a specific inter-layer relationship—searching from "major indexes used to evaluate running exercise motions" (uppermost layer) to "intermediate indexes corresponding to problems" (intermediate layer) to "causes of the problems" (lowermost layer)—patentably distinguishes over the combination of Katis and Mettler. Applicant contends that Katis only shows a generic "movement category" and that Mettler only teaches finding a "most significant impact," failing to cure Katis's allegedly deficient disclosure of the claimed hierarchy.
Examiner respectfully disagrees because, while the Examiner acknowledges that Katis and Mettler alone do not explicitly disclose the "intermediate layer" decision-tree structure, the New Rejection incorporates Verstegen to cure this deficiency. Verstegen explicitly teaches automating a trainer's thought process using an "expert system" in the form of a "decision tree" based on a series of questions (Verstegen, para. [0024]), which corresponds to the claimed multi-level nested search from major goals down to specific causes. It would be obvious to a person of ordinary skill in the art to organize Katis’s movement ranks and Mettler’s prioritized features into Verstegen’s decision-tree structure to create the claimed hierarchy, applying a known technique (decision-tree expert systems) to improve similar devices (automated coaching) in the same way (providing granular, drill-down guidance. Therefore, the claimed invention yields predictable results from combining familiar elements (Katis's metrics, Mettler's prioritization, Verstegen's tree structure).
Applicant argues new claims 10-12 are allowable by virtue of their dependence on the (argued to be allowable) independent claims and because they add new, specific features supported by the specification, namely the list of indexes such as "landing impact," "vertical motion," "braking force," "ground contact position," "trunk angle," etc.
Examiner respectfully disagree. Applicant's argument is not persuasive. The specific features added by claims 10-12 (the explicit list of biomechanical indexes) do not confer patentability because they are taught by the prior art of record, as detailed in the rejection of claims 10-12 for 35 U.S.C 103 below.
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, 2, and 8–12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Step 1 determines whether the claims fall within one of the four statutory categories under 35 U.S.C. § 101: process, machine, manufacture, or composition of matter.
The claims encompass three statutory categories: process, machine, and manufacture.
Machine (Claim 1-2, 10): The language "a running improvement guidance device, comprising: a server comprising: a memory... and a processor..." recites a machine because it describes a concrete thing consisting of parts, such as memory and processor devices combined to perform functions.
Process (Claim 8, 11): The language "a running improvement guidance method, comprising: storing on a server a trained model... providing the measurement data... searching within each layer..." recites a process because it describes a series of acts, such as storing, providing, searching, and providing guidance.
Manufacture (Claim 9, 12): The language "a non-transitory computer-readable medium storing a running improvement guidance program causing a computer to implement..." recites a manufacture because it describes a tangible article, the medium, given a new form or property through man-made means to store and execute instructions.
Having confirmed the claims are directed to statutory subject matter, the analysis proceeds to Step 2A.
Step 2A Prong One:
Prong One evaluates whether the claim recites a judicial exception, such as an abstract idea, by identifying limitations that fall within enumerated groupings like mental processes, mathematical concepts, or certain methods of organizing human activity.
Recitation Independent Claim 1:
A running improvement guidance device, comprising:
a server comprising:
a memory that stores a trained model configured to receive over a network from a measurement device input of measurement data of a user during running and to output over the network to a display device an index among hierarchized indexes for improvement of at least one of a form of a running motion of the user and a force of the running motion of the user;
and a processor that provides the measurement data of the user during running as the input to the trained model and outputs to the display device exercise guidance to the user for correcting a cause of a problem of the at least one of the forms of the running motion of the user and the force of the running motion of the user based on the index output by the trained model,
wherein the trained model is configured to:
search the hierarchical indexes from an uppermost layer of the hierarchical indexes to a lowermost layer of the hierarchical indexes via an intermediate layer of the hierarchical indexes, the uppermost layer of the hierarchical indexes including major indexes used to evaluate running exercise motions, the intermediate layer of the hierarchical indexes including intermediate indexes corresponding to problems with the at least one of the form of the running motion and the force of the running motion within the major indexes, the lowermost layer of the hierarchical indexes including the index output by the trained model corresponding to causes of the problems with the form of the at least one of the running motion and the force of the running motion within the intermediate indexes,
search within each layer of the hierarchical indexes for a most divergent index among a plurality of indexes in the layer that most diverges from reference measurement data representing a normal value of users during running,
and sequentially search between each layer of the hierarchical indexes from the most divergent index in each layer of the hierarchical indexes towards the lowermost layer of the hierarchical indexes.
Note: Bold element, are additional elements further evaluated under prong two and step 2B, non-bold are the identify abstract idea.
Claims Categorized Rational:
The claims recite limitations that fall within the "Mental processes" grouping of abstract ideas. The core concept of the claims is a method of evaluation that can be practically performed in the human mind. MPEP § 2106.04(a)(2) defines the "Mental processes" grouping as "concepts performed in the human mind (including an observation, evaluation, judgment, opinion)." Under the broadest reasonable interpretation (MPEP § 2111), the claimed method, stripped of its computer implementation, describes a process a human running coach could perform: observe a runner (collect measurement data), mentally organize potential flaws into a hierarchy (e.g., overall balance -> posture -> foot strike), compare the runner's form to an ideal (reference data), identify the most significant flaw ("most divergent index"), and provide corrective advice ("guidance information"). The specific logic recited—searching a hierarchy from top to bottom, finding the most divergent index in a layer, and then proceeding to a lower layer—is simply a codification of this human mental process.
These steps of collecting information, analyzing it against a baseline, and identifying a problem based on the analysis are "concepts performed in the human mind." The specification itself confirms this abstract nature, stating the goal is to "identify a problem that leads to effective improvement of exercise motions" (para. [0005]) by "evaluating the hierarchized indexes based on measurement data" (para. [0008]). This is a classic example of a mental process of categorized evaluation. While the claims also touch upon "Certain methods of organizing human activity," specifically the sub-category of "managing personal behavior" by providing guidance to improve a running form.
The claim’s limitation of identifying a root cause through hierarchical indexes in running merely describes a mental process that can be performed by a human with pen and paper and does not overcome prong one because are tools of the human mind as observation, evaluation, judgment, opinion.
Dependent claim 2 does not add limitations that move the claim outside of the abstract idea category. It recites generating the exercise guidance based on the index, which is the logical conclusion of the mental evaluation. Generating guidance is what a human coach would do after identifying the problem and does not add a patent-eligible concept beyond the underlying abstract evaluation.
Claim 10-12 just describe what are the differences hierarchy indexes, that it is for example analogous to a coach include that recited specify indexes in their mental process of identifying a problem and correcting the cause. Therefore, claims 10-12 do not overcome prong one because they still recite an abstract idea—a categorized evaluation.
The claims recite a judicial exception because they set forth the abstract idea of performing a mental process of categorized evaluation. The analysis therefore proceeds to Prong Two.
Step 2A, Prong Two: Does the Claim Integrate the Exception into a Practical Application?
The claims as a whole do not integrate the judicial exception into a practical application but are instead directed to the abstract idea itself.
The additional elements recited in the claims do not integrate the abstract idea into a practical application; they merely provide a generic technological environment for it. The claims recite performing the abstract evaluation using generic computer components (server, processor, memory, Measurement Device) and a generic network to receive data and transmit results. This is an example of merely "linking the use of a judicial exception to a particular technological environment," which is not a practical application (MPEP § 2106.05(h)).
The claims do not specify any improvement in the functioning of the computer, network, or sensor technology itself. Instead, they use these components as mere tools to automate the abstract process of providing exercise advice (MPEP 2106.05(f)). The specification confirms the generic nature of these components, listing a "wearable device, such as a smartwatch, or a smartphone" (para. [0015]) as the measurement device and a "smartphone, tablet, or personal computer" (para. [0021-0022]) as the display device. These limitations simply provide a generic technological environment for the abstract idea and do not add an inventive concept.
The trained model itself, as claimed, does not confer eligibility at this step. It is defined functionally by the abstract steps it performs (searching, comparing, identifying), not by a specific technical structure or process that improves how the computer operates. The applicant's specification discusses that a "learning model... may be generated by machine learning" (para. [0023]), but the claims do not recite a specific AI architecture or training process that overcomes a technical problem in computing. The claims simply take the abstract idea of a coach's evaluation process and state to "apply it" using a trained model on a computer, which is insufficient to establish a practical application (MPEP § 2106.05(f)).
The dependent claims 2 limitation of "generates the exercise guidance" adds no further integration. It is simply another data processing step that is part of the abstract idea itself and does not transform the claim into a practical application of the exception. The generation of advice is the inherent goal of the abstract evaluation, not a separate, integrating application. Viewed in combination, these additional elements do not integrate the abstract idea; they collectively describe a generic system for automating a mental process, which does not impose a "meaningful limit on the judicial exception" (MPEP § 2106.04(d)).
The dependent claims 10-12 limitation does not recite new additional element just further describe the abstract idea of independent claims as describe above in prong one.
The claims as a whole do not integrate the judicial exception into a practical application. Therefore, the analysis proceeds to Step 2B.
Step 2B: Is There an Inventive Concept?
The claims, evaluated individually and as a whole, fail to provide an inventive concept because the additional elements do not amount to “significantly more” than the judicial exception. In Step 2B, the analysis determines whether a claim directed to a judicial exception recites additional elements that "transform the nature of the claim" into a patent-eligible application. An inventive concept "cannot be furnished by the unpatentable... abstract idea... itself" (MPEP § 2106.05). This rationale will demonstrate that the limitations here do not supply that inventive concept.
Independent Claims Analysis
Data Processing System (Server, Memory, Processor) (Claims 1, 8, 9) The claims recite a server, memory, and processor that do not provide an inventive concept. The specification describes these in generic terms, stating the "exercise improvement guidance device 100 is configured on a server capable of communicating with the measurement device 20 and the display device 30" (para. [0022]) and that the functions "may be implemented as application software running on" an information processing device like a "smartphone, tablet, or personal computer" (para. [0022]). These descriptions portray the components as generic hardware performing their most basic functions of storing data and executing instructions. MPEP § 2106.05(f) clarifies that claim amounting to "mere instructions to implement an abstract idea on a computer" lack an inventive concept. The specification provides no details on any specialized configuration or unconventional operation of these components that would constitute an improvement to computer technology itself. Therefore, the data processing system does not provide "significantly more" than the abstract idea. It is an example of an attempt to "monopolize the [judicial exception]" by "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use" (MPEP § 2106.05(h), quoting Diamond v. Diehr).
Network and Peripheral Devices (Measurement Device, Display Device) (Claims 1, 8, 9) The claims recite a network and peripheral devices that do not provide an inventive concept. The claims recite receiving measurement data "over a network from a measurement device" and outputting guidance "over the network to a display device". These are examples of "insignificant extra-solution activity" (MPEP § 2106.05(g)). Specifically, receiving data from a sensor is mere data gathering, a pre-solution activity. Outputting guidance to a display is a post-solution activity. The specification confirms the generic nature of these components, listing a "wearable device, such as a smartwatch, or a smartphone" (para. [0015]) as the measurement device and a "smartphone, tablet, or personal computer" (para. [0021-0022]) as the display device. These limitations simply provide a generic technological environment for the abstract idea and do not add an inventive concept.
Trained Model (Claims 1, 8, 9) The claims recite a "trained model" that does not provide an inventive concept. The specification suggests that "a learning model... may be generated by machine learning" (para. [0023]) and that the hierarchical structure can be automatically generated by "autonomously analyzing a term used in each index and its biomechanical meaning" (para. [0023]). However, the claims themselves do not recite any specific, unconventional AI technique, training method, algorithm, or data structure that improves AI technology itself. The model is defined purely by its function—automating the abstract steps of hierarchical comparison. This is an attempt to claim the idea of a solution, rather than a particular technical means for achieving it. The trained model limitation does not amount to significantly more than the abstract idea it implements.
Claim "As a Whole" Analysis Viewed in combination, the additional elements fail to supply an inventive concept. The claim, taken as a whole, simply automates the abstract idea of providing personalized exercise coaching using a collection of generic computer components for their intended and conventional purposes. The combination of these elements does not result in an improvement to the functioning of the computer itself or to any other technology, nor does it effect a transformation of an article into a different state or thing. The elements together provide a mere technological setting for the abstract idea, which does not amount to "significantly more."
Dependent Claims Step 2B Evaluation
Claim 2 does not add an inventive concept. Claim 2 adds the limitation that the processor "generates the exercise guidance based on the index". The specification describes this step in general terms: "The guidance information thus generated is transmitted to the display device 30 and displayed on the display screen thereof" (para. [0031]). This limitation is an inherent part of the abstract idea itself—the goal of evaluating performance is to generate guidance. It is an insignificant post-solution activity that does not add an inventive concept to the combination. The dependent claims 10-12 limitation does not recite new additional element just further describe the abstract idea of independent claims as describe above in prong one.
The limitations added by the independent and dependent claims, both individually and in combination, fail to provide an inventive concept. They merely narrow the field of use to exercise guidance and add insignificant, pre- and post-solution activities (data gathering, displaying results) to the core abstract idea of categorized evaluation. As such, the claims as a whole do not amount to significantly more than the judicial exception and are therefore ineligible under 35 U.S.C. § 101.
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.
Claims 1-2, 8-9, are rejected under 35 U.S.C. § 103 as being unpatentable over Katis US 2018/0104541 in combination with Mettler (US 2017/0061817) and in view of Verstegen (US 2009/0269728).
Claim 1:
Katis teaches A running improvement guidance device, comprising:
a server comprising: (Katis, Abstract, para. 0018, 0021-0022, 0030).
Katis describes an automated system that provides suggestions for improving the athlete's performance for movements including distance running, which is semantically and functionally equivalent to a running improvement guidance device. This system utilizes cloud resources 150 to house its main components like the ANN 120 and coaching engine 140, which is functionally a server that provides services to a user's mobile device 160.
a memory that stores a trained model configured to receive over a network from a measurement device input of measurement data of a user during running and to output over the network to a display device an index among hierarchized indexes for improvement of at least one of a form of a running motion of the user and a force of the running motion of the user; (Katis, 0003, 0018, 0019, 0020, 0022-0023, 0027-0031, 0036).
Katis describes a coaching system that functions as a running improvement guidance device, where cloud resources 150 including a computer server act as the server. This server contains a reference database 122 (a memory) that holds data used to train an artificial neural network (ANN) 120 (a trained model). The system is configured to receive over a network (WiFi, Bluetooth) data from a scanner 130 (a measurement device). This data is input of measurement data (position data) captured from a user during a movement (which under BRI includes running supported by paragraph 0030). The system is further configured to output over the network to a display device (mobile device 160) information such as rank data or a score ("91.50/100"), which constitutes an index among hierarchized indexes. This output is provided for improvement of the user's technique, as Katis explicitly mentions providing correction to form of a movement, and for improvement of the user's power, as the system analyzes acceleration, which is directly related to force.
and a processor that provides the measurement data of the user during running as the input to the trained model and outputs to the display device exercise guidance to the user for correcting a cause of a problem of the at least one of the form of the running motion of the user and the force of the running motion of the user based on the index output by the trained model, (Katis, 0004, 0020-0022, 0028-0031).
Katis discloses a system where its processor (the ANN 120 and coaching engine 140) provides the measurement data (position data 132) as the input to the trained model (ANN 120). The processor then outputs to the display device (mobile device 160) exercise guidance (coaching information) for improving the form of the running motion (correction to form) and force (derived from acceleration data), and this guidance is generated based on the index output by the trained model (rank data). Katis discloses that the guidance is for correcting a problem with the user's form. The coaching information explicitly includes instructions to correct a deficiency in the performance and suggestions for modifying the user's form. This is functionally identical to guidance for "correcting a cause of a problem... of the form of the running motion."
wherein the trained model is configured to: (Katis, paragraph 0020-0022)
by the trained model corresponding to causes of the problems with the form of the at least one of the running motion and the force of the running motion within the intermediate indexes, (Katis, paragraphs 0021, 0022, 0027, 0029, 0033).
Katis describes a system where an ANN 120 (trained model) processes user data. Katis’s system of a movement category with associated grades and flaws constitutes a set of hierarchical indexes, and the ANN's processing of data to identify a specific flaw for correction represents a systematic examination. This process flows from a general movement category to a specific flaw, which is a functional search from an uppermost layer... to a lowermost layer. The final output is specific coaching instructions based on an identified flaw, which represents an index output from the lowermost layer.
Katis discloses an intermediate layer corresponding to problems. Katis's reference data set 300 contains rank data that includes identification of one or more flaws in the movement, such as a deficiency in technique. This "flaw" is a "problem with the form" and functions as an "intermediate index" within the "major index" (the movement category).
The ANN 120 outputs rank data (the "index output by the trained model"). This rank data (index) is used by the coaching engine to look up coaching instructions (para. 0029). These instructions, such as C1... suggestions for modifying the user's form, are the "causes" or, more accurately, the corrections for the "problems" (the "flaws" from the intermediate layer). The rank value itself is the "index" in the "lowermost layer" that correlates to and corresponds to the "cause" (the corrective instruction).
search within each layer of the hierarchical indexes for a most divergent index among a plurality of indexes in the layer that most diverges from reference measurement data representing a normal value of users during running, (Katis, paragraphs 0003, 0005, 0021-0022, 0026-0028, 0034).
Katis uses an “artificial neural network (ANN) trained via a reference data set”, which meets the interpretation of a trained model. This system organizes movements into a general “movement category” and then identifies a specific “class or subset” or “flaw”, which functionally creates a set of hierarchical indexes. The process starts by applying the user's overall “position data” to the ANN trained for that “movement category” and drills down to identify a specific “flaw”, which constitutes a search from an uppermost to a lowermost layer. Katis compares the user's performance to a “reference data set” to find “similarities” and identify a “distinction” from a higher-ranked movement.
and sequentially search between each layer of the hierarchical indexes from in each layer of the hierarchical indexes towards the lowermost layer of the hierarchical indexes. (Katis, paragraphs 0003, 0005, 0022, 0027, 0034).
Katis employs a trained model (an artificial neural network) to first determine a high-level assessment, the “rank value” for the performance. The system then uses this high-level assessment as a basis for a sequential search between each layer by drilling down into a lower layer, as it is “based on the rank value” that the system determines specific “coaching information”. This coaching can include notifying the user of a specific “distinction” (a lower-level problem) and providing “instructions for traversing the distinction”.
Katis does not teach a diagnostic search for the greatest point of error.
The analytical process in Katis is holistic, focused on determining overall similarities between the performed movement and the movements represented in the reference data to output a general rank (Katis, para. 0021). It does not teach a specific, the single most divergent flaw within a layer and then proceeds downward from that specific point of error.
Mettler discloses a system that first decomposes a movement into a hierarchy of movement units and movement phases (Mettler, para. 0018), which is functionally equivalent to the claimed hierarchical indexes. The core of Mettler's analysis is to then identify and focus on the most critical error. Mettler's feedback synthesis process explicitly includes steps to 1) Determine the movement features... that the user has the ability to change... 2) Determine movement features that display a significant impact on an outcome... [and] 3) Prioritize movement features that are both changeable and have the most significant impact on outcomes (Mettler, para. 0289-0290). This act of prioritizing the feature with the greatest impact on outcome is functionally the same as searching for the most divergent index, and the process of analyzing decomposed phases and synergies represents a sequential search from the most divergent index... towards the lowermost layer.
It would have been obvious to a person of ordinary art skill to combine Katis's teachings with Mettler because both references are in the same field of art and are directed toward solving the same problem: providing effective, automated feedback to improve a user's movement skills.
Katis aims to determine coaching information for the user (Katis, para. 0022), but its feedback is based on a general "rank." Mettler explicitly seeks to improve upon such general feedback by providing a method to identify specifically where change is required in order to provide actionable information that can be leveraged directly for training (Mettler, paras. 0012, 0065).
Mettler explicitly teaches that its method of prioritizing feedback allows a user to close the training or rehabilitation loop and run it as an iterative scheme (Mettler, para. 0065). A POSITA would recognize this as a significant improvement over the more general feedback provided by Katis. Integrating Mettler's analytical software logic into Katis's established client-server hardware architecture would be a predictable combination of known elements, providing a reasonable expectation that the resulting system would successfully deliver more targeted and effective coaching.
Katis teaches a server comprising a trained model that evaluates running motions, disclosing that it “outputs rank data”, 0021 which indicates a relationship between the performance and recorded movements (Katis, para. [0003]). Katis teaches that this output is used to determine instructions, identifying “one or more flaws in the movement, such as a deficiency in technique” (Katis, para. [0027]), which corresponds to providing an index for improvement.
However, Katis utilizes an Artificial Neural Network (ANN) to process position data directly into rank data (Katis, para. [0003]). Katis fails to explicitly disclose searching the hierarchical indexes from an uppermost layer... via an intermediate layer... to a lowermost layer; instead, it relies on a processing technique that functions as a "black box" without the explicit, sequential decision-tree structure recited in the claims.
Mettler teaches the specific hierarchical indexes structure required by the claim, explicitly decomposing movement into “three primary levels of movement organization” (Mettler, para. [0112]): (i) “Movement Repertoire” and “movement profiles” (Uppermost/Major, paras. [0170], [0197]); (ii) “Movement Phases” which identify internal structure and technique flaws (Intermediate/Problems, para. [0114]); and (iii) “Movement Synergies” which correspond to neuromuscular implementation (Lowermost/Causes, para. [0115]). Verstegen teaches the search mechanism to navigate such a structure. Once the motion problem is identified via the screen, the system analyzes why the problem exists by mimicking the cognitive analysis of a human expert. The system uses “business logic integrating the various data points collected” (Verstegen, para. [0053]) to analyze the results of the movement screen against the athlete's history. To break down the problem (e.g., determining if a squat failure is due to ankle mobility vs. hip strength), the system employs an “expert System” that “could be Something as simple as a decision tree that is based on an expert trainer's responses to a series of questions” (Verstegen, para. [0024]). The breakdown includes analyzing “current state of an injury” and “injury history” (Verstegen, para. [0024]) to ensure the solution is safe.
While Katis provides the automated coaching platform, the combination with Mettler and Verstegen makes obvious the specific method of diagnosing the error via hierarchical search. A PHOSITA would interpret the claimed “hierarchical indexes” as reading on Mettler’s “hierarchical organization” (para. [0163, 0170]) and would recognize that Katis’s system is improved by integrating Verstegen’s logic to navigate it. By applying Verstegen’s “decision tree” logic (para. [0024]), the system searches through Mettler’s layers: starting at the “Movement Repertoire” (Uppermost), analyzing deviations in the “Movement Phases” (Intermediate), and determining the root cause in the “Movement Synergies” (Lowermost). This integration replaces the opaque nature of Katis’s ANN with the transparent, logical breakdown of Verstegen, allowing the system to “identify specifically where change is required” (Mettler, para. [0065]) using “business logic” (Verstegen, para. [0053]).
It would have been obvious to combine Katis with Mettler and Verstegen because all three references reside in the same field of automated athletic training and seek to solve the shared problem of providing accurate, actionable feedback to a user. Katis aims to provide instructions for correction to form (para. [0022]). Mettler provides the motivation to use a hierarchical structure because “complex movements are obtained by combination of motion segments or phases” (para. [0082]), making decomposition necessary for precise analysis. Verstegen provides the motivation to use decision-tree search logic to “mimic the thought processes employed by a professional trainer” (para. [0011, 0024, 0054]), ensuring the automated advice is as logical and reliable as a human expert's.
A person of ordinary skill in the art would have been motivated to integrate Mettler’s hierarchical data structure and Verstegen’s decision-tree search into Katis’s system to achieve the benefit of more granular and understandable diagnostics. Mettler explicitly teaches that this hierarchical decomposition “provides a sparse description of the high-dimensional movement data” and “plays a central role in functional analysis” (para. [0020]). Furthermore, Verstegen teaches that using such logic allows the system to “mimic the thought processes employed by a professional trainer” (para. [0024]). There is a reasonable expectation of success because hierarchical data structures (Mettler) and decision tree logic (Verstegen) are well-known, compatible computational techniques for diagnostic systems.
Claim 2:
Katis in combination with Mettler and Verstegen teaches, The running improvement guidance device according to claim 1, wherein the processor generates the exercise guidance based on the index output by the trained model. (Katis, paragraphs 0003, 0021-0022, 0028, claim 1).
Katis discloses a “coaching engine”, which is a processor, that is responsible for “determining instructions for a second movement”, which is functionally how the processor generates the exercise guidance. This generation of instructions is explicitly performed “based on the rank value”, which is the index output by the trained model (“the ANN”). The “rank value” is the metric determined by the ANN that “indicates a relationship between the performance of the first movement and a subset of the plurality of recorded movements”.
Note: Claims 8-9 are rejected with the same analysis above for being very similar to Claim 1-2.
Claims 10-12 are rejected under 35 U.S.C. § 103 as being unpatentable over Katis (US 2018/0104541) in view of Mettler (US 2017/0061817), and in view of Verstegen (US 2009/0269728), and further in view of Souza (Reference U), and Teng (Reference V). , refer to PTO-892 for NPLs.
Claim 10.
Katis in combination with Mettler and Verstegen teaches, The running improvement guidance device according to claim 1, wherein the major indexes include
Katis in view of Verstegen teaches the system of claim 1, including a processor and trained model that performs a hierarchical search by analyzing a particular movement category (e.g., a box jump) as an uppermost layer, identifying one or more flaws in the movement, such as a deficiency in technique as an intermediate layer, and mapping those flaws via rank data to coaching instructions as a lowermost layer as described in claim 1 mapping above.
However, Katis fails to disclose the specific hierarchical indexes for running, namely wherein the major indexes include landing impact, vertical motion, and braking force, and the lowermost indexes include ground contact position and ground contact angle.
Souza (Reference U) teaches the missing indexes in bold, describing a systematic video-based running biomechanics analysis that measures Foot strike patterns (mapping to ground contact position), Foot inclination angle (mapping to ground contact angle), and identifies higher peak vertical ground reaction force (mapping to landing impact/vertical motion) and braking impulse (mapping to braking force) as key factors in running injury (Souza, Abstract and Figures 2-3).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to combine the teachings of Katis with Souza because both references share the purpose of tracking and evaluating athletic performance to prevent injury (Katis, par. 0034, 0030; Souza, Introduction). Specifically, a POSITA developing Katis's distance running mode would look to Souza to supply the validated Evidence-Based biomechanical parameters required to populate the hierarchical layers of the trained model with meaningful data rather than arbitrary inputs (Souza, Title).
A person of ordinary skill in the art would have been motivated to integrate the ground contact and impact indexes from Souza into the system of Katis to achieve the benefit of reducing injury risk, as Souza teaches that Running biomechanics play an important role in the development of injuries and identifying these variables allows for the development of treatment strategies (Souza, summary).
A PHOSITA would have had a reasonable expectation of success in combining the references because the modification required only ordinary skill and routine experimentation. Souza demonstrates that these variables are essentially measuring 2-dimensional (2D) video surrogates (Souza, Summary), which is perfectly compatible with Katis's method of generating position data based on image data.
The combination of Katis, Mettler, Verstegen, and Souza teaches the running improvement guidance device with hierarchical indexes for the lower extremities (landing impact, braking force, ground contact). However, this combination fails to disclose wherein the lowermost indexes of the lowermost layer include trunk angle.
Teng (Reference V) teaches the Missing Element in bold, describing a study where The trunk angle was calculated as the orientation of the trunk segment relative to the global coordinate system (Teng, Methods) and identifying sagittal plane trunk posture as a critical variable (Teng, Abstract).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to combine the teachings of Katis + Souza with Teng because both references operate in the same field of running biomechanics and injury prevention.
A POSITA would recognize that Souza's lower-body analysis is complemented by Teng's upper-body analysis, creating a comprehensive "whole-body" index list for the processor, as Teng explicitly links trunk mechanics to knee stress which is a common running injury (Teng, Abstract).
A person of ordinary skill in the art would have been motivated to integrate the trunk angle from Teng into the system of Katis to achieve the benefit of reducing joint stress, as Teng teaches that Incorporation of a forward trunk lean may be an effective strategy to reduce PFJ Patellofemoral Joint stress during running (Teng, Conclusion).
A PHOSITA would have had a reasonable expectation of success in combining the references because the modification required only ordinary skill and routine experimentation. Integrating a defined kinematic vector like trunk angle (from Teng) into a processor capable of tracking athlete position data (as taught by Katis ) is a routine task in computer vision and biomechanics analysis.
Note: Claims 8-9, 11-12 are rejected by the same analysis above as being very similar.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSHUA DAMIAN RUIZ whose telephone number is (571)272-0409. The examiner can normally be reached 0800-1800.
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, Shahid Merchant can be reached at (571) 270-1360. 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.
/JOSHUA DAMIAN RUIZ/Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684