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 .
Response to Amendment
The Amendment filed 10/09/2025 in response to the Non-Final Office Action mailed 07/31/2025 has been entered.
Claims 1-3, 5 and 7-15 are currently pending in U.S. Patent Application No. 18/020,217 and an Office action on the merits follows.
Response to Specification Objections
Examiner acknowledges and appreciates Applicant’s submission of a more descriptive title, and corresponding objection(s) are withdrawn accordingly. Examiner also notes however that the title has now been entered (not by the Examiner) to include all of the text below that “as shown below:” portion of the amendment, so as to also include “Title:”. Applicant may consider an additional amendment to remove “Title:” if desired.
Response to Interpretation under 35 USC § 112(f)
Applicant’s remarks regarding 112(f) invocation have been considered and determined largely non-persuasive. As an initial note, any identification of 112(f) invocation is not a rejection per se, as Applicant’s page 8 remarks section 2. title/heading might suggest – and the claims have not been rejected under 112(b)/(a) in view of any 112(f) invocation, e.g. under an assertion that the written description fails to disclose the corresponding structure, material, or acts for performing the claimed function and/or clearly link such structure to the function (see MPEP 2181(II)(A-C)). The fact that no 112(b) rejection was given/made in response to invocation is an implicit admission that requisite structure is present in Applicant’s disclosure at large, however Prongs A-C in the analysis determining if 112(f) is invoked concern what is recited in the claims (MPEP 2181(I) outlines the test for determining if the claim(s) invoke, and (II) describes requirements upon invocation). From MPEP 2181 section I. “Application of 35 U.S.C. 112(f) is driven by the claim language, not by applicant’s intent or mere statements to the contrary included in the specification or made during prosecution. See In re Donaldson Co., 16 F.3d at 1194, 29 USPQ2d at 1850”. For functional language (when considered at Prong B) that is e.g. ‘storing information’, even if the recited “section” is arguably a nonce term/generic placeholder when considered at Prong A, such language is preceded by the term/modifier “storage”, which may be a sufficient structural modifier when considered at Prong C (given the nature of the function at B). In other words, as recited the claim language concerns ‘storage sections’ and not all/other ‘sections’ to include software only embodiments that may not even be capable of storing, and accordingly at least connote sufficient structure (as understood by POSITA) for the associated function. With reference to MPEP 2181 and Williamson v. Citrix Online, LLC, 792 F.3d 1339, 1349, 115 USPQ2d 1105, 1111 (Fed. Cir. 2015), “[t]he standard is whether the words of the claim are understood by persons of ordinary skill in the art to have a sufficiently definite meaning as the name for structure”. Examiner disagrees however with any assertion that the terms “a meaning estimation section” and/or “recognition section” is/are understood by POSITA as the name for structure. This is particularly the case because the associated functional language for these terms, goes beyond ‘storing information’. The preceding language/modifier “a meaning estimation” and “recognition” are not structural, but instead functional in nature (in so far as consideration(s) at Prong C). The fact that Applicant’s remarks reference the specification reinforces an understanding that the recited claim language does not on its face, as recognized by a person of skill in the art, denote structure. Remarks at page 9 paragraph 2 appear to suggest that the language “based on spatial relationships…” (further describing that ‘estimating’) is a structural modifier when considered at Prong C, and while it may connote algorithmic structure this language is part of the functional language considered at Prong B. Remarks appear to suggest that “recognition section” would be understood by POSITA as requiring “a learned model constructed based on a predetermined machine learning algorithm”, however no such model is recited, and such model(s) are often described as having entirely software based embodiments (many machine learning models are a series of functions). Applicant also argues (final paragraph of page 9) “The term "section" in the context of information processing apparatuses is commonly understood in the art to denote specific hardware/software modules with defined structural implementations, not generic placeholders” (emphasis added). Accordingly, by Applicant’s own admission the term “section” may at least in some instances and to include those recited, be recognized as “software modules”, and the term “module” itself is also an established/ recognized nonce term/generic placeholder when considered at Prong A (see MPEP 2181 section I sub-section A). Stated differently, the fact that a ‘section’ as known to POSITA may denote ‘software modules’, serves as evidence against any determination that “recognition section” denotes structure and excludes solely software embodiments. Examiner maintains that at least claims 1-3, 5, 7-12 and 15 invoke the provisions of 112(f) accordingly. As identified above this is not a rejection per se, and in practice such an identification often has minimal impact given the manner in which the associated/disclosed structure and equivalents is/are invoked (upon invocation the claim limitation is “construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof”). See MPEP 2173.01 and 2111. Claim language (for at least Examination purposes which may differ from e.g. court proceedings) is given Broadest Reasonable Interpretation (BRI) consistent with the specification as it would be interpreted by POSITA in all instances to include those where 112(f) is invoked and those where it is not, it is simply the case that in the absence of invocation, it is more common for a ‘plain meaning’ (ordinary and customary meaning) to apply, as potentially distinct from the meaning otherwise provided in the specification and equivalents thereto (however in essence the same/equivalent interpretation can be reached in the absence of invocation). See MPEP § 2111:
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Applicant may amend claim(s) to explicitly recite e.g. a processor and/or memory as housing the ‘sections’, if desired (see Applicant’s Fig. 16 and [0199]). In either instance the corresponding structure for the sections in question, namely processor 10a disclosed as comprising section 220 (and equivalents), remains used for claim interpretation purposes.
Response to 35 USC § 112 Rejections
In view of the foregoing amendments to claim 11 (amending dependency so as to include claim 5), claim rejections under 35 U.S.C. § 112(b) to claim 11 are withdrawn.
Response to 35 USC § 101 Rejections
Applicant’s remarks concerning Subject Matter Eligibility analysis have been considered and determined non-persuasive. Applicant’s remarks at page 10 assert that the ‘estimating the meaning relationship’ (wherein the ‘meaning relationship’ is e.g. "the person is going to hit the golf ball with the golf club" (PGPUB [0074], [0112])) is precluded from interpretation under the mental processes grouping “because it requires automated feature point extraction from image objects using computer vision algorithms, computational estimation of supplementary information (…), and algorithmic processing to correlate spatial relationships with pre-stored relationship information”. Examiner acknowledges that the ‘supplementary information’, ‘feature points’ and stored ‘spatial relationship’ are all basis for the determination/estimation in question – but none of these elements are themselves precluded from being determined mentally and more importantly none of these elements preclude the estimating in question from being performed mentally. ‘Feature points’ can and often are visually/mentally recognized (see applicant’s PGPUB at [0198] (Applicant’s remarks reference Spec ¶ 182) disclosing a judgement of a person’s posture and manual input) – e.g. evaluating an image/video/person performing a golf swing, and detect feature points that are e.g. hands, elbows, feet, golf-ball, etc.. This finding is further evidenced by the manner in which training samples for supervised pose recognition models are/were often/historically manually generated. Even if they (the feature/key-points) are first annotated by some automatic algorithm, they can still then be visually recognized post annotation (meaning the estimation can still occur mentally despite a computer vision algorithm being used to ‘extract’ feature points – a person looking at Applicant’s Fig. 4 can mentally determine the so-called ‘meaning relationship’ example from above). Similarly, a mentally ‘stored’ ‘spatial relationship’ may be referenced in visually/mentally recognizing that a person is performing a certain action (such as the golf swing example based on the relative position between object/club and person) – so even the ‘spatial relation’ as recited (e.g. the club at a certain position relative to the body), is not of any complexity or other characteristic barring it from being analyzed/ considered mentally. This is also the case for the ‘supplementary information’, which as broadly recited in the alternative may be e.g. the pose of the person, the orientation of the club/object, etc., among a very broad array of alternative embodiments – all visually/mentally recognizable/determinable. Examiner accordingly disagrees with Applicant’s remarks in so far as they assert that the estimating cannot be performed mentally when considered at Prong One of 2A.
Remarks at page 11, first paragraph, again reference the specification (distinct from claim limitations explicitly recited) in asserting that the feature points, in optional embodiments, may be extracted ‘using’ a learned model. Even if such a use was explicitly recited, the use of such a model would not be an additional element for integration at Prong Two (instead the ‘apply it’ considerations of MPEP 2106.05(f) and/or (h) would apply). Support for this determination can be found in the 2024 PEG – previously linked at page 3 of the Non-Final, and Example 47 claim 2 – wherein (d) is particularly relevant. The feature/key points in question are much like the ‘one or more anomalies’ detected in the example analysis, and that recited ‘using the trained ANN’ was insufficient for integration at Prong Two of 2A.
“Step (d) recites detecting one or more anomalies in a data set using the trained ANN. The claim does not provide any details about how the trained ANN operates or how the detection is made, and the plain meaning of “detecting” encompasses mental observations or evaluations, e.g., a computer programmer’s mental identification of an anomaly in a data set.
…
As discussed above, the broadest reasonable interpretation of steps (b), (d), and (e) is that those steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III.
…
The limitations in (d) and (e) reciting “using the trained ANN” provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.”
Examiner also understands the recited feature points, and person/body pose based thereon, as disclosed in Applicant’s Specification to be extracted/recognized by means of e.g. off the shelf ML models (reinforced by Applicant’s remarks and absence of specifics regarding such models in Applicant’s description) – as such even if any recognition/extraction of these elements was an ‘additional element’ (which they are not) they would at best generally link to fields of use involving such models (MPEP 2106.05(h)).
Applicant’s remarks further assert that the claim realizes one or more improvements to include an improvement to the technical field that is computer vision itself, which might be persuasive if such an improvement could be clearly linked to explicitly recited limitations that themselves were distinct from the exception (i.e. actually ‘additional elements’ for consideration at Prong Two of 2A/Step 2B), and for an improvement that is not ‘to the exception itself’ (the 2024 PEG Example 49 claim 1 analysis identifies with reference to MPEP 2106.05(a) that the improvement cannot be to the abstract idea/exception itself). Remarks at page 11 paragraph three describe one improvement as being that the ‘supplementary information’ is now automatically determined based on feature point analysis, and that this same information given its wide array of embodiments (it can even be ‘muscle strength’ as determined from feature points of the person – presumably enabled because machine learning models can derive embeddings/statistical relationships for any number of desired outputs/ classifications given sufficient training samples), is what facilitates a plurality of improvements to the technical field of computer vision based action recognition broadly, such as accuracy in the face of motion of both the person and the material body/object and/or various forms of occlusion. Despite potential benefits associated with such supplementary information, it is still recited at a high level of generality and is still a characteristic/information that may be mentally determined, and is not an ‘additional element’ distinct from the exception accordingly. Remarks page 12 paragraph 2 credit the improvement to the ‘meaning relationship’ (an action of the person as performed on/to/with one or more objects) estimation as being that it too is based on feature points, while simultaneously asserting that the feature extraction and e.g. pose recognition models optionally implemented are those that are conventional in the art. The only improvement apparent to the Examiner from those explicitly recited claim limitations is that a very broad array of human-object actions may be recognized. There is an implied ‘additional element’ that is the use of one or more machine learning models (or ‘various kinds of information processing’ executed by 220, PGPUB at [0088]) to ultimately derive that ‘meaning relationship’, however this sort of ‘use’ of ML appears to be exactly that which the 2024 PEG and recent decisions from the Federal Circuit are aimed at discouraging. Applicant may wish to review recent case law e.g. Recentive Analytics, Inc., v. Fox Corp., Appeal No. 2023-2437, (Fed. Cir. Apr. 18, 2025) available at https://www.cafc.uscourts.gov/opinions-orders/23-2437.OPINION.4-18-2025_2500790.pdf. Also Longitude Licensing Limited v. Google, LLC, available at https://www.cafc.uscourts.gov/opinions-orders/24-1202.OPINION.4-30-2025_2506816.pdf. While it is the MPEP that governs Examination and not necessarily case law, these opinions serve to illustrate the manner in which claims that seek to apply broad classes of machine learning to a ‘new’ field of use, and/or claim limitations that do not explain/capture how a purported inventive concept/ improvement is actually achieved (in a manner distinct from the exception), are not likely to be determined eligible/enforceable.
Reference may also be made to the most recent SME Memo(s) available at: https://www.uspto.gov/patents/laws/examination-policy/subject-matter-eligibility and more specifically: https://www.uspto.gov/sites/default/files/documents/memo-desjardins.pdf
The most recent memo dated December 05, 2025 makes reference to the Appeals Review Panel (ARP) Decision in Ex Parte Desjardins Appeal No. 2024-000567 (09/26/2025), for Application No. 16/319,040, designated precedential on November 4, 2025. It is the Examiner’s understanding that Enfish was cited specifically in that instance, not so as to imply that training a ML model is ‘functioning of a computer’ analogous to that of Enfish (memory read/write operations being integral to the way computers operate – in other words ‘functioning’ of a computer is not the wide array of “functions” which general/special purposes computers can be made to perform (such as activity recognition), but instead functions integral to the way computers operate – and instead ‘training a ML model’ is more akin to a ‘technical field’ given the recent changes to the MPEP), but so as to convey the manner in which ‘software’ (and not solely hardware elements) can be the means by which an improvement is realized. Unlike an analysis for the instant claims however, the analysis by the ARP in Desjardins deemed that “adjust[ing]… parameters.. to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task” as an ‘additional element’ that was not “subsumed in the identified mathematical calculation” (as revisions to MPEP § 2106.04(d)(1) are due to reflect). Examiner asserts that for the case of the instant claims, there are no ‘additional elements’ that avoid being subsumed by/drawn under the exception. Even if that broadly recited ‘supplementary information’ might achieve any of those purported improvements in any of the extremely broad array of ‘meaning relationship’ estimation applications (the recited estimation/recognition – of an extremely broad array of actions of a person as performed on/to/with one or more of any other objects conceivable – arguably inviting scope of enablement rejection(s)), no limitations outweigh those drawn to the exception. While preemption is not a standalone test for eligibility, the Alice/Mayo two-part framework’s roots in preemption, as described in the MPEP require weighing ‘additional elements’ (those that are precluded from being drawn to the exception at Prong One), not in a vacuum, but with/in view of those portions of the claim falling under the exception (MPEP 2106.04(d)), when evaluating whether “meaningful limits” are imposed. The claims remain rejected under 35 USC § 101 accordingly and that previously presented grounds is reproduced below.
Response to Arguments/Remarks
Applicant's arguments filed 10/09/2025 have been fully considered but they are not persuasive. Claims as amended incorporate those limitations of now cancelled claims 4 and 6 in the alternative. More specifically (remarks at page 13), that ‘supplementary information’ include[es]:
information about at least one of the person's position, physique, posture, or orientation estimated based on feature points of the image object of the person, or
information about at least one of a position, size, shape, or orientation of the material body estimated based on feature points of the image object of the material body.
Applicant asserts that for both instances, feature points are not the basis of that information which might otherwise serve as ‘supplementary information’ equivalent(s):
For (1) Yabuki as applied, because the locations of the pommel horse are ‘predetermined’ they are not ‘estimated from feature points’;
(2) Jang’s features are not used as any basis for estimating supplementary information; and
(3) Asikainen’s user profile information is under no circumstance derived on the basis of feature points, but solely manual measurement and/or user input.
Regarding (1), Yabuki’s evaluation unit 155 specifies regions 8a-e (Fig. 10, [0036]) ‘support positions’, of the pommel horse such that they are known/predetermined prior to one or more steps of detecting/analyzing those keypoints forming a pose for subject/gymnast 5, but this does not preclude any modification to Yabuki such that this ‘specifying’ by 155 is accomplished by means of object recognition/feature point extraction (as addressed in the modification in view of Jiang as previously presented in the rejection of claim 1). Similarly, the fact that support positions of the pommel in Yabuki are fixed/stationary, does not preclude them from being detected/recognized from image feature points – as feature points can be detected for stationary as well as moving objects. More importantly however, that supplementary information is presented in the alternative (it may pertain to the person, or the material body/object), and may simply be the position, posture or orientation of the subject 5 – which Yabuki does determine on the basis of those disclosed feature points (e.g. Yabuki Fig. 6). At least Yabuki discloses ‘supplementary information’ as now recited on this basis alone.
Regarding (2), Examiner respectfully disagrees because the supplementary information embodiment pertaining to the object minimally includes an object position and/or size, which Jiang at least suggests in view of that associated object detection e.g. [0073] “Similarly, a per-frame object detection module 530 is configured to process the video frame to identify and
output detected object regions in the video frame, and each region can be represented by a bounding box (xj, yj, wj, hj) for the j-th object.” Jiang at the minimum suggests detecting from an image and associated object feature points, a position that is represented in terms of x and y pixel coordinates and an object size that is w by h dimension.
Regarding (3), Applicant’s selective emphasis on the profile information 222 of Asikainen (which also appears updated on the basis of those video based metrics determined within the disclosure at large and not solely those manually input, [0064-0066]) fails to recognize the manner in which Asikainen discloses equivalent supplementary information that is in fact based on object and/or person features as extracted/recognized by at least pose estimator 302 and object detector 304. For at least that person/user, position, physique, posture and/or orientation as suggested at least in [0078] “The pose estimator 302 receives the processed sensor data stream including one or more images from the data processing engine 204 depicting one or more users and estimates the 2D or 3D pose coordinates for each keypoint (e.g., elbows, wrists, joints, knees, etc.). The pose estimator 302 tracks a movement of one or more users in real-world space by predicting the precise location of keypoints associated with the users. For example, the pose estimator 302 receives the RGB image and associated depth map, inputs the received data into a trained convolutional neural network for pose estimation, and generates 3D pose coordinates for one or more keypoints associated with a user”. See also at least [0072], [0090], [0094] and [0101]. For that object (e.g. equipment 134 of Asikainen), as was previously identified in the rejections of claims 6 and 5, Asikainen [0077], [0080] and [0087] – at the minimum disclosing [0080] “For example, the object detector 304 receives the RGB image and associated depth map, inputs the received data into a trained You Only Look Once (YOLO) convolutional neural network for object detection, detects a location of an object (e.g., barbell with weight plates)”. Asikainen even discloses in [0087] that a detected facial expression may be used by performance tracker 314. As was previously presented at least in the rejection of claim 6 (page 21 of the Non-Final), Asikainen further provides motivation regarding ‘supplementary information’ equivalents in that such information allows for a more nuanced classification that better tracks a user’s progress in fitness goals, and/or may serve to group users similar/related users with same or competing interests. For example, instead of an activity recognition such as ‘a person is performing a bench-press’, Asikainen suggests a system/method providing a more nuanced estimation e.g. ‘user Wyeth v Stone has performed a second repetition of squatting 1840 lbs’ and such an achievement may then be shared on social media with Wyeth’s group of fellow ice cutters/case law enthusiasts. Asikainen suggests training engine 202 may access a user’s social media ([0064] “the personal training engine 202 may access an API 136 of a third-party social network server 140”, etc.,) and gamification engine 212 may create badges/achievements associated with performance statistics ([0100], etc.,).
The equivalent disclosure identified with respect to (1)-(3) above is also identified under an assumption/ interpretation that the ‘information about’ a, b, c, or d, actually includes a, b, c, and/or d and not otherwise some related/associated information. As may be gleaned from the Non-Final Office Action, “supplementary information accompanying the image objects” was initially understood by the Examiner to involve information not necessarily derivable directly from associated images/sub-images (which is why Asikainen was relied upon in the rejection of claim 2) – however applicants remarks have clarified the manner in which that ‘based on feature points’ language of claims 4 and 6 as previously presented is intended to further modify the information in question as opposed to those characteristics a, b, c or d of which said information may be ‘about’. The claims as amended do not constrain ‘how’ that ‘supplementary information’ is determined other than its basis on image/video derived feature points broadly, and Applicant’s disclosure as a whole appears to suggest, particularly in view of the broad array of embodiments for such information, that its detection from images/video may be accomplished by means of known/conventional machine learning models and/or image analysis within the abilities of POSITA. References of record suggest the same and corresponding rejections to the claims are maintained accordingly.
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.
Claim(s) 1-3, 5 and 7-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, in particular an Abstract Idea falling under at least the (c) mental processes grouping (concepts performed in the human mind including an observation, evaluation, judgement, opinion), not ‘integrated into a practical application’ at Prong Two of Step 2A and without ‘significantly more’ at Step 2B.
Step 1: The claim(s) in question are directed to a computer implemented (hardware/ structural limitations considered under the ‘apply it’ provisions of MPEP 2106.05(f)) method/ process for estimating, based on ‘relationship information’ which ‘associates’ two other pieces of information (namely a ‘spatial relationship’ and ‘meaning information’ (See Fig. 2)) a so-called ‘meaning relationship’ (see PGPUB at [0074] and claim 7 regarding interpretation for said ‘meaning relationship’ e.g. “the person is going to hit the golf ball with the golf club” ([0112])). Put simply, a process for estimating/determining a ‘meaning relationship’ (an action of the person as performed on/to/with one or more objects). (Step 1: Yes).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim. Claims 1/13/14/15 recite at a high level of generality that “estimating… a meaning relationship”, and also that “extracting”/recognizing ‘feature points’ (for the case of claim 15), falling (in view of a plain meaning/broadest reasonable interpretation(s), see MPEP 2111.01) under at least the mental processes Abstract Idea category. Reference may be made to the July 2024 PEG (available at https://www.uspto.gov/sites/default/files/documents/2024-AI-SMEUpdateExamples47-49.pdf ) and the analysis of those various limitations drawn to the mental processes grouping, to include those of Example 47 claim 2 (featuring a broadly recited anomaly detection (step (d)) and analysis thereof (step (e)) each falling under the mental processes grouping). The claims/limitations in question are recited at a high level of generality and lack any specifics precluding such an analysis from being interpreted under the mental processes grouping practically performed in the mind (see also MPEP 2106.04(a)(2) identifying how e.g. a use of pen and paper and/or a computer as a tool (to assist in visually/mentally analyzing/observing acquired images/video) fail to preclude such an interpretation under the mental processes Abstract Idea grouping). Dependent claims are similarly analyzed at Prong One (e.g. ‘supplemental information’ similarly considered/evaluated mentally). (Step 2A, Prong One: Yes).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by (1) identifying whether there are any ‘additional elements’ recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). Examiner notes for consideration at Prong Two of 2A that MPEP 2106.05(a), (b), (c), and (e) generally concern limitations that are indicative of integration, whereas 2106.05(f), (g), and (h) generally concern limitations that are not indicative of integration. As an additional note, ‘additional elements’ are generally limitations excluded from interpretation under the Abstract Idea groupings, and may comprise portions of limitations otherwise identified as falling under those Abstract Idea groupings of the 2019 PEG (e.g. any ‘determination’ that may be made mentally accompanied by the use of a neural network and/or generic computer hardware considered under the ‘apply it’ considerations of 2106.05(f)). Any ‘providing’/outputting broadly, and ‘collection’ of data (i.e. image acquisition(s)), be they images for training any learning model and/or data/images visually observable/ evaluated by a user/operator, also fail(s) to integrate at least in view of MPEP 2106.05(g) (extra-solution data gathering/output) and/or 2106.05(h) as ‘generally linking’ the exception to a field of use involving machine learning and/or imagery so acquired. The same determination holds for dependent claims that serve to limit the collection of data/images (by means of what is collected based on recited conditions) and/or introduce limitations generally linking to a field of use (Examiner notes that a field of use e.g. ‘grading a golf swing’ is gleaned entirely from the non-limiting Specification). None of the instant claims appear to explicitly/clearly capture/recite any disclosed improvement in technology (see MPEP 2106.05(a), with note that ‘functioning of a computer’ concerns functions integral to the way a computer operates and not ‘functions’ that a generic computer can be programmed/adapted to perform (see also 2106.05(f))) and any ‘additional elements’ (namely only the use of a generic and/or programmed/special purpose computer considered under the ‘apply it’ provisions of 2106.05(f)), even when considered in combination, fail to integrate at Prong Two of Step 2A accordingly. Integration in view of 2106.05(a) requires an identification of the manner in which the improvement is achieved, to be explicitly and specifically (not at a high level of generality) recited in the claims, as ‘additional elements’ precluded from interpretation under any of the Abstract Idea groupings (since the improvement cannot be to the exception itself). With reference to MPEP 2106.05(a):
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 the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981))
Even when viewed in combination, the ‘additional elements’ present do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: No), and the claims are directed to the judicial exception. (Revised Step 2A: Yes [Wingdings font/0xE0] Step 2B).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to ‘significantly more’ than the recited exception, i.e., whether any ‘additional element’, or combination of additional elements, adds an inventive concept to the claim. The considerations of Step 2A Prong 2 and Step 2B overlap, but differ in that 2B also requires considering whether the claims feature any “specific limitation(s) other than what is well-understood, routine, conventional activity in the field” (WURC) (MPEP 2106.05(d)). Such a limitation if specifically recited however, must still be excluded from interpretation under any of the Abstract Idea groupings. Step 2B further requires a re-evaluation of any additional elements drawn to extra-solution activity in Step 2A (e.g. gathering video/image(s)) – however no limitations appear directed to any novel collection per se. Limitations not indicative of an inventive concept/ ‘significantly more’ include those that are not specifically recited (instead recited at a high level of generality), those that are established as WURC, and/or those that are not ‘additional elements’ by nature of their analysis at Prong One (i.e. directed to the exception). The July 2024 PEG describes that an improvement/ inventive concept (for ‘significantly more’ determination(s)) cannot be to the judicial exception itself. The claim(s) in question recite little beyond those limitations recited at a high level of generality and falling under e.g. the mental processes Abstract Idea grouping, and the claim(s) in question do not amount to ‘significantly more’ than the Abstract Idea/exception accordingly. (Step 2B: No).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 2 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends. Claim 2 was previously intervening for both claims 4 and 6, now cancelled and incorporated into claim 1, and claim 2 provides no additional limitations outside of those now recited in claim 1. Specifically “wherein the estimating comprises estimating the meaning relationship among the plurality of image objects based on supplementary information accompanying the image objects” (claim 1) is not significantly different from “wherein the meaning estimation section estimates the meaning relationship among the plurality of image objects based on supplementary information accompanying the image objects” (claim 2). Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 of this title, 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.
1. Claims 1-3, 5 and 7-15 are rejected under 35 U.S.C. 103 as being unpatentable over Yabuki et al. (US 2019/0220657 A1) (cited by Applicant in 02/07/2023 IDS) in view of Jiang et al. (US 2019/0180090 A1) (cited in Applicant’s 08/09/2024 IDS) and Asikainen et al. (US 2021/0008413 A1).
As to claim 1, Yabuki discloses an information processing apparatus (Abs “A motion recognition device includes a memory, and a processor coupled to the memory and configured to classify a plurality of frames including positional information of a feature point that corresponds to a predetermined part or a joint part of a body of a subject”) comprising:
a storage section (e.g., storage 140, comprising data(s) 141-145, [0044], Fig. 4, etc.,) storing relationship information in which a spatial relationship among feature points included in a plurality of target objects is associated with meaning information ([0044] “The storage unit 140 has skeleton data 141, calculation result data 142, skill approval rule data 143, upgrade rule data 144, and evaluation result data 145”, [0044-0048], Fig. 5, Fig. 8, Fig. 12, [0052] “The description will be made returning to FIG. 4. The skill approval rule data 143 is data used in a case where a first evaluation unit 155 which will be described later determines the skill name and the difficulty level. FIG. 8 is a view illustrating an example of a data structure of the skill approval rule data. As illustrated in FIG. 8, the skill approval rule data 143 includes basic motion type, starting point body angle region, end point body angle region, starting point left hand support position, starting point right hand support position, end point left hand support position, end point right hand support position, previous basic motion type, skill name, group, and difficulty level”, Fig. 11, etc.,) among the plurality of target objects (among subject 5 and pommel horse 8, however permissible interpretation further includes skeleton points 5a-5m and points/portions of e.g. pommel 8, 8a-8e); and
a meaning estimation section (Fig. 1 100 control unit 150, Fig. 4) estimating, based on a spatial relationship among feature points extracted from a plurality of image objects included in an image and the relationship information, a meaning relationship among the plurality of image objects (Figs 5, 8, 11, 12, 17, etc., [0059], etc., wherein at least those higher order labels e.g. ‘skill name’, score information, difficulty level(s), etc., are determined on the basis of hand/foot position and body vector data, in conjunction with basic motion type information and partial data for neighboring frames within a segment of interest, [0121], etc.,);
While the claim does not require the detection/recognition of any feature points for the pommel horse itself (at least for the case of claim 1, the plurality of target objects may all be skeleton points associated with subject 5) as distinct from the subject’s hands, Yabuki discloses that support positions 8a-8e are known/predetermined, and that the position of hands relative thereto is also identified (in other words Yabuki at least suggests a spatial relationship that is between the hands and one or more of the support positions for certain moves/actions) ([0057] “FIG. 10 is a view illustrating an example of the support position. In a case where the hand (left hand or right hand) of the subject 5 exists in the region 8a, the position of the hand corresponds to the support position "1". In a case where the hand of the subject 5 exists in the region 8b, the position of the hand corresponds to the support position "2". In a case where the hand of the subject 5 exists in the region 8c, the position of the hand corresponds to the support position "3". In a case where the hand of the subject 5 exists in the region 8d, the position of the hand corresponds to the support position "4". In a case where the hand of the subject 5 exists in the region 8e, the position of the hand corresponds to the support position "5"”).
Jiang further evidences the obvious nature of a recognition section extracting feature points from each of a plurality of image objects included in a moving image, the plurality of image objects including an image object of a person and an image object of a material body (Abs “The computing device is configured to process a video frame of a video segment on a per-frame basis and based on joint human-object interactive activity (HOIA) to generate a per-frame pairwise human-object interactive (HOI) feature based on a plurality of candidate HOI pairs. The computing device is also configured to process the per - frame pairwise HOI feature to identify a valid HOI pair among the plurality of candidate HOI pairs and to track the valid HOI pair through subsequent frames of the video segment to generate a contextual spatial-temporal feature for the valid HOI pair to be used in activity detection”, Fig. 3B 371 in conjunction with 372, Fig. 5, Fig. 8, etc.,).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date, to modify the system and method of Yabuki so as to apply those teachings for the detection of those points of subject 5, in the detection of pommel horse points associated with predetermined regions 8a-8e, even if simply to corroborate the location of those various pommel segments ‘predetermined’ by alternative means, the motivation being as readily recognized by PHOSITA that such a detection/recognition would serve as an “Obvious to try” means for determining pommel horse segments (and/or other object(s)) with a reasonable expectation of success while serving to confirm/validate those same positions if otherwise determined (see MPEP 2143 Rationale (E) and (G)).
Yabuki in view of Jiang further suggests the apparatus wherein the plurality of image objects include an image object of a person (Yabuki subject 5, Jiang Fig. 5 human 1, human 2, etc., detected by 520/371) and an image object of a material body (Yabuki pommel horse 8 as identified in the combination/modification presented in the rejection of claim 1; Jiang Fig. 5 object 1, object 2, etc, from object detection 530/372), and wherein the estimating comprises estimating the meaning relationship among the plurality of image objects based on supplementary information accompanying the image objects, the supplementary information including information about at least one of the person's position, physique, posture, or orientation estimated based on feature points of the image object of the person, or information about at least one of a position, size, shape, or orientation of the material body estimated based on feature points of the image object of the material body (see remarks above regarding ‘supplementary information’ at least suggested in (1) Yabuki for those embodiments that include subject/person 5 position, posture, or orientation, and (2) from above re. Jiang’s disclosure of an image feature derived object (e.g. sports ball) position and size/dimensions),
Asikainen further evidences the obvious nature of an apparatus wherein the plurality of image objects include an image object of a person (Asikainen Fig. 4, [0079] set of connected keypoints from pose estimator 302) and an image object of a material body (Asikainen Object Detector 304, [0077] “As depicted, the feedback engine 208 may include a pose estimator 302, an object detector 304, an action recognizer 306, a repetition counter 308, a movement adherence monitor 310, a status monitor 312, and a performance tracker 314. Each one of the components 302, 304, 306, 308, 310, 312, and 314 in FIG. 3 may be configured to implement one or more machine learning models 226 trained by the machine learning engine 206 to execute their functionality as described herein”), and wherein the estimating comprises estimating the meaning relationship among the plurality of image objects based on supplementary information accompanying the image objects, the supplementary information including information about at least one of the person's position, physique, posture, or orientation estimated based on feature points of the image object of the person, or information about at least one of a position, size, shape, or orientation of the material body estimated based on feature points of the image object of the material body (see remarks above re. (3), see also Fig. 5, [0065] “In some implementations, the user profile 222 may include additional information about the user including name, age, gender, height, weight, profile photo, 3D body scan, training preferences (e.g. HIIT, Yoga, barbell powerlifting, etc.), fitness goals (e.g., gain muscle, lose fat, get lean, etc.), fitness level (e.g., beginner, novice, advanced, etc.), fitness trajectory (e.g., losing 0.5% body fat monthly, increasing bicep size by 0.2 centimeters monthly, etc.) … The personal training engine 202 stores and updates the user profiles 222 in the data storage 243”, [0066], [0072] “the machine learning engine 206 may train the one or more machine learning models 226 for a variety of machine learning tasks including estimating a pose (e.g., 3D pose (x, y, z) coordinates of keypoints), detecting an object (e.g., barbell, registered user), detecting a weight of the object (e.g., 45 lbs), edge detection (e.g., boundaries of an object or user), recognizing an exercise movement (e.g., dumbbell shoulder press, bodyweight push-up), detecting a repetition of an exercise movement (e.g., a set of 8 repetitions), detecting fatigue in the repetition of the exercise movement, detecting heart rate, detecting breathing rate, detecting blood pressure, detecting facial expression, detecting a risk of injury, etc. In another example, the machine learning engine 206 may train a machine learning model 226 to classify an adherence of an exercise movement performed by a user to predefined conditions for correctly performing the exercise movement”, [0080], [0090] engine 210 recites user profile data including those ‘common characteristics’, [0094] “the recommendation engine 210 receives the user profile of a user, analyzes the profile of the user, and generates one or more recommendations based on the user profile”, [0101], etc., ; Examiner notes the mapping is not exhaustive – see remarks above re. the manner in which it is not solely profile 222 of Asikainen that reads on that ‘supplementary information’).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date, to further modify the system and method of Yabuki in view of Jiang such that the 150/evaluation unit 155, estimates the meaning relationship among the plurality of image objects (comprising a person and material body) based on supplementary information as taught/suggested by Asikainen, the motivation as similarly taught/suggested therein that such supplementary information may enable the engine to provide user-specific multi-exercise feedback involving progress tracking, in addition to enabling a gamification, leaderboard, etc., involving comparison, ranking, etc., to other users sharing one or more similar profile characteristics.
As to claim 2, Yabuki in view of Jiang and Asikainen teaches/suggests the apparatus of claim 1. Claim 2 is similarly rejected under that same disclosure/rationale as presented above for the case of claim 1. Claim 2 does not further limit claim 1 – see 112(d) above.
As to claim 3, Yabuki in view of Jiang and Asikainen teaches/suggests the apparatus of claim 2.
Yabuki in view of Jiang and Asikainen further teaches/suggests the apparatus wherein the plurality of image objects include an image object of a person (Yabuki subject 5, Jiang Fig. 5 human 1, human 2, etc., detected by 520/371; Asikainen Fig. 4, [0079] set of connected keypoints from pose estimator 302); and
the supplementary information includes information about at least any of the person's age, gender, muscle strength, exercise capacity, and wearing or carrying article (Asikainen [0065], [0072], [0090], [0094], [0101] in view of that combination/modification as established in the rejection of claim 1 above).
As to claim 5, Yabuki in view of Jiang and Asikainen teaches/suggests the apparatus of claim 2.
Yabuki in view of Jiang and Asikainen further teaches/suggests the apparatu