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 .
This office action is in response to communication filed on 5/16/2025.
Claims 1-20 are presented for examination.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory anticipated double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,347,542.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Determining that a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 U.S.C. 101 (i.e., process, machine, manufacture, or composition of matter). (MPEP 2106.03)
Claims 1-7 recite a series of steps, thus falling within one of the four statutory classes; i.e., a process. Claims 8-14 describe non-transitory computer readable medium, thus falling within one of four statutory classes; i.e. manufacture. Claims 15-20 describe tangible system components, thus falling within one of the four statutory classes; i.e., machine.
Step 2A, Prong One: Evaluating whether the claim(s) recite(s) a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. (MPEP 2106.04).
Representative claim 1 recites:
inputting, a user profile to an exercise selection model to rank a set of exercises for a user to perform based on a history of exercises the user has performed, available gym equipment, and one or more exercise goals of the user;
modifying, the ranking of exercises based on a level of variance selected by the user, wherein the modification comprises:
selecting a random set of exercises performed by the user within a threshold period of time based on the level of variance; and adjusting rankings of the selected random set of exercises;
applying, a weight recommendation to performance statistics of the user and a first exercise from the set of exercises, the weight recommendation trained using supervised learning on training data including a plurality of exercise pairs each labeled with current capabilities of and weight recommendations for users that completed both exercises in a respective exercise pair;
receiving, from the weight recommendation, a current target weight to recommend to the user for the first exercise; and
modifying, to display the current target weight and the first exercise.
The claims as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting “by a processor”, “Machine learning”, and “graphical user interface” nothing in the claim elements precludes the steps from practically being performed in the mind.
For example, but for the reciting “by a processor”, “Machine learning”, and “graphical user interface” in the context of this claim encompasses actions that a human could perform; e.g., a human can recommend and modify set of exercises workout based on user’s profile. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
In addition, the limitations mentioned above (i.e., “inputting”, “modifying”, “selecting”, “adjusting”, “applying”, “receiving” in the context of this claim) as drafted, are processes that, under their broadest reasonable interpretations, exemplify managing personal behavior.
That is, other than reciting “by a processor”, “Machine learning”, and “graphical user interface”, the steps of ., “inputting”, “modifying”, “selecting”, “adjusting”, “applying”, “receiving” in the context of this claim encompasses steps of recommending and modifying a set of exercises workout based on user’s profile .
If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior, then it falls within the “Certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Independent claims 8 and 15 recite the same abstract idea as identified above and dependent claims 2-7, 9-14 and 16-20 further narrow it.
Step 2A, Prong Two: Identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and then evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application. Prong Two distinguishes claims that are "directed to" the recited judicial exception from claims that are not "directed to" the recited judicial exception. (MPEP 2106.04).
This judicial exception is not integrated into a practical application. In particular, the claims recite the following additional elements:
• by a processor (claims 1, 8 and 15); machine learning model (claims 1, 8 and 15); graphical user interface (claims 1, 8 and 15);
• A non-transitory computer-readable storage medium (claim 8);
The “by a processor”, “machine learning”, “graphical user interface”, and the “A non-transitory computer-readable storage medium” are recited at a high-level of generality (i.e., as generic processors) such that they amount no more than mere instructions to apply the exception using generic computer components. They are no more than a tool to perform the “inputting”, “modifying”, “selecting”, “adjusting”, “applying”, and “receiving” steps, and are considered as “apply it” as the claim invokes the computer as a tool to perform the abstract idea. See MPEP 2106.05(f)(2) (similar to Apple, Inc. v Ameranth and Intellectual Ventures I LLC v Capital One Bank (USA).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (MPEP 2106.05(f) Mere Instructions To Apply An Exception).
Regarding the limitations “by a processor”, “machine learning”, “graphical user interface”, and the “A non-transitory computer-readable storage medium” ,as seen above, this limitation has been interpreted as “apply it”. However, this limitation can be additionally interpreted as insignificant extra-solution activity. As such, this limitation alone and in combination, does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. (MPEP 2106.05(g) Insignificant Extra-Solution Activity).
Therefore, under Step 2A, Prong Two, the claims are directed to an abstract idea.
Step 2B: Identifying whether there are any additional elements (features/limitations/steps) recited in the claim beyond the judicial exception(s), and then evaluating those additional elements individually and in combination to determine whether they contribute an inventive concept (i.e., amount to significantly more than the judicial exception(s)). (MPEP 2106.05)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “by a processor”, “machine learning”, “graphical user interface”, and the “A non-transitory computer-readable storage medium” alone and in combination amount to no more than mere instructions to apply the exception using generic computer components.
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Regarding the limitations “by a processor”, “machine learning”, “graphical user interface”, and the “A non-transitory computer-readable storage medium”; it is noted that sending information over a network has been recognized in the courts as being Well Understood Routine and Conventional (see MPEP 2106.05(d)(II) - i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Therefore, these additional elements do not amount to significantly more than a judicial exception and cannot provide an inventive concept. (MPEP 2106.05(d) Well-Understood, Routine, Conventional Activity).
Therefore, claims 1-20 are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over
Riley et al. (WO 2008/030484 A2 hereinafter Riley) in view of article by Tran, titled “Recommender System with Artificial Intelligence Intelligence for Fitness Assistance System” (Tran).
With respect to claims 1, 8 and 15, Riley teaches methods, systems and computer readable mediums for generating a workout plan (See Figures 1-19).
Inputting, by a processor, a user profile to an exercise selection model to rank a set of exercises for a user to perform based on a history of exercises the user has performed, available gym equipment, and one or more exercise goals of the user (see figures 1-19 and paragraph 77 on page 34 for receiving profile of the user including: user’s age, height, weight, fitness level, gender, past performance, goals and on Figure 13 a user can begin creating a workout by selecting add part button 1302/high level of variance or delete part of the workout/low level of variance, the user may indicate a desire to add or change the activity type by selecting change block 1308 associated with part c activity type line. This action may cause a list of possible activities to appear as a pop-up as a separate screen, as an overlay and the user may select the desired activity for the workout from the list);
modifying, the ranking of exercises based on a level of variance selected by the user, wherein the modification comprises:
selecting a random set of exercises performed by the user within a threshold period of time based on the level of variance; and adjusting rankings of the selected random set of exercises (see figures 1-19 and paragraph 77 on page 34 for using the user’s parameter feedback such as stair stepping, elliptical, calories burned for modifying/ranking the workout activity or machine information to previous workouts or to user’s personal best, and on paragraph 82 “The characteristics or parameters of the workout may be varied widely, depending at least in part on the selected workout level. For example, if desired, the types of activities included as part of the workout may be changed, depending on the selected workout level. As another example, the time duration(s) of one or more of the activities may be changed, depending on the selected workout level. As yet another example, if desired, one or more of the amount of resistance, timing/pace/distance goals, calorie burn goals, overall workout times, incline levels, number of free weight lifting sets, number of repetitions per set, free weight lifting weight levels, and the like, may be varied to increase or decrease the "workout level." Also, various combinations of the potential changes described above (as well as other potential changes) may be used to change the "workout level" for the user based, at least in part, on the user's input ”.
Applying, by the processor, a weight recommendation machine learning model to performance statistics of the user and a first exercise from the set of exercises, the weight recommendation trained using supervised learning on training data including a plurality of exercise pairs each labeled with current capabilities of and weight recommendations for users that completed both exercises in a respective exercise pair; receiving, from the weight recommendation, a current target weight to recommend to the user for the first exercise; and modifying, to display the current target weight and the first exercise (i.e. user’s workout parameter feedback such as speed, distance covered, stair-stepping, elliptical, calories burned, elapsed time for machine setting change to be performed manually or automatically, such as when to move to a new machine or activity or to move to a stationary bike after 20 minutes )(Figure 13 paragraph 77 on page 34).
With respect to the selection model being a machine learning model to rank the set of exercises. Riley teaches on paragraph 77 automatically/machine model to change the workout activities based on the user’s profile and level of variance selected by the user. Riley doesn’t specifically teach that the model/machine used in Riley is machine learning. Tran teaches a recommender system to support a fitness assistance system with artificial intelligence. The recommender system is applied to make suggestions for beginners and existing users. Artificial Neural Network and Logistic Regression is employed to predict suitable workouts. It would have been obvious for Riley to have used the machine learning model of Tran in order to support a fitness assistance system with artificial intelligence, which would make the workouts of Riley more reliable.
With respect to claims 2, 9 and 16, Riley further teaches wherein the user profile includes a recency of each exercise in the history of exercises, exercise restrictions of the user, and one or more ratings for one or more exercises previously performed by the user (selection of an activity type that utilizes an exercise machine to include information relevant to that machine such as desired speed, desired incline level, desired weights or other resistance levels, historical data relating to a user’s recorded workout history)(see figures 1-19 and page 43, paragraph 96).
With respect to claims 3, 10 and 17, Riley further teaches wherein the level of variance indicates how much to vary one or more of each exercise, workout length, and focused muscle group in the workout plan for the user (i.e. desired workout intensity features, desired distance for desired calorie; flabby arm workout)(see figures 1-19 and page 43, paragraph 96; page 62, paragraph 135).
With respect to claims 4-5, 11-12, 18-19, Riley further teaches a plurality of exercise pairs each labeled with current capabilities of and weight recommendations for users that completed both exercises in a respective exercise pair; creates a function that maps the exercise pairs to the respective current capabilities and weight recommendations (i.e. User created workout routines according to examples of this invention may include one or more "parts," wherein a "part" constitutes a specific type of workout activity, such as: warm-up, walking, running, biking, rowing, use of exercise equipment or gym machines (such as treadmills, stair-stepping machines, elliptical machines, exercise bicycles, rowing machines, cross-country ski simulators, etc.), weight lifting (free weights or gym machines), yoga, dance, aerobics, martial arts, team sports, cool- down, etc. In this example system and user interface, any combination of activities may be included in a workout routine without departing from this invention, including, for example, gym or spa based activities, outdoor or free range activities, machine or free (non-machine) based activities, team sports or individual activities, etc.)(see figures 1-19 and paragraph 93).
With respect to claims 6-7, 13-14, 20, Riley further teaches wherein modifying the ranking of exercises comprises altering scores of one or more exercises recently performed by the user and removing one or more exercises with low ratings by the user from the modified ranking; removing one or more exercises associated with equipment the user does not have from the modified ranking (i.e. removing one or more activities from the workout routine; and/or changing activities or goals in the workout routine)(see figures 1-19 and paragraph 22).
References of record not applied in the current rejection:
(2021/0056643 Narulla) teaches responsive to detecting the actuation of the user interface element 326, the system can take different steps depending on the configuration of the rating exercise. For instance, in some examples, the rating exercise is complete from the user's perspective, and in such examples, a screen thanking the user can be presented. In other examples, the user is taken to one or more summary screens. Where the rating exercise is a synchronous rating exercise, responsive to actuation of the user interface element 326, the system presents a wait interface on the user's device until a condition is met.
Article by VENTICINQUE, J, TITLED “Building Data-Driven Fitness with Fitbod” teaches mixing up muscles, exercises, sets, reps or weight between workouts promotes strength gains. Sets reps & weight recommendations are based on a non-linear periodization resistance techniques, which alters the intensity/volume relationships for every workout. Prilepin’s formula is used to calculate a recommended volume and intensity for any exercise.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAQUEL ALVAREZ whose telephone number is (571)272-6715. The examiner can normally be reached Mondays thru Thursdays 8:30-6:30.
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/RAQUEL ALVAREZ/Primary Examiner, Art Unit 3622
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