DETAILED ACTION
This action is in reference to the communication filed on 7 APRIL 2026.
Amendments to claims 1, 4, 13, 14, 20, the cancellation of claim 16, and addition of claim 21 entered and considered.
Claims 1-15, 17-21 are present and have been examined.
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 1-15, 17-21 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. As explained below, the claim(s) are directed to an abstract idea without significantly more.
Step One: Is the Claim directed to a process, machine, manufacture or composition of matter? YES
With respect to claim(s) 1-15, 17-21 the independent claim(s) 1, 14, 20 recite(s) methods and a system, each of which falls into a statutory category of invention.
Step 2A – Prong One: Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? YES
With respect to claim(s) 1-15, 17-21 the independent claim(s) (claims 1, 14, 20) is/are directed, in part, to:
A method for providing exercise recommendations, comprising:
receiving exercise information for a user ;
identifying, using a missed activity identifier comprising based at least in part on a pattern of the exercise information, a missed exercise activity;
presenting, based at least in part on the missed exercise activity comprising a query generator and an exercise information analyzer, the user with a query generated by the query generator based on the missed exercise activity and the exercise information, the query comprising a request for additional information related to the missed exercise activity;
receiving a first response to the query; and
applying a recommendation first response to generate an exercise recommendation;
presenting the user with a follow-up query generated by the exercise chatbot based on the first response; and
receiving a second response to the follow up query.
These claim elements are considered to be abstract ideas because they are directed to mental processes, which include concepts performed in the human mind such as observation, evaluation, judgment and opinion. Receiving information for a user, identifying based on that information, a missed event, presenting a query for additional information, and applying rules to generate a recommendation are all examples of such concepts.
These claim elements are arguably directed to certain methods of human activities, including managing personal behavior or interactions including following rules or instructions. The claimed limitations recite a set of instructions to ultimately present the recommended activity to the user.
Examiner further finds that the use of the recommendation model is arguably an example of a mathematical concept including relationships, formulas, equations, or calculations. If a claim limitation, under its broadest reasonable interpretation, covers concepts performed in the human mind, then it falls within the “mental processes” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers following rules or instructions, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships/concepts/formulas/equations, then it falls within the “mathematical concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A – Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? NO.
This judicial exception is not integrated into a practical application. In particular, the claim(s) recite(s) additional elements: Claims 1, 20 recite the use of a “chatbot,” while claim 20 recites “processors and memories” to store instructions and execute instructions. Claims 1, 14, 20 have been amended to recite the use of exercise machines to send the information via a network, as well as the use of an algorithm/model. Examiner does not find any additional elements for consideration at this step with regard to claim 14 in particular beyond those which have been recently amended. The chatbot, as well as the processor/memories, as well as the algorithms/models are recited at a high level of generality and as such amount to no more than adding the words “apply it” to the judicial exception, or mere instructions to implement the abstract idea on a computer, or merely uses the computer as a tool to perform the abstract idea (see MPEP 2106.05f), or generally links the use of the judicial exception to a particular technological field of use/computing environment (see MPEP 2106.05h). The chatbot is clearly only used to display/send the queries. The processor/memory are clearly only used as a tool to execute the limitations of claim 20. The exercise devices appear to only collect and send information. Examiner finds no improvement to the functioning of the computer, the exercise devices, or any other technology or technical field in the chatbot nor the processor/memory as claimed (see MPEP 2106.05a), nor any other application or use of the judicial exception in some meaningful way beyond a general like between the use of the judicial exception to a particular technological environment (see MPEP 2106.05e). Examiner further notes that the use of a memory to store information, and the general sending/receiving of information is generally found to be adding insignificant extra solution activity to the judicial exception(s) identified (see MPEP 2106.05g), as is any general sending and receiving of data.
Accordingly, this/these additional element(s) do(es) not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? NO.
The independent claim(s) is/are additionally directed to claim elements such as: Claims 1, 20 recite the use of a “chatbot,” while claim 20 recites “processors and memories” to store instructions and execute instructions. Claims 1, 14, 20 have been amended to recite the use of exercise machines to send the information via a network, as well as the use of an algorithm/model. Examiner does not find any additional elements for consideration at this step with regard to claim 14 in particular beyond the noted amendments above common to claims 1, 20. When considered individually, the “chatbot” and the “processor/memories” as well as the exercise devices and algorithm/modeling claim elements only contribute generic recitations of technical elements to the claims. It is readily apparent, for example, that the claim is not directed to any specific improvements of these elements. Examiner looks to Applicant’s specification in:
[0018] As used herein, a chatbot may be a foundation model or an ML model that is trained in natural language algorithms to provide queries to a user and receive responses to the queries. The chatbot may be trained in natural language algorithms that may simulate a human interaction. For example, the chatbot may be trained to generate and present a query using syntax, verbs, nouns, and other grammatical elements to provide information and request information as a human being would. The chatbot may be trained to collect information based on an input dataset, such as the exercise information discussed herein. In some embodiments, the chatbot analyzes the input dataset, identify initial patterns in the input dataset, and request additional information to complete the pattern analysis.
[0047] The exercise recommendation system 200 may further include an exercise chatbot 216. The exercise chatbot 216 may communicate with the user regarding exercise information. For example, the exercise chatbot 216 may present the user with queries. The user may respond to the queries to the exercise chatbot 216. As discussed herein, the exercise chatbot 216 may provide multiple queries to the user and the user may respond to the multiple queries. In this manner, the exercise chatbot 216 may hold a conversation or other back-and-forth engagement with the user.
[0071] The computer system 700 includes a processor 701. The processor 701 may be a general-purpose single or multi-chip microprocessor (e.g., an Advanced RISC (Reduced Instruction Set Computer) Machine (ARM)), a special purpose microprocessor (e.g., a digital signal processor (DSP)), a microcontroller, a programmable gate array, etc. The processor 701 may be referred to as a central processing unit (CPU). Although just a single processor 701 is shown in the computer system 700 of FIG. 7, in an alternative configuration, a combination of processors (e.g., an ARM and DSP) could be used.
[0072] The computer system 700 also includes memory 703 in electronic communication with the processor 701. The memory 703 may be any electronic component capable of storing electronic information. For example, the memory 703 may be embodied as random access memory (RAM), read-only memory (ROM), magnetic disk storage media, optical storage media, flash memory devices in RAM, on-board memory included with the processor, erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM) memory, registers, and so forth, including combinations thereof.
[086] The user devices may include any type of user device. For example, the user devices may include exercise devices, such as treadmills, elliptical machines, stationary bicycles, rowers, cable exercise devices, weight devices, any other exercise device, and combinations thereof.
These passages, as well as others, makes it clear that the invention is not directed to a technical improvement. The chatbot is disclosed in functional terms only – i.e. any means of communicating with the system is sufficient. Similarly, the processor/memory are categorically recited such that any device/machine capable of sending/receiving/processing/storing is suitable for use. When the claims are considered individually and as a whole, the additional elements noted above, appear to merely apply the abstract concept to a technical environment in a very general sense – i.e. a generic computer receives information from another generic computer, processes the information and then sends information back. The most significant elements of the claims, that is the elements that really outline the inventive elements of the claims, are set forth in the elements identified as an abstract idea. The fact that the generic computing devices are facilitating the abstract concept is not enough to confer statutory subject matter eligibility.
As per dependent claims 2-13, 15, 17-19, 21:
Dependent claims 2, 15, 21 do not recite any additional abstract ideas than those identified above with respect to claims 1, 14, 20, however they do recite the element of an LLM. In the interest of compact prosecution, Examiner notes that as claimed this is simply providing description as to the type of model used in claims 1, 14. Accordingly, these claim elements do not serve to confer subject matter eligibility to the claims since they are directed to abstract idea and/or not supportive of a finding of significantly more.
Dependent claims 3-12, 16-19 are not directed any additional abstract ideas and are also not directed to any additional non-abstract claim elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above – such as additional input provided either by the user or by the system regarding the workout habits, the types of information presented, and different applications of the model itself being used to provide the recommendations. While these descriptive elements may provide further helpful context for the claimed invention these elements do not serve to confer subject matter eligibility to the invention since their individual and combined significance is still not heavier than the abstract concepts at the core of the claimed invention.
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.
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
Claim(s) 1, 3-14, 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lyke et al (US 20210201691 A1, hereinafter Lyke) in view of Bissonnette et al (US 20220047921 A1, hereinafter Bissonnette).
In reference to claim 1:
Lyke teaches: A method for providing exercise recommendations, comprising:
receiving exercise information for a user (at least [059] “FIG. 2A illustrates one exemplary personalized workout recommendation and dynamic feedback user interface 200 based on workout history, readiness/recovery information, and/or personal fitness goals, consistent with the various principles described herein. “ at [055] “0055] In one embodiment, readiness and/or recovery metrics may be collected for an assessment period, obtained from historic tracking data, or inferred from existing schedule data. “ at [0084] “The user workout history database 308 stores a plurality of user data records and their corresponding workout data records. Each user data record may include detailed information with regard to e.g., accuracy of data, fitness goal definition, progression of performance, psychological parameters (e.g., behaviors, motivations, etc.), height, weight, age, sex, ethnicity, and/or any number of other user specific parameters. Each workout data record may include detailed information with regard to e.g., date/time of past exercises, scheduled date/time of future exercises, type and/or number of exercises, frequency of exercise, exerted muscle groups, duration of exertion, intensity of exertion, absolute load, relative load, range of movement, repetition, recovery time, fatigue, dynamic feedback/user response, frequency of revision, revision success/failure, and/or any number of other workout specific parameters. “).;
identifying, based at least in part on the exercise information, a missed exercise activity (at least [0061] “Additionally, the exemplary user interface 200 can provide dynamic feedback 210 in response to such user input and/or during the workout. In some cases, dynamic feedback may be provided in response to skipping an exercise (e.g., “Why did you skip squats?”).”);
presenting, based at least in part on the missed exercise activity and using an exercise chatbot, the user with a query, the query comprising a request for additional information related to the missed exercise activity (at least [0061] “Additionally, the exemplary user interface 200 can provide dynamic feedback 210 in response to such user input and/or during the workout. In some cases, dynamic feedback may be provided in response to skipping an exercise (e.g., “Why did you skip squats?”). In another example, as shown in FIG. 2A, a user that has failed to meet the expected performance may receive a notification (described in greater detail in FIGS. 2B-2C infra). Dynamic feedback may include a variety of different messages e.g.: interrogatory (e.g., “You have only completed 18 of 25 push-ups. How do you feel?), suggestions (e.g., “For this set, let's focus on form”), motivation (e.g., “You're doing great!”), information (e.g., “Push-ups in a wide stance or a narrow stance can activate different muscle groups”), status (“Only one more set to go”), etc.”);
receiving a response to the query (at least [0074] “ For example, the user may be prompted to answer questions regarding e.g., soreness, energy level, mood, and/or sleep.”); and
applying a recommendation model to the response to generate an exercise recommendation (at least [075-078] “Once the user decides to start their 1 PM workout, the workout 258 is dynamically modified to account for the skipped lunch event. In another such implementation, the fitness program may consider subjective reporting measures of readiness. For example, the user may be prompted to answer questions regarding e.g., soreness, energy level, mood, and/or sleep. Modifying the workout to account for reduced user readiness is more beneficial than e.g., skipping the workout or attempting an unrealistic workout (and risking injury).” See at least [0102-109] for discussion of machine learning techniques). Lyke as cited teaches all the limitations above, but does not specifically disclose exercise devices or their incorporation into a machine learning model. Bissonnette however does teach:
receiving exercise information for a user from one or more exercise devices via an exercise network, wherein the exercise information indicates exercise activities performed by the user using the one or more exercise devices and comprises training plan information, and wherein the exercise information indicates operating parameters of the one or more exercise devices associated with the exercise activities (at least [fig 1 and related text] “In some embodiments, the system architecture 10 may include a computing device 12 communicatively coupled to an exercise machine 100. The computing device 12 may also be communicatively coupled with a computing device 15 and a cloud-based computing system 16. As used herein, a cloud-based computing system refers, without limitation, to any remote or distal computing system accessed over a network link. Each of the computing device 12, computing device 15, and/or the exercise machine 100 may include one or more processing devices, memory devices, and network interface devices. In some embodiments, the computing device 12 may be included as part of the structure of the exercise machine 100…The application 21 may present a user interface 22 including various screens to a physician, trainer, or caregiver that enable the person to create an exercise plan for a user based on a treatment (e.g., surgery, medical procedure, etc.) the user underwent and/or injury (e.g., sprain, tear, fracture, etc.) the user suffered, view progress of the user throughout the exercise plan, and/or view measured properties (e.g., force exerted on portions of the exercise machine 100) of the user during exercises of the exercise plan. The exercise plan specific to a patient may be transmitted via the network 20 to the cloud-based computing system 16 for storage and/or to the computing device 12 so the patient may begin the exercise plan. The exercise plan may specifying one or more exercises that are available at the exercise machine 100. “; see [0130-0131] for more discussion of exercise device including additional parameters);
using a missed activity identifier comprising a machine learning model trained to identify patterns of exercise activities and a pattern of missed activity (at least [fig 26 and related text] “During performance of an exercise, the user may select the options for too easy or too hard on the user interface 2600 or say the words “too easy” or “too hard”. Such selection may cause the machine learning model 60 to alter the exercise, the exercise session, or both. For example, the cloud-based computing system 16 may increase or decrease the intensity of the exercise by modifying a parameter (e.g., resistance, speed, etc.) of the exercise machine 100. Further, if the options for too easy or too hard are chosen more than a threshold number of times, the machine learning model 60 may remove the exercise from the exercise session, add another exercise to the exercise session, switch to a different exercise, or the like.” At [268] “The second exercise 3602 may be selected as a result of the user indicating the previous exercise was too hard or too easy. In some embodiments, the second exercise 3602 may be selected by default by the machine learning model 60. After the baseline fitness test is performed by the user, the machine learning model 60 may determine the fitness level of the user, and use the fitness level of the user to select the exercises and/or their difficulty levels in the improved exercise plan.” At [0527-550, 0549 in particular] “ [0549] c) When A User Presses Button or Says (e.g., by voice) Skip [0550] i. The system may record the exercise was skipped, and may begin the exercise at the user's previous exercise level the next time the exercise is shown.” See also [0232, 0317 ;
[sending messages] using a query generator and an exercise information analyzer, the query generated by the query generator based on the missed exercise activity and the exercise information [including request for additional information related to the activity] (at least [0527-0549] the system generates messages based on a user skipping/finding the exercises “too hard”: “Each time: [0536] 1. the User will be alerted on the dashboard and by the virtual coach to the degree of difficulty increase in repetitions and sets, and [0537] 2. will have the option of pressing or saying (e.g., by voice) “Too Hard” on the new level, and the repetitions and sets may immediately revert to their prior values. [0538] iii. Users may perform the Baseline Exercise for 3 Exercise Sessions before the “Too Easy” button becomes available to use. [0539] b) When A User Presses Button or Says (e.g., by voice) Too Hard [0540] i. The key exercise dimension for non-cycling exercise drops: [0541] 1. the first time by 20% of the original amount (rounded down to the nearest whole number), then [0542] 2. the second time by 40% of the original amount, then [0543] 3. the third time by 80% of the original amount. [0544] ii. The key exercise dimension for cycling drops: [0545] 1. first by reducing the Resistance Level setting, then [0546] 2. by reducing the ROM setting. [0547] iii. If a user presses button or says (e.g., by voice) “Too Hard” 3 times on the same exercise in the same session, a push notification message may appear and ask if everything is alright and ask if they want to continue with the exercise. [0548] iv. If a user presses button or says (e.g., by voice) “Too Hard” a 4th time, the exercise will stop, and a push notification and notification may let the user know to stop this exercise for the session. [0549] c) When A User Presses Button or Says (e.g., by voice) Skip [0550] i. The system may record the exercise was skipped, and may begin the exercise at the user's previous exercise level the next time the exercise is shown.”)
Presenting a user with a follow up query generated by the [message system] based on the first response; and receiving a second response to the follow up query (see citations above, and at least [0547] “iii. If a user presses button or says (e.g., by voice) “Too Hard” 3 times on the same exercise in the same session, a push notification message may appear and ask if everything is alright and ask if they want to continue with the exercise. [0548] iv. If a user presses button or says (e.g., by voice) “Too Hard” a 4th time, the exercise will stop, and a push notification and notification may let the user know to stop this exercise for the session.”). Lyke and Bissonnette are analogous references as both disclose monitoring a user’s progress with a pre-determined workout. One of ordinary skill in the art would have found the inclusion of additional information regarding the skipped workout (the skipped workout being common to both references), to have been obvious to include as Bissonnette teaches a particular application of exercise monitoring to encourage recovery by monitoring progress remotely (see 0104), which is analogous with the motivational dialog provided in Lyke. Further, Lyke does teach progress monitoring entered by the user, and as such, additional monitoring from the device itself would have been an obvious addition to ensure compliance with the workout plan(s) provided.
In reference to claim 3:
Lyke further teaches: wherein the exercise information comprises historical exercise information of the user (at least [0084] “The user workout history database 308 stores a plurality of user data records and their corresponding workout data records. Each user data record may include detailed information with regard to e.g., accuracy of data, fitness goal definition, progression of performance, psychological parameters (e.g., behaviors, motivations, etc.), height, weight, age, sex, ethnicity, and/or any number of other user specific parameters. Each workout data record may include detailed information with regard to e.g., date/time of past exercises, scheduled date/time of future exercises, type and/or number of exercises, frequency of exercise, exerted muscle groups, duration of exertion, intensity of exertion, absolute load, relative load, range of movement, repetition, recovery time, fatigue, dynamic feedback/user response, frequency of revision, revision success/failure, and/or any number of other workout specific parameters.”).
In reference to claim 4:
Lyke further teaches: wherein the exercise information further comprises an exercise activity type, an exercise activity day and time, a heartrate of the user, or any combination thereof (at least [059] “FIG. 2A illustrates one exemplary personalized workout recommendation and dynamic feedback user interface 200 based on workout history, readiness/recovery information, and/or personal fitness goals, consistent with the various principles described herein. “ at [055] “0055] In one embodiment, readiness and/or recovery metrics may be collected for an assessment period, obtained from historic tracking data, or inferred from existing schedule data. “ at [0084] “The user workout history database 308 stores a plurality of user data records and their corresponding workout data records. Each user data record may include detailed information with regard to e.g., accuracy of data, fitness goal definition, progression of performance, psychological parameters (e.g., behaviors, motivations, etc.), height, weight, age, sex, ethnicity, and/or any number of other user specific parameters. Each workout data record may include detailed information with regard to e.g., date/time of past exercises, scheduled date/time of future exercises, type and/or number of exercises, frequency of exercise, exerted muscle groups, duration of exertion, intensity of exertion, absolute load, relative load, range of movement, repetition, recovery time, fatigue, dynamic feedback/user response, frequency of revision, revision success/failure, and/or any number of other workout specific parameters. “).
In reference to claim 5:
Lyke further teaches: wherein applying the recommendation model comprises: comparing the missed exercise activity with a previous missed exercise activity (at least [0102]) “Referring back to step 402, the workout data and/or readiness/recovery information analysis can be performed via machine learning and/or other artificial intelligence (AI) techniques. Specifically, machine learning and/or artificial intelligence (AI) can be used to filter populations of users into subsets having similar characteristics. Each subset is associated with appropriate performance metrics based on e.g., workout and/or readiness/recovery history, interpolation/extrapolation from workout data, interpolation/extrapolation based on related muscle group exercises and workouts (e.g., pull-ups may enable some extrapolation to similar muscle exercises e.g., bicep curls, etc.) and/or user input/responses. In other implementations, performance metrics can be assigned based on expert human analysis or even explicit user responses/input (e.g., similar circumstances where users report similarly, etc.).” – i.e. grouping users who also missed/skipped and have trouble with motivation/consistency, i.e. at [0131] “ For example, a user that consistently falls below their personal health and fitness goals may receive encouragement and/or feedback to re-evaluate their goals. In some cases, user workout, sleep, and/or consumable item data records may be matched against expected goals to ensure that adequate progress is being made. Logging is prone to error and/or misreporting; e.g., some users may consistently under/overreport their workout, sleep, and/or nutritional regimen. “).
In reference to claim 6:
Lyke further teaches: wherein applying the recommendation model further comprises: identifying common behaviors between the missed exercise activity and the previous missed exercise activity (at least [0102]) “Referring back to step 402, the workout data and/or readiness/recovery information analysis can be performed via machine learning and/or other artificial intelligence (AI) techniques. Specifically, machine learning and/or artificial intelligence (AI) can be used to filter populations of users into subsets having similar characteristics. Each subset is associated with appropriate performance metrics based on e.g., workout and/or readiness/recovery history, interpolation/extrapolation from workout data, interpolation/extrapolation based on related muscle group exercises and workouts (e.g., pull-ups may enable some extrapolation to similar muscle exercises e.g., bicep curls, etc.) and/or user input/responses. In other implementations, performance metrics can be assigned based on expert human analysis or even explicit user responses/input (e.g., similar circumstances where users report similarly, etc.).” – i.e. grouping users who also missed/skipped and have trouble with motivation/consistency, i.e. at [0131] “ For example, a user that consistently falls below their personal health and fitness goals may receive encouragement and/or feedback to re-evaluate their goals. In some cases, user workout, sleep, and/or consumable item data records may be matched against expected goals to ensure that adequate progress is being made. Logging is prone to error and/or misreporting; e.g., some users may consistently under/overreport their workout, sleep, and/or nutritional regimen. “ at [057] “ workout history is collected from a large population of users for an exercise (e.g., sit-ups, push-ups, squats, etc.) The workout histories are analyzed to generate a set of expected profiles. “).
In reference to claim 7:
Lyke further teaches: wherein the exercise recommendation comprises a change in time of day for exercise, a change in exercise activity type, a change in exercise activity duration, a change in exercise activity intensity, a change in trainer, a change in exercise activity location, or any combination thereof (at least [0147] “As but one example, the heuristic may specify a number of repetitions; the client device counts repetitions, if the user cannot meet (or exceeds) the specified number of repetitions, then the client device provides dynamic feedback and/or modification to the recommended workout (e.g., by increasing or reducing the next set's repetitions). In another such example, the heuristic may specify an expected time of completion (e.g., each repetition/set should be completed within a time window), failure to remain within the time window may be indicative of fatigue or incorrect form. Still other examples may monitor a user's psychological state; for example, a user that is tired early may be allowed to cut their workout short to prevent injury, or urged to complete the entire workout, depending on the expected profile's psychometric rules.” At [0154] “] The user's subjective input may be used to improve future workout recommendations. For example, a person that routinely underperforms due to lack of readiness or failure to maximize recovery can be shifted to a slower progression track. In another such example, user input can be used to shift workout scheduling e.g., to ensure that the user is adequately rested and/or fed before and after workouts.”)
In reference to claim 8:
Lyke further teaches: wherein presenting the user with the query comprises: presenting the user with the query without user input by the user (at least [072] “In some such variants, the fitness program may automatically populate meal, rest, and/or workout data records with recommended values that the user either confirms, rejects, and/or modifies. For instance, a sleep record may be populated with the recommended 8 hours of sleep from 11 PM to 7 AM; the user may accept the default sleep record or update the sleep record with their best estimate.”)
In reference to claim 9:
Lyke further teaches: wherein presenting the user with the query comprises: presenting the user with the query at a pre-scheduled time (at least [0073] “ For example, the user's calendaring program may include a variety of personal and/or professional appointments as well as reminders or links for health and fitness activities (e.g., suggested meals/snacks, rest reminders, suggested workouts, etc.) In some cases, the calendaring application may directly launch the corresponding tracking application. “ at [0162] “Other common examples of client-side considerations may include without limitation: available time and/or resources, other scheduled tasks, subjective user input, convenience, business considerations, etc. For example, a user may receive an eight-hour sleep action item; the sleep item may be flexibly scheduled based on the user's existing schedule (e.g., a bedtime reminder is calculated based on a client-side alarm setting). “ at [093] “For example, workout data records that have been logged (or scheduled) at a particular user device 302 may be stored locally until e.g., synchronized with the network (or vice versa). Additionally, or in the alternative, expected profiles (in whole or in part) may be stored at the analytics engine 306 and portions may be made accessible to particular devices 302 when queried and/or locally cached. Any combination of the foregoing configurations may be utilized with equal success.”)
In reference to claim 10:
Lyke further teaches: wherein the pre-scheduled time is based at least in part on a trigger event (at least at [0162] “Other common examples of client-side considerations may include without limitation: available time and/or resources, other scheduled tasks, subjective user input, convenience, business considerations, etc. For example, a user may receive an eight-hour sleep action item; the sleep item may be flexibly scheduled based on the user's existing schedule (e.g., a bedtime reminder is calculated based on a client-side alarm setting). “)
In reference to claim 11:
Lyke further teaches: wherein the additional information comprises additional exercise information not included in the exercise information (at least [0061] “Additionally, the exemplary user interface 200 can provide dynamic feedback 210 in response to such user input and/or during the workout. In some cases, dynamic feedback may be provided in response to skipping an exercise (e.g., “Why did you skip squats?”). In another example, as shown in FIG. 2A, a user that has failed to meet the expected performance may receive a notification (described in greater detail in FIGS. 2B-2C infra). Dynamic feedback may include a variety of different messages e.g.: interrogatory (e.g., “You have only completed 18 of 25 push-ups. How do you feel?), suggestions (e.g., “For this set, let's focus on form”), motivation (e.g., “You're doing great!”), information (e.g., “Push-ups in a wide stance or a narrow stance can activate different muscle groups”), status (“Only one more set to go”), etc.” – i.e. the answers to these questions would be information that a user provides upon prompting rather than the detection of the missed/skipped workout).
In reference to claim 12:
Lyke further teaches: wherein the query comprises additional content (at least [0061] “Additionally, the exemplary user interface 200 can provide dynamic feedback 210 in response to such user input and/or during the workout. In some cases, dynamic feedback may be provided in response to skipping an exercise (e.g., “Why did you skip squats?”). In another example, as shown in FIG. 2A, a user that has failed to meet the expected performance may receive a notification (described in greater detail in FIGS. 2B-2C infra). Dynamic feedback may include a variety of different messages e.g.: interrogatory (e.g., “You have only completed 18 of 25 push-ups. How do you feel?), suggestions (e.g., “For this set, let's focus on form”), motivation (e.g., “You're doing great!”), information (e.g., “Push-ups in a wide stance or a narrow stance can activate different muscle groups”), status (“Only one more set to go”), etc.” – additional content provided to the user in the form of the suggestions and/or the interrogatory remarks).
In reference to claim 13:
Lyke/Bissonnette teaches all the limitations above. Lyke teaches further teaches: applying the exercise chatbot to the response, wherein a follow up query requests additional information related to the response and the missed exercise activity (at least [fig 2A and related text] “For example, the illustrated interface 200 recommends a set of exercises (e.g., “Sit-ups” 202A, “Push-ups” 202B, “Squats” 202C, etc.) and logs actual performance (204A, 204B, 204C) against an expected goal performance for the expected profile (206A, 206B, 206C). The user interface 200 may further include status e.g., a set count and/or motivational messaging (“New Personal Record!” 208).”; at [fig 2b and related text] “Referring now to FIG. 2B, one exemplary implementation of a dynamic feedback interface 220 is provided in greater detail. As shown therein, a user has failed to meet the expected performance. In the illustrated embodiment, the user device prompts the user to provide user input 210 to explain why. For example, a user may select a subjective emotional state (happy, indifferent, afraid of injury).”). Lyke as cited does not specifically teach a relationship between a first response as claimed, however, Bissonnette does teach the follow up query applied to the first response for additional information related to the first response (see citations above for claim 1, and at least [0547] “iii. If a user presses button or says (e.g., by voice) “Too Hard” 3 times on the same exercise in the same session, a push notification message may appear and ask if everything is alright and ask if they want to continue with the exercise. [0548] iv. If a user presses button or says (e.g., by voice) “Too Hard” a 4th time, the exercise will stop, and a push notification and notification may let the user know to stop this exercise for the session.”). Lyke and Bissonnette are analogous references as both disclose monitoring a user’s progress with a pre-determined workout. One of ordinary skill in the art would have found the follow up questions pertaining to the response to be an obvious natural flow of conversation regarding the user’s workouts.
In reference to claim 14:
Lyke teaches: A method for exercise recommendations, comprising:
receiving exercise information for a user (at least [059] “FIG. 2A illustrates one exemplary personalized workout recommendation and dynamic feedback user interface 200 based on workout history, readiness/recovery information, and/or personal fitness goals, consistent with the various principles described herein. “ at [055] “0055] In one embodiment, readiness and/or recovery metrics may be collected for an assessment period, obtained from historic tracking data, or inferred from existing schedule data. “ at [0084] “The user workout history database 308 stores a plurality of user data records and their corresponding workout data records. Each user data record may include detailed information with regard to e.g., accuracy of data, fitness goal definition, progression of performance, psychological parameters (e.g., behaviors, motivations, etc.), height, weight, age, sex, ethnicity, and/or any number of other user specific parameters. Each workout data record may include detailed information with regard to e.g., date/time of past exercises, scheduled date/time of future exercises, type and/or number of exercises, frequency of exercise, exerted muscle groups, duration of exertion, intensity of exertion, absolute load, relative load, range of movement, repetition, recovery time, fatigue, dynamic feedback/user response, frequency of revision, revision success/failure, and/or any number of other workout specific parameters. “);
providing, based at least in part [on] on the exercise information, the user with a query, the query requesting health information from the user (at least [0061] “Additionally, the exemplary user interface 200 can provide dynamic feedback 210 in response to such user input and/or during the workout. In some cases, dynamic feedback may be provided in response to skipping an exercise (e.g., “Why did you skip squats?”). In another example, as shown in FIG. 2A, a user that has failed to meet the expected performance may receive a notification (described in greater detail in FIGS. 2B-2C infra). Dynamic feedback may include a variety of different messages e.g.: interrogatory (e.g., “You have only completed 18 of 25 push-ups. How do you feel?), suggestions (e.g., “For this set, let's focus on form”), motivation (e.g., “You're doing great!”), information (e.g., “Push-ups in a wide stance or a narrow stance can activate different muscle groups”), status (“Only one more set to go”), etc.”);
applying a health habit model to the health information, the health habit model comprising a machine learning model trained on trainer health habits of one or more trainers, the health habit model identifying a health habit for the user (at least [075-078] “Once the user decides to start their 1 PM workout, the workout 258 is dynamically modified to account for the skipped lunch event. In another such implementation, the fitness program may consider subjective reporting measures of readiness. For example, the user may be prompted to answer questions regarding e.g., soreness, energy level, mood, and/or sleep. Modifying the workout to account for reduced user readiness is more beneficial than e.g., skipping the workout or attempting an unrealistic workout (and risking injury).” See at least [0102-109] for discussion of machine learning techniques; at [0100] “In another embodiment, workout data and/or readiness/recovery information for specific individuals is analyzed to identify physiological and/or psychological traits for that individual. For example, celebrities and/or athletes may have their own workout data and/or readiness/recovery information analyzed. The celebrity and/or athlete may then publish their workout data and/or readiness/recovery information such that other individuals can aspire to holistically steer their workouts and/or readiness/recovery habits to match the celebrity/athlete. More directly, even though a professional athlete may focus on strength training for specific muscle groups, an amateur may actually need to improve many different muscle groups (and/or when and how they eat/sleep) to achieve similar results. In other words, the celebrity profile can be used to dynamically coach a person toward an aspirational level of fitness of another person.”);
generating, using the health habit model, a recommendation to improve the health habit ( at least [0054] “For example, a user that has never logged any workouts may be instructed to first establish a regular workout regimen before attempting assessment (e.g., “get in the habit of walking every day” before “run a timed mile. “); and
presenting the recommendation to the user (at least [054, 0120-25] recommendations presented to user based on collected data);
receiving in response to presenting the recommendation, updated exercise information for the user (at least [054] “ In some cases, a user may be required to meet a minimum threshold of physiological and/or psychological performance for the assessment to be accurate. For example, a user that has never logged any workouts may be instructed to first establish a regular workout regimen before attempting assessment (e.g., “get in the habit of walking every day” before “run a timed mile.”); and
applying the health habit model to the updated exercise information to identify whether the user has implemented the recommendation (at least [0163-0164] “ some embodiments, actionable items may include subsequent reporting back to the health tracking system. Subsequent reporting may include data regarding the user's subsequent activity (e.g., completion, percent adherence, skipped, etc.) For example, a user that is prescribed a post-workout snack and rest may be assessed for adherence to the suggestions. In some cases, adherence data can be useful for updating the user's profile.” Lyke as cited teaches all the limitations above, but does not specifically teach integration with exercise devices. Bissonnette however does teach: receiving exercise information for a user from one or more exercise devices via an exercise network, wherein the exercise information indicates exercise activities performed by the user using the one or more exercise devices and comprises training plan information, and wherein the exercise information indicates operating parameters of the one or more exercise devices associated with the exercise activities (at least [fig 1 and related text] “In some embodiments, the system architecture 10 may include a computing device 12 communicatively coupled to an exercise machine 100. The computing device 12 may also be communicatively coupled with a computing device 15 and a cloud-based computing system 16. As used herein, a cloud-based computing system refers, without limitation, to any remote or distal computing system accessed over a network link. Each of the computing device 12, computing device 15, and/or the exercise machine 100 may include one or more processing devices, memory devices, and network interface devices. In some embodiments, the computing device 12 may be included as part of the structure of the exercise machine 100…The application 21 may present a user interface 22 including various screens to a physician, trainer, or caregiver that enable the person to create an exercise plan for a user based on a treatment (e.g., surgery, medical procedure, etc.) the user underwent and/or injury (e.g., sprain, tear, fracture, etc.) the user suffered, view progress of the user throughout the exercise plan, and/or view measured properties (e.g., force exerted on portions of the exercise machine 100) of the user during exercises of the exercise plan. The exercise plan specific to a patient may be transmitted via the network 20 to the cloud-based computing system 16 for storage and/or to the computing device 12 so the patient may begin the exercise plan. The exercise plan may specifying one or more exercises that are available at the exercise machine 100. “; see [0130-0131] for more discussion of exercise device including additional parameters);
In reference to claim 17:
Lyke further teaches: wherein the recommendation comprises environmental information, habit stacking, a reward cycle, or any combination thereof ( at least [0054] “For example, a user that has never logged any workouts may be instructed to first establish a regular workout regimen before attempting assessment (e.g., “get in the habit of walking every day” before “run a timed mile.”) “)
In reference to claim 18:
Lyke further teaches: wherein the recommendation comprises educational material (at least [060] “In some cases, the recommended workout may also provide the user with instructional information (e.g., exercised muscle group, proper form, why the exercise is important, etc.)” at [0165] “In some cases, similarly situated and motivated users may have greater success by e.g., focusing on physiological goals (e.g., working on one muscle group before another, focusing on flexibility, etc.) and/or psychological goals (e.g., establishing a regular workout discipline, pushing through discomfort, etc.) In other words, steering users to improve and transition to new expected profiles may be more efficient to achieve performance progression than others.”).
In reference to claim 19:
Lyke further teaches: wherein the educational material comprises process-oriented education, consistency education, or both (at least [060] “In some cases, the recommended workout may also provide the user with instructional information (e.g., exercised muscle group, proper form, why the exercise is important, etc.)” at [0165] “In some cases, similarly situated and motivated users may have greater success by e.g., focusing on physiological goals (e.g., working on one muscle group before another, focusing on flexibility, etc.) and/or psychological goals (e.g., establishing a regular workout discipline, pushing through discomfort, etc.) In other words, steering users to improve and transition to new expected profiles may be more efficient to achieve performance progression than others.”).
In reference to claim 20:
Lyke teaches: An exercise recommendation system, comprising:
one or more processors and one or more memories, the one or more memories comprising instructions that, when executed by the one or more processors (at least [figs 5a/5b and related text]), cause the exercise recommendation system to:
receive exercise information for a user (at least [059] “FIG. 2A illustrates one exemplary personalized workout recommendation and dynamic feedback user interface 200 based on workout history, readiness/recovery information, and/or personal fitness goals, consistent with the various principles described herein. “ at [055] “0055] In one embodiment, readiness and/or recovery metrics may be collected for an assessment period, obtained from historic tracking data, or inferred from existing schedule data. “ at [0084] “The user workout history database 308 stores a plurality of user data records and their corresponding workout data records. Each user data record may include detailed information with regard to e.g., accuracy of data, fitness goal definition, progression of performance, psychological parameters (e.g., behaviors, motivations, etc.), height, weight, age, sex, ethnicity, and/or any number of other user specific parameters. Each workout data record may include detailed information with regard to e.g., date/time of past exercises, scheduled date/time of future exercises, type and/or number of exercises, frequency of exercise, exerted muscle groups, duration of exertion, intensity of exertion, absolute load, relative load, range of movement, repetition, recovery time, fatigue, dynamic feedback/user response, frequency of revision, revision success/failure, and/or any number of other workout specific parameters.“);
identify, based at least in part on the exercise information, a missed exercise activity (at least [0061] “Additionally, the exemplary user interface 200 can provide dynamic feedback 210 in response to such user input and/or during the workout. In some cases, dynamic feedback may be provided in response to skipping an exercise (e.g., “Why did you skip squats?”).”);
present, based at least in part on the missed exercise activity and using an exercise chatbot, the user with a query, the query comprising a request for additional information related to the missed exercise activity (at least [0061] “Additionally, the exemplary user interface 200 can provide dynamic feedback 210 in response to such user input and/or during the workout. In some cases, dynamic feedback may be provided in response to skipping an exercise (e.g., “Why did you skip squats?”). In another example, as shown in FIG. 2A, a user that has failed to meet the expected performance may receive a notification (described in greater detail in FIGS. 2B-2C infra). Dynamic feedback may include a variety of different messages e.g.: interrogatory (e.g., “You have only completed 18 of 25 push-ups. How do you feel?), suggestions (e.g., “For this set, let's focus on form”), motivation (e.g., “You're doing great!”), information (e.g., “Push-ups in a wide stance or a narrow stance can activate different muscle groups”), status (“Only one more set to go”), etc.”);;
receive a response to the query (at least [0074] “ For example, the user may be prompted to answer questions regarding e.g., soreness, energy level, mood, and/or sleep.”); and
apply a recommendation model to the response to generate an exercise recommendation (at least [075-078] “Once the user decides to start their 1 PM workout, the workout 258 is dynamically modified to account for the skipped lunch event. In another such implementation, the fitness program may consider subjective reporting measures of readiness. For example, the user may be prompted to answer questions regarding e.g., soreness, energy level, mood, and/or sleep. Modifying the workout to account for reduced user readiness is more beneficial than e.g., skipping the workout or attempting an unrealistic workout (and risking injury).” See at least [0102-109] for discussion of machine learning techniques). ). Lyke as cited teaches all the limitations above, but does not specifically disclose exercise devices or their incorporation into a machine learning model. Bissonnette however does teach:
receiving exercise information for a user from one or more exercise devices via an exercise network, wherein the exercise information indicates exercise activities performed by the user using the one or more exercise devices and comprises training plan information, and wherein the exercise information indicates operating parameters of the one or more exercise devices associated with the exercise activities (at least [fig 1 and related text] “In some embodiments, the system architecture 10 may include a computing device 12 communicatively coupled to an exercise machine 100. The computing device 12 may also be communicatively coupled with a computing device 15 and a cloud-based computing system 16. As used herein, a cloud-based computing system refers, without limitation, to any remote or distal computing system accessed over a network link. Each of the computing device 12, computing device 15, and/or the exercise machine 100 may include one or more processing devices, memory devices, and network interface devices. In some embodiments, the computing device 12 may be included as part of the structure of the exercise machine 100…The application 21 may present a user interface 22 including various screens to a physician, trainer, or caregiver that enable the person to create an exercise plan for a user based on a treatment (e.g., surgery, medical procedure, etc.) the user underwent and/or injury (e.g., sprain, tear, fracture, etc.) the user suffered, view progress of the user throughout the exercise plan, and/or view measured properties (e.g., force exerted on portions of the exercise machine 100) of the user during exercises of the exercise plan. The exercise plan specific to a patient may be transmitted via the network 20 to the cloud-based computing system 16 for storage and/or to the computing device 12 so the patient may begin the exercise plan. The exercise plan may specifying one or more exercises that are available at the exercise machine 100. “; see [0130-0131] for more discussion of exercise device including additional parameters);
using a missed activity identifier comprising a machine learning model trained to identify patterns of exercise activities and a pattern of missed activity (at least [fig 26 and related text] “During performance of an exercise, the user may select the options for too easy or too hard on the user interface 2600 or say the words “too easy” or “too hard”. Such selection may cause the machine learning model 60 to alter the exercise, the exercise session, or both. For example, the cloud-based computing system 16 may increase or decrease the intensity of the exercise by modifying a parameter (e.g., resistance, speed, etc.) of the exercise machine 100. Further, if the options for too easy or too hard are chosen more than a threshold number of times, the machine learning model 60 may remove the exercise from the exercise session, add another exercise to the exercise session, switch to a different exercise, or the like.” At [268] “The second exercise 3602 may be selected as a result of the user indicating the previous exercise was too hard or too easy. In some embodiments, the second exercise 3602 may be selected by default by the machine learning model 60. After the baseline fitness test is performed by the user, the machine learning model 60 may determine the fitness level of the user, and use the fitness level of the user to select the exercises and/or their difficulty levels in the improved exercise plan.” At [0527-550, 0549 in particular] “ [0549] c) When A User Presses Button or Says (e.g., by voice) Skip [0550] i. The system may record the exercise was skipped, and may begin the exercise at the user's previous exercise level the next time the exercise is shown.” See also [0232, 0317 ;
[sending messages] using a query generator and an exercise information analyzer, the query generated by the query generator based on the missed exercise activity and the exercise information [including request for additional information related to the activity] (at least [0527-0549] the system generates messages based on a user skipping/finding the exercises “too hard”: “Each time: [0536] 1. the User will be alerted on the dashboard and by the virtual coach to the degree of difficulty increase in repetitions and sets, and [0537] 2. will have the option of pressing or saying (e.g., by voice) “Too Hard” on the new level, and the repetitions and sets may immediately revert to their prior values. [0538] iii. Users may perform the Baseline Exercise for 3 Exercise Sessions before the “Too Easy” button becomes available to use. [0539] b) When A User Presses Button or Says (e.g., by voice) Too Hard [0540] i. The key exercise dimension for non-cycling exercise drops: [0541] 1. the first time by 20% of the original amount (rounded down to the nearest whole number), then [0542] 2. the second time by 40% of the original amount, then [0543] 3. the third time by 80% of the original amount. [0544] ii. The key exercise dimension for cycling drops: [0545] 1. first by reducing the Resistance Level setting, then [0546] 2. by reducing the ROM setting. [0547] iii. If a user presses button or says (e.g., by voice) “Too Hard” 3 times on the same exercise in the same session, a push notification message may appear and ask if everything is alright and ask if they want to continue with the exercise. [0548] iv. If a user presses button or says (e.g., by voice) “Too Hard” a 4th time, the exercise will stop, and a push notification and notification may let the user know to stop this exercise for the session. [0549] c) When A User Presses Button or Says (e.g., by voice) Skip [0550] i. The system may record the exercise was skipped, and may begin the exercise at the user's previous exercise level the next time the exercise is shown.”)
Presenting a user with a follow up query generated by the [message system] based on the first response; and receiving a second response to the follow up query (see citations above, and at least [0547] “iii. If a user presses button or says (e.g., by voice) “Too Hard” 3 times on the same exercise in the same session, a push notification message may appear and ask if everything is alright and ask if they want to continue with the exercise. [0548] iv. If a user presses button or says (e.g., by voice) “Too Hard” a 4th time, the exercise will stop, and a push notification and notification may let the user know to stop this exercise for the session.”). Lyke and Bissonnette are analogous references as both disclose monitoring a user’s progress with a pre-determined workout. One of ordinary skill in the art would have found the inclusion of additional information regarding the skipped workout (the skipped workout being common to both references), to have been obvious to include as Bissonnette teaches a particular application of exercise monitoring to encourage recovery by monitoring progress remotely (see 0104), which is analogous with the motivational dialog provided in Lyke. Further, Lyke does teach progress monitoring entered by the user, and as such, additional monitoring from the device itself would have been an obvious addition to ensure compliance with the workout plan(s) provided.
Claim(s) 2, 15, 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lyke in view of Bissonnette, further in view of Shin et al (PlanFitting: Personalized Exercise Planning with Large Language Model-driven Conversational Agent, 2023, hereinafter Shin).
In reference to claim 2, 15, 21:
Lyke/Bissonnette teaches all the limitations above, as well as teaching the use of a communication system and a modeling process, but does not specifically teach the use of an LLL. Shin however does teach: wherein the exercise chatbot comprises a large language model (LLM).(Section 4: “Building on these insights, we designed and implemented PlanFitting, a conversational agent system aimed to help individuals set up their personalized exercise plan and iterate on it. Focusing on the expressivity and comprehensibility that LLMs offer, we designed our system using LLMs to foster engaging interaction, while adapting to the unique constraints of users and allowing them to iterate their plans”). Shin and Lyke are analogous as both deal with modeling fitness recommendations for users and providing data based insights. One of ordinary skill in the art would have found the inclusion of an LLM as taught by Shin to be obvious to include with the chat bot in Lyke, as Shin teaches particular applicability of the LLM to this environment: “One potential solution to tackle these challenges is to use LLM driven conversational agents (CAs) to tailor exercise plans to individuals. With the availability and scalability that CAs offer, we posit that CAs driven by LLMs can guide users to create and continuously refine plans tailored to their individual contexts.” As such, one would have found it obvious to include the LLM of Shin to continuously improve the suggestions or recommendations as common to both Shin and Lyke.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
2021/0316185, to McKenna discloses a sequential process of interacting with a user about the workouts performed and/or skipped.
US 20150364057, to Catani, discloses recommendations to a user based on collected fitness and health data.
Response to Arguments
Applicant’s remarks as filed on 7 APRIL 2026 have been fully considered.
Applicant’s remarks regarding the rejection under 35 USC 101 begin on page 7 of the remarks. Applicant’s remarks regarding the mental process category of abstract ideas are not found persuasive, in that Applicant references elements of the claims that are better addressed in subsequent steps of the analysis as noted above. Th elements Applicant refers to in support of this position are generally found to be analogous to adding the words “apply it” to the abstract idea, and/or using the computing device(s) as a tool to execute.
Applicant’s remarks regarding the certain methods of organizing human activity are also not found persuasive, for the reasons outlined above. The elements pertaining to automation or technical elements are more appropriate addressed in the additional elements steps (i.e. practical application/significantly more) of the analysis.
Applicant’s remarks regarding the amended claims as a whole, on page 10, are not found to be persuasive as they repeat the claimed limitation(s) identified above, and provides conclusions that appear to be more suited to the subsequent steps of the analysis.
Applicant turns to prong 2 of the analysis on page 10/11. Applicant’s remarks regarding a specific technical improvement, on page 11, are not found to be persuasive. Specifically, Examiner does not find the problem being solved – addressing user needs – to be technical in nature. While the solution itself is arguably technical, the problem being solved is not. Considering someone’s lifestyle, attitude, motivational style, etc. are important in making a response to a user’s missed workout, however, are in no way technical. The technical/additional elements of the claims – i.e. an algorithm or the computer itself, are in no way being improved. Applicant’s own language in the remarks suggests the reliance on the “trained ML model” to apply the abstract idea. The algorithm itself is not improved by the parameter changes. Similarly, a query being generated is again, not an improvement in the technical sense nor is the query generation itself improved. Examiner finds this line of reasoning unpersuasive. Similarly, the remarks regarding the chatbot itself are also found unpersuasive – the chatbot itself is in no way improved, merely applied to the abstract idea or used to apply it. Generating a follow up question is itself an abstract idea, and the chatbot/technical elements are relied upon to execute.
With regard to the health habit model in claim 14, Examiner again finds that the technical elements are not improved nor limited in a manner to support a practical application. The algorithm itself is not improved, rather applied to the abstract idea(s). Put another way, the claim requires the model itself be trained to execute a given result.
Applicant’s remarks regarding the prior art rejection begin on page 13 of the remarks. Applicant’s remarks appear largely directed to the newly amended limitations to claims 1, 14, 20, including the exercise device element(s) as added. These remarks are moot in view of the citations to newly added Bissonnette, which teaches collecting information from the actual exercise devices themselves and using said information to determine a missed or skipped workout. Bissonnette also teaches parameters of these devices such as limits and/or sizing.
Applicant’s remarks regarding the remaining dependent claims are found moot in view of the discussion of claims 1, 14, 20 above, from which they all depend.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KATHERINE KOLOSOWSKI-GAGER whose telephone number is (571)270-5920. The examiner can normally be reached Monday - Friday.
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/KATHERINE . KOLOSOWSKI-GAGER/
Primary Examiner
Art Unit 3687
/KATHERINE KOLOSOWSKI-GAGER/Primary Examiner, Art Unit 3687
