DETAILED ACTION
This action is in reference to the communication filed on 7 JAN 2025.
Claims 1-20 are present and have been examined.
Claim Objections
Claim 14 recites the word “on” twice. Examiner assumes this is a typographical error and the intent is for a
single “on.” Correction is required.
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 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) 1-20 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) 1-20 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, based at least in part on the exercise information, a missed exercise activity;
presenting, based at least in part on the missed exercise activity
receiving a response to the query; and
applying a recommendation model to the response to generate an exercise recommendation.
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. Examiner does not find any additional elements for consideration at this step with regard to claim 14 in particular. The chatbot, as well as the processor/memories 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. Examiner finds no improvement to the functioning of the computer 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 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. Examiner does not find any additional elements for consideration at this step with regard to claim 14 in particular.. When considered individually, the “chatbot” and the “processor/memories” 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.
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, 15-19:
Dependent claims 2, 15 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 § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 3-14, 16-20 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Lyke et al (US 20210201691 A1, hereinafter Lyke).
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).
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 comprises an exercise activity type, an exercise activity day and time, a heartrate of the user, training plan information, 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 further teaches: applying the exercise chatbot to the response (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).”); and
presenting the user with a follow-up query requesting for additional information related to the response and the missed exercise activity (at least [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).”).
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 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);
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).
In reference to claim 16:
Lyke further teaches: wherein the health habit model is trained on trainer health habits of trainers (at least [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.”)
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).
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) 2, 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lyke 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:
Lyke 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.
Conclusion
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/KATHERINE . KOLOSOWSKI-GAGER/
Primary Examiner
Art Unit 3687
/KATHERINE KOLOSOWSKI-GAGER/Primary Examiner, Art Unit 3687