Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claim(s)
Claims 1, 3-14, 28, 53, 59, 61, 90-92 have been examined. Claims 1, 3,6, 10,14, 28, 90-91 have been amended. Claims 2, 15-27, 29-52, 54-58, 60, 62-89 have been previously canceled.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 14, 28 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites “wherein an archetypal pattern defines the states of the multi-feel state longitudinal journey and timing of the states over the duration “. However, the disclosure does not provide adequate structure to perform claim function of the states of the multi-feel state longitudinal journey and timing of the states over the duration (The Examiner notes that the term “archetypal” is not structure). The specification does not demonstrate that applicant has made an invention that achieves the claim function because the invention is not described with sufficient detail such that one of ordinary skill in the art can conclude that the invention had possession 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.
Claim(s) 1, 3-14, 28, 53, 59, 61, 90-92 is/are rejected under 35 U.S.C. 103 as being unpatentable over Margolin et al. (US20190272466A1 hereinafter Margolin) in view of Mohammed et al. (US20210050089A1 hereinafter Mohammed) and further in view of Kokoszka et al. (US20220240843A1 hereinafter Kokoszka).
With respect to claim 1, Margolin teaches a non-transitory computer readable medium with instructions stored thereon, that when executed by a hardware processor cause the processor to:
receive input data identifying qualities associated with an individualized wellness pattern aware intervention (‘466; Para 0025: Margolin describes the intervention signals are determined by algorithmic signal processing and/or machine learning methods such that the intervention signals are responsive, interactive, and adaptive to the users. For example, multimodal data streams can be obtained on a variety of metrics, such as physiological arousal, speech, vocal pitch and tone, GPS location, etc. These data are inputted machine learning algorithms (e.g., neural networks, support vector machines) used to detect interpersonal states of interest (e.g., conflict, feelings of closeness). Ground truth for states of interest are determined via self-report from phone surveys and observational data from audio and/or video collected.);
Mohammed teaches
generate an individualization data object by processing the input data and additional input data identifying qualities associated with the intervention from a simulation environment with one or more digital twins corresponding to one or more physical objects relating to the intervention ((‘089; Para 0081: by disclosure, Mohammed describes the recommendation includes a set of objectives for a patient to complete to improve the patient's metabolic health. The set of objectives include a medication regimen or schedule, a food or meal schedule, micronutrient and biota nutrient supplements, one or more lifestyle adjustments, or a combination thereof.; Para 0067: Mohammed further describes the digital twin module 450 generates a digital replica of the patient's metabolic health based on a combination of biological data 410 and patient data 420, hereafter referred to as a digital twin. The digital twin module 450 considers different aspects of a patient's health and well-being to generate and continuously update a patient's digital twin; Para 0084: the recommendation module 460 generates multiple candidate recommendations and communicates each candidate recommendation to digital twin module 450. Each candidate recommendation prescribes a different possible intervention (e.g., adjustments or changes in a patient's nutrition, exercise, and sleep habits),
wherein the one or more physical objects comprise a user and the one or more digital twins comprise a digital twin corresponding to the user (‘089; Para 0070: the metabolic model determines the impact of an aspect patient data or (e.g., particular types of foods, medications, symptoms, or lifestyle adjustments) on a patient's metabolic state by drawing correlations and relationships between recorded patient data and each labeled metabolic state, for example the impact of given foods or medications on insulin sensitivity. Once trained, the metabolic model predicts a patient's metabolic state given an aspect of the patient data 420 as an input(s). By aggregating the output of each metabolic model, the digital twin module 450 generates a predicted change in patient's metabolic state resulting from a complete set of patient data inputs by aggregating the output of each metabolic model.),
It would have been obvious to one of ordinary skill in the art before the effective filing date of claimed invention to modify the system of Margolin with the technique of usinf a precision platform as taught by Mohammed and the motivation is to generating a digital twin for patient aware wellness intervention.
Koskoszka teaches
wherein an archetypal pattern defines the states of the multi-feel state longitudinal journey and timing of the states over the duration (‘843; Para 0022: by disclosure, Edwarrds describes six archetypal sleeper types were defined and developed that exist across an identified population. In this example, there were also eleven sleep metrics selected or defined from the available metrics or data, which can be used to assess how a user slept over a period of time, such as the past 28 nights, in comparison to others like them, such as of the same or similar sleeper type.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of claimed invention to modify the system of Margolin/Mohammed with the technique of cluster-based analysis as taught by Kokoszka and the motivation is to generating a n archetypal patternfor patient aware wellness intervention
Margolin in view of Mohammed/Kokoszka discloses
wherein the simulation environment represents an environment with similarities to an environment generated by the activation, triggering, or presentation of the intervention (‘466; Para 0027: the sending of interventions can be triggered by algorithms that automatically detect and predict moods and events to send prompts to oneself or to other users in a social network. For example, if the algorithms detect that conflict is likely, the intervention system could be programmed to send a prompt that says “You are at risk for having conflict with your child/husband/friend. Would you like to try a relaxation exercise?” Users would then be guided through a computer-assisted relaxation module. Prompts could also be sent cross-person in the network. For example, when increases in stress of 40% above baseline are observed, a prompt could be sent to a family member that say “Your husband/child/friend is feeling more stressed today than usual.” The interventions can also include sending prompts after events of interest have occurred. The moods and events can include risky behaviors, extreme emotions, and/or negative moods. Further, the prompts can include warning people that conflict or other events are likely to occur, prompting people to engage in relaxation exercises, take a break, give a compliment, or to do something nice for someone else.);
generate an individualized wellness pattern aware intervention by applying a plurality of computer models to the individualization data object (‘466; Para 0031: a step of investigating an impact of each prompt and intervention on individual and interpersonal functioning and providing feedback about which interventions ate most helpful population-wide and which are better for specific users, couples, or groups of users. This could be done via a combination of machine learning/reinforcement learning models to see which interventions contribute most to changes in desired outcomes, clustering analyses and the development of sub-population specific models), wherein the individualized wellness pattern aware intervention comprises a multi-feel state longitudinal journey over a duration (‘466; Para 0055: computer algorithms to detect artifacts, which were then visually inspected and revised. All scores were averaged across each hour to obtain one estimate of each measure per hour-long period (‘466; Para 0033: Future projects could use audio recordings as an alternative, perhaps more accurate, way to identify periods of conflict);
generate output data indicating the individualized wellness pattern aware intervention (‘466; Para 0033: The output at the right of FIG. 4A provide reconstructed modalities which should provide a good approximation of the inputs if the neural network has been properly trained. Once trained, the neural network and the decision tree can be used to perform the classification. FIG. 4B is an example of a decision tree. In a variation, statistics, e.g., regression analyses, latent class analysis can be used to predict changes in relationship functioning.) and
provide a user device with the individualized wellness pattern aware intervention to activate, trigger or present the intervention based on the timing of the states over the duration.(‘466; Para 0027: the sending of interventions can be triggered by algorithms that automatically detect and predict moods and events to send prompts to oneself or to other users in a social network…The interventions can also include sending prompts after events of interest have occurred. The moods and events can include risky behaviors, extreme emotions, and/or negative moods. Further, the prompts can include warning people that conflict or other events are likely to occur, prompting people to engage in relaxation exercises, take a break, give a compliment, or to do something nice for someone else),
wherein the instructions control the hardware processor to trigger measurements by one or more input devices to receive at least part of the input data (‘466; Para 0031: the intervention schemes can be performed quantitatively through signal- and data-derived measures indicative of individual characteristics and relationship functioning concepts. This means that intervention sets or designs may be determined from the machine-learning identified interventions that are found to be most useful (i.e., the exact structure of the intervention frequency, type, etc. would be determined from algorithms conducting ongoing monitoring of whether it was effective for that individual or group of individuals). Moreover, feedback regarding which interventions are most helpful to which people could be provided directly through the intervention system (e.g., a prompt sent through phones telling a person that this exercise or technique is particularly helpful for you) or through therapist-provided feedback.).
Claims 14 and 28 are rejected as the same reason with claim 1.
With respect to claim 3, the combined art teaches the non-transitory computer readable medium of claim 1 wherein the digital twin corresponding to the user within the simulation environment is used to generate simulation data and the simulation data is presented to the user as part of the individual wellness pattern aware intervention(‘089; Para 0067: The digital twin module 450 continuously monitors biological data and patient data and correlates a patient's metabolic history with their ongoing medical history to identify changes in the patient's metabolic state).
With respect to claim 4, the combined art teaches the non-transitory computer readable medium of claim 1 wherein the measurements comprise data relating to physical physiological modeling (‘466; Para 0060).
With respect to claim 5, the combined art teaches the non-transitory computer readable medium of claim 1 wherein the plurality of computer models comprise a wellness pattern model, an individualization model, and an intervention model (‘089; Para 0059)
With respect to claim 6, the combined art teaches the non-transitory computer readable medium of claim 1 wherein the individualized wellness pattern aware intervention comprises the multi-feel state longitudinal journey over a multi-day time duration to create a specific type of user experience over the multi-day time duration (‘089; Paras 0045, 0047, 0076).
With respect to claim 7, the combined art teaches the non-transitory computer readable medium of claim 1 wherein the individualized wellness pattern aware intervention comprises an intervention output that is associated with an intervention type (‘466; Para 0034).
With respect to claim 8, the combined art teaches the non-transitory computer readable medium of claim 7 wherein the intervention type comprises one or more of suggesting an activity, starting an activity, suggesting a change in a current activity, changing a current activity, suggesting a change in the intensity of an activity, changing the intensity of an activity, canceling a scheduled activity, suggesting canceling a scheduled activity, suggesting the end of a current activity, ending a current activity, providing a reward, increasing a challenge, suggesting a product, providing a product, providing points, suggesting a future activity, starting a future activity, displaying a tool for user reflection, suggesting user feedback, displaying a tool for user feedback, suggesting a user chat or conversational activity, opening a user chat, suggesting an accountability partner, providing an accountability partner, suggesting a change in a current accountability partner, changing a current accountability partner, suggesting a team, providing a team, suggesting a change in a current team, changing a current team, suggesting a community, providing a community, suggesting a change in a current community, changing a current community, providing a level, removing a level, customizing a level, upgrading a level, downgrading a level, providing access to a product, providing access to an event, providing access to an experience, providing a badge, changing a badge, changing a social ranking, changing an indication of social belonging, changing an indication of contribution to a group, team or community, changing a social network connection, changing a group membership, providing a social connection, providing a means of communicating with another user, providing a means of sharing an emoticon with another user, providing feedback or an indication related to at least one of an activity being started, an activity in progress, an activity successfully completed, an activity duration, a user activity measure, a user activity success measure, a user activity partial success measure, a user activity in the context of a user's previous activity, a user activity in the context of a community activity, a user activity in the context of a digital twin data model, a user activity in the context of an ideal self data model, a user activity in the context of a cohort activity, a user activity in the context of a hero activity, a user activity in the context of a group of fitness class participants, a user activity in the context of an accountability partner, a user activity in the context of team, a user activity in the context of a community, a user's readiness to advance in an activity, a user's readiness to add a new activity, a user's readiness to stop an activity, a user's readiness to disrupt an activity, providing feedback or an indication related to at least one of a user habit being started, a user habit in progress, a user habit successfully completed, a user habit duration, a user habit activity measure, a user habit success measure, a user habit partial success measure, a user habit streak, a suggestion for resetting a habit, a suggestion for rescheduling a habit, rescheduling an activity, scheduling an activity, canceling a scheduled activity, a user's readiness to expand a habit, a user's readiness to add a new habit, a user's need to disrupt a habit, a user habit in the context of a community habit, a user habit in the context of a digital twin data model, a user habit in the context of an ideal self data model, a user habit in the context of a cohort habit, a user habit in the context of a hero habit, a user habit in the context of a group of fitness class participants, providing a virtual assistant, customizing a virtual assistant, providing information about a product, providing personalized information about a product, providing customized information about a product, providing a navigational path, providing a customized navigational path, providing a category of products, providing a customized category of products, and filtering a set of products, providing a special offer (‘089; Para 0063: Lifestyle data describes a record of a patient's physical activity (e.g., exercise) and a record of a patient's sleep history).
With respect to claim 9, the combined art teaches the non-transitory computer readable medium of claim 8 further comprising providing one or more media of the type video, interactive presentation, game, image, hologram image projection, autostereoscopic image projection, audio, text, spoken word, guided conversation, music, interactive simulation wherein the media contains content with a greater than average statistical probability to result in one or more of a technique correction, an emotional shift, an intellectual reframing of an experience, an emotional reframing of an experience, a distraction from a current or past experience, changing the user's anxiety level, changing the user's fear level, changing the user's hopefulness level, changing the user's sense of competence, changing the user's curiosity level, changing the user's feeling of agency, offering a relatable motivational experience, changing the user's competitiveness level, changing the user's cooperation level, changing the user's perception of social connection, changing the user's perception of personal advancement, changing the user's perception of social status, changing the user's perception of social belonging, changing the user's gratitude level, changing the user's calmness level, changing the user's focus level, changing the user's equanimity level, changing the user's sense of inner peace level (‘466; Paras 0029, 0088).
With respect to claim 10, the combined art teaches the non-transitory computer readable medium of claim 1 wherein the archetypal pattern is one of, a quest for identity, a quest to find a ideal location, emotional state, or sense of spiritual realization, a quest for justice, a quest to help a community member, a quest for social connection, a quest for social acceptance within a group, a quest for a role or status designation, a quest in search of knowledge, competence or skill, a quest for acceptance and personal affirmation, a quest for transformation, a quest for self-actualization, a quest for pleasure, fun, surprise and adventure, a quest to remove a danger, a quest for a symbolic or metaphoric goal (‘089; Para 0063: Lifestyle data may also include a description or selection of emotions or feelings capturing the patient's current state of mind and body (i.e., tired, sore, energetic)).
With respect to claim 11, the combined art teaches the non-transitory computer readable medium of claim 6 wherein the multi-feel state longitudinal journey designed to create a specific type of user experience over a multi-day time duration is associated with a pattern for adherence to a wellness, fitness lifestyle, basic health criteria, training plan, nutritional plan, and interventions are provided that are consistent with generating and improving the individual's adherence to the overall plan (‘089; Para 0189: A patient may be presented with a notification panel prompting them to manually sync step data recorded via a wearable sensor, for example a fitness tracker).
With respect to claim 12, the combined art teaches the non-transitory computer readable medium of claim 1 wherein the individualized wellness pattern aware intervention contains an interactive element or selectable indicia that enable a user to engage with and/or select from more than one individualized wellness pattern aware intervention (‘089; Para 0112: engage in more exercise, future treatment recommendations may be generated with an emphasis on more frequent exercise).
With respect to claim 13, the combined art teaches the non-transitory computer readable medium of claim 1 wherein the individualized wellness pattern aware intervention is generated as a module with executable instructions to receive inputs, provide outputs and display the habit aware intervention (‘089; Para 0113: the lifestyle twin module 670 implement a metabolic model to predict a patient's metabolic state based on patient data describing the patient's exercise habits, sleep habits, and lifestyle habits).
With respect to claim 53, the combined art teaches the computer system of claim 14, wherein the one or more non-transitory memory stores a trained individualization model, a trained wellness pattern model, and a trained intervention model;
wherein the hardware processor is programmed with the executable instructions in the non-transitory memory to evaluate at least one model of the trained individualization model, the trained wellness pattern model, and the trained intervention model, evaluate an activity, evaluate the intervention, and evaluate a modification to the intervention, to generate the one or more individualized wellness pattern aware intervention (‘089; Para 0059).
With respect to claim 59, the combined art teaches the system of claim 53 wherein the hardware processor updates one or more of the trained individualization model, trained wellness pattern model, trained intervention model stored in the memory based on machine learning, wherein the machine learning is based on simulated digital twin intervention feedback (‘089; Para 0059: he training of machine-learned models may be performed using extensive training datasets. Additionally, given the large volume of wearable sensor data, machine learned models may provide extensive insight into a patient's metabolic health at a high level of granularity.).
Claim 60. (Cancelled).
With respect to claim 61, the combined art teaches the system of claim 53, wherein the intervention model comprises an activity measure comprising evaluation of activity effect using a digital twin model. (‘089; Para 0075: he patient health management platform 130 compares timeliness, accuracy, and completeness evaluations against different thresholds)
With respect to claim 90, the combined art teaches the computer system of claim 14 wherein the individualized wellness pattern aware intervention comprises the multi-feel state longitudinal journey over a multi-day time duration for a specific type of user experience over the multi-day time duration (‘089; Paras 0045, 0047, 0076).
With respect to claim 91, the combined art teaches the computer system of claim 14 wherein the archetypal pattern is one of, a quest for identity, a quest to find a ideal location, emotional state, or sense of spiritual realization, a quest for justice, a quest to help a community member, a quest for social connection, a quest for social acceptance within a group, a quest for a role or status designation, a quest in search of knowledge, competence or skill, a quest for acceptance and personal affirmation, a quest for transformation, a quest for self-actualization, a quest for pleasure, fun, surprise and adventure, a quest to remove a danger, a quest for a symbolic or metaphoric goal (‘089; Para 0191: he illustrated interface includes a list of questions to characterize a patient's current mental, emotional, and physical state, for example questions related to their energy or mood)).
With respect to claim 92, the combined art teaches the computer system of claim 90 wherein the multi-feel state longitudinal journey designed to create a specific type of user experience over a multi-day time duration is associated with a pattern for adherence to a wellness, fitness lifestyle, basic health criteria, training plan, nutritional plan, and interventions are provided that are consistent with generating and improving the individual's adherence to the overall plan (‘089; Para 0196: he resulting feedback loop encourages high patient adherence to their patient-specific recommendations.).
Response to Arguments
Applicant’s arguments, see Remark, filed 01/14/2026, with respect to the amended claim(s) 1, 14, 28 to recite the simulation environment represents an environment with similarities to an environment generated by the activation, triggering, or presentation of the intervention, and that the simulation environment includes a digital twin corresponding to the user under rejection 35USC101 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn.
For claim rejection under 35USC103, the Applicant argued that Margolin does not disclose generate the intervention with a duration (Remark, page 11 of 13). Mohammed fails to disclose to generate an individual data object by processing the input data and additional data associated with the intervention from simulation environment with one or more digital twin corresponding to physical objects relating the intervention.
In response to the Applicant’s argument, the Examiner respectfully disagrees. Under broadest reasonable interpretation of the recited clams, Margolin discloses computer algorithm to detect artifacts, which were then visually inspected and revised. All scores were averaged across each hour to obtain one estimate of each measure per hour-long period (‘466; Para 0055). Mohammed discloses he digital twin module 450 generates a digital replica of the patient's metabolic health based on a combination of biological data 410 and patient data 420, hereafter referred to as a digital twin. The digital twin module 450 considers different aspects of a patient's health and well-being to generate and continuously update a patient's digital twin. As described herein, a digital twin is a dynamic digital representation of the metabolic function of a patient's human body. The digital twin module 450 continuously monitors biological data and patient data and correlates a patient's metabolic history with their ongoing medical history to identify changes in the patient's metabolic state (‘089; Para 0067).
Given broadest reasonable interpretation of the recited claims, it is submitted that the estimation of measure per period of Margolin, the digital twin as taught by Mohammed are in the form as described in the inventyion.
Therefore, the Examiner maintains rejection of all claims.
Conclusion
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/HIEP V NGUYEN/Primary Examiner, Art Unit 3686
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