DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-20, as recited in an RCE filed on November 14, 2024, were subject to a non-final office action filed on April 9, 2025 (the “April 9, 2025 Non-Final Office Action”). On October 9, 2025, Applicant submitted further amendments to claims 1, 6, 11, and 16 (the “October 9, 2025 Amendment”). Claims 2-5, 7-10, 12-15, and 17-20 were not amended in the October 9, 2025 Amendment (except for the amendments to independent claims 1 and 11 which they individually depend on). As such, claims 1-20, as recited in the October 9, 2025 Amendment, are currently pending and subject to the final office action below.
Response to Applicant’s Remarks
Response to Applicant’s Remarks Concerning Rejections of Claims under 35 U.S.C. § 103
Applicant’s arguments, see Applicant’s Remarks, pp. 7-10, Claims Rejections under 35 U.S.C. § 103 Section, filed October 9, 2025, with respect to rejections of: claims 1-20 under 35 U.S.C. § 103, have been fully considered, but they are not persuasive in light of Applicant’s amendments to independent claims 1 and 11. Specifically, Applicant generally argues that the combination of: Swartz et al. (Pub. No. US 2018/0165418); as modified in view of: Solari (Pub. No. US 2018/0233223); Stocker et al. (Pub. No. US 2006/0272652); and Coles et al. (Pub. No. US 2018/0211723), cited in the April 9, 2025 Non-Final Office Action, does not teach the newly added limitation directed to: “rank the at least a user effort based on an improvement of the first vitality metric”; “calculate a second vitality metric based on the rank and the improvement”; and “wherein calculating the second vitality metric further comprises using the trained metric machine-learning model to recalculate the first vitality metric as a function of prior determinations corresponding to a magnitude of numerical impact of the ranked at least a user effort”, now described in claims 1 and 11. Even though these limitations are new and considered for the first time in this office action, the Examiner respectfully disagrees.
First, Swartz et al. (Pub. No. US 2018/0165418) teaches that factors may be ranked according to the extent to which is the factor is associated with improved health (i.e., the user efforts are ranked based on an improvement to the first vitality metric), based on, for example the average positive slope of the corresponding health score trend, such that a factor associated with a more pronounced improvement in health is ranked higher. Swartz, paragraph [0019]. Paragraph [0019] further teaches that the filtered and ranked factors are then provided by the system to the individual as a health recommendation (i.e., since the filtered and ranked factors are provided as health recommendations to the user in order to improve the user’s health, the filtered and ranked factors are interpreted as the equivalent of the ranked user efforts that are based on an improvement to the first vitality metric). Paragraph [0072] also teaches that at a block 425, the system ranks the identified factors (i.e., ranking the user efforts). For example, the system may rank more highly those factors with a higher strength of correlation to the positive health change (i.e., the user efforts are ranked based on an improvement to the first vitality metric). For these reasons, Swartz is deemed to teach the new limitations directed to “rank the at least a user effort based on an improvement of the first vitality metric”.
Next, Swartz further teaches that the system can provide, on an on-going basis, recommended changes to contextual factors that are likely to cause an improvement in the health of the individual (i.e., the ranked user efforts). Swartz, paragraph [0020]. To do so, the system continues to monitor the various direct and contextual factors associated with the individual. Id. Recently captured factors are used to extend the health vector associated with the individual. Id. Based on the extensible health vector, the system assesses the health of the individual and refreshes the health score trend of the individual (i.e., calculating a second vitality metric based on the ranked user efforts and their projected improvement to the first vitality metric). Id. For these reasons, Swartz is deemed to teach the new limitations directed to “calculate a second vitality metric based on the rank and the improvement”.
Further, Solari (Pub. No. US 2018/0233223) teaches that the system may calculate one nutritional health score that shows the impact the indicated amount of a particular food item would have (i.e., calculating the first vitality metric as a function of the magnitude of numerical impact of the user effort, where the impact that an amount of a particular food item would have on the nutritional health score is interpreted to be the equivalent of a determination corresponding to a magnitude of numerical impact of the user effort). Solari, paragraph [0061]. The system may also calculate a nutritional health score that is determined to be the optimum score (i.e., an example of calculating a second vitality metric) that can be achieved by consuming the particular food item (i.e., calculating the second vitality metric is based on recalculating the first vitality metric based on the numerical impact of the user effort if the user consumes a certain amount of the particular food item). Id. Paragraph [0168] generally teaches that the nutrients may be sorted by their nutrient health score (i.e., the user effort is ranked based on a magnitude of their numerical impact on the second vitality metric). Solari, paragraph [0168]. For these reasons Solari is deemed to teach the new limitations directed to “wherein calculating the second vitality metric further comprises […] recalculating the first vitality metric as a function of prior determinations corresponding to a magnitude of numerical impact of the ranked at least a user effort”.
As such, the prior art previously cited in the April 9, 2025 Non-Final Office Action is deemed to teach these newly amended limitations, and Applicant’s arguments are not persuasive for at least the aforementioned reasons. Please see the amended rejections under the Claim Rejections – 35 U.S.C. § 103 Section below, for further clarification and complete analysis.
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 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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over:
- Swartz et al. (Pub. No. US 2018/0165418); as modified in view of:
- Solari (Pub. No. US 2018/0233223);
- Stocker et al. (Pub. No. US 2006/0272652); and
- Coles et al. (Pub. No. US 2018/0211723).
Regarding claims 1 and 11,
- Swartz et al. (Pub. No. US 2018/0165418) teaches:
- an apparatus for simulating a vitality metric, the apparatus comprising at least a processor; and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to (as described in claim 1) (Swartz, paragraphs [0011] and [0044]; Paragraph [0011] teaches a health recommendations system (i.e., an apparatus) that collects data about multiple factors pertaining to the health of an individual and uses such data to recommend contextual changes that are likely to have a positive health impact on the individual. Paragraph [0044] teaches that that the health recommendations system can also be embodied in a special purpose computer or data processor (i.e., the apparatus comprises at least a processor). Paragraph [0044] generally teaches that the data processors are programmable with computer-executable instructions that may also be stored in one or more storage devices (i.e., the apparatus comprises a memory containing instructions that configure the processor to perform functions), such as magnetic or optical-based disks, flash memory devices, or any other type of non-volatile storage medium or non-transitory medium for data.):
- a method for simulating a vitality metric, the method comprising (as described in claim 11) (Swartz, paragraph [0024]; Paragraph [0024] teaches that FIG. 1A illustrates a process 100 (i.e., a method) performed by a health recommendation system for constructing a health vector 125 and a health score trend 135 associated with an individual (i.e., simulating vitality metrics).):
- retrieve a biotic extraction pertaining to a user (as described in claims 1 and 11) (Swartz, paragraphs [0024], [0025], and [0047]; Paragraph [0025] teaches that data about an individual may be gathered by the system using a variety of techniques (i.e., retrieving a biotic extraction pertaining to a user). For example, paragraphs [0024] and [0047] teaches “receiving a biotic extraction pertaining to a user”, specifically monitoring objective health data of an individual, such as the individual’s heart rate, blood pressure, age, weight, cholesterol level, length of sleep, medical conditions, acute injuries, etc.; (ii) contextual factors, which relate to other factors that impact or characterize the health of the individual, such as the weather at the individual's location, the amount of recreational travel done by the individual, the frequency of social activities undertaken by the user, etc.; and (iii) other data, such as behavioral patterns (i.e., biotic extraction data) (see paragraph [0024]) and receiving sensor data related to directed factors characterizing the health of the user (see paragraph [0047]). NOTE: Claim Interpretation – Based on Applicant’s disclosure in paragraph [0011] of the specification, the term “biotic extraction” is interpreted as being any data that relates to a user’s health and physiology. See Applicant’s specification, as filed on July 12, 2022, paragraph [0011]. Therefore, one of ordinary skill in the art would recognize that the data collected from an individual described in Swartz is the equivalent of “a biotic extraction” as described in Applicant’s claimed invention, because the data that is collected in Swartz is related to characterizing the health of an individual (i.e., data that relates to a user’s health and physiology).);
- generate a first vitality metric using a metric machine-learning model and the biotic extraction, wherein generating the first vitality metric further comprises (as described in claims 1 and 11) (Swartz, paragraph [0033]; Paragraph [0033] teaches that the health assessment model (i.e., the metric machine-learning model) may assess both historical data stored in the individual’s health vector (e.g., the history of changes to the individual's blood pressure), the current data stored in the health vector (i.e., the individual's current blood pressure), and health vector changes (e.g., differences in the individual's blood pressure captured at different times) to generate a health score (i.e., generating a first vitality metric). The health score of an individual (i.e., the first vitality metric) may be represented by a value, such as from +100 to -100, that corresponds to strongly healthy and strongly unhealthy, respectively. The system generates the health assessment model using machine learning techniques (i.e., generating the first vitality metric using a metric machine-learning model). NOTE: Claim Interpretation – Based on Applicant’s disclosure in paragraph [0014] of the specification, the term “vitality metric” is interpreted as being a numerical value that summarizes a user’s health, energy, and well-being. See Applicant’s specification, as filed on July 12, 2022, paragraph [0014]. Therefore, one of ordinary skill in the art would recognize that the health score described in Swartz is the equivalent of the vitality metric described in Applicant’s claimed invention, since the health score described in Swartz is represented by a value, such as from +100 to -100, that corresponds to strongly healthy and strongly unhealthy (i.e., a numerical value that summarizes a user’s health and well-being).):
- training a metric machine-learning model with training data, the training data containing a plurality of data entries correlating the biotic extraction data to measured biotic parameters pertaining to the user (as described in claims 1 and 11) (Swartz, paragraphs [0013], [0048], and [0071]; Paragraph [0013] teaches that a set consisting of individual health vectors and corresponding assigned health scores is used by the system to train a health assessment model (i.e., a metric machine-learning model), using, for example, machine learning techniques (i.e., training the metric machine-learning model with training data). Paragraph [0048] teaches that the health assessment model may have been initially formed by training data stored in training data storage area 250 (i.e., training the metric machine-learning model with training data). For example, paragraph [0071] teaches that the system may recognize changes in factor values that are most strongly correlated with health improvements (i.e., correlating the biotic extraction data to measured biotic parameters), wherein the analysis is performed by a machine learning algorithm to detect such correlations.);
- generating the first vitality metric and user actions, the first vitality metric containing a summation of all individual biotic parameters associated with the biotic extraction data, as a function of the metric machine-learning model (as described in claims 1 and 11) (Swartz, paragraphs [0026]-[0032] and FIG. 1A; Paragraph [0026] teaches and Figure 1A illustrates that the direct and contextual factors 115 (i.e., examples of biotic parameters associated with the biotic extraction data) are constructed 120 by the system into a health vector 125 (i.e., generating a first vitality metric) that characterizes the individual over time (i.e., the first vitality metric contains a summation of the biotic parameters). Further, the health vector may include other categories of data, including behavioral factors (see paragraph [0028]) (i.e., generating user actions); data related to other, different events, such as the user’s health and injury events (see paragraph [0029]); and contextual health scores (see paragraph [0030]) (i.e., further examples of a summation containing all of the individual biotic parameters that are used to calculate the final health score). Paragraph [0032] teaches that the health assessment model (i.e., the metric machine-learning model) is applied 130 by the system to the health vector 125 (i.e., the biotic parameters) to generate the current health score 140 of the individual (i.e., generating the first vitality metric). NOTE: Claim Interpretation – Based on Applicant’s disclosure in paragraph [0013] of the specification, the term “biotic parameter” is interpreted as being any variable element of data relating to an element resent in the user’s biotic extraction [e.g., Boolean categories and identifiers (i.e., ‘yes/no’, ‘true/false’, etc.), qualitative elements such as the presence or absence of exercise, names of the types of exercise, numerical values, coordinates, functions, etc.]. See Applicant’s specification, as filed on July 12, 2022, paragraph [0013]. Therefore, one of ordinary skill in the art would recognize that the different factors, including the direct, contextual, and behavioral factors, described in Swartz is the equivalent of “biotic parameters” as described in Applicant’s claimed invention, because the different factors described in Swartz are variable elements of data that relate to user’s overall health. Further, as Figure 1A shows, these factors are variable elements which include qualitative and numerical elements, such as numerical values for the user’s heart rate, and the amount of the user’s activity levels (e.g., Low/Medium/High).);
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- provide, to the user, […] the at least a user effort (similar to the limitation described in claims 1 and 11) (Swartz, paragraphs [0075] and [0076]; Paragraph [0075] teaches that at block 430, the system selects the recommendations to be provided to an individual. Paragraph [0076] teaches that, at block 435, the system provides the selected recommendations (i.e., providing at least a user effort) to the individual.)…;
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- rank the at least a user effort based on an improvement of the first vitality metric (as described in claims 1 and 11) (Swartz, paragraphs [0019] and [0072]; Paragraph [0019] teaches that factors may be ranked according to the extent to which is the factor is associated with improved health (i.e., the user efforts are ranked based on an improvement to the first vitality metric), based on, for example the average positive slope of the corresponding health score trend, such that a factor associated with a more pronounced improvement in health is ranked higher. Paragraph [0019] further teaches that the filtered and ranked factors are then provided by the system to the individual as a health recommendation (i.e., since the filtered and ranked factors are provided as health recommendations to the user in order to improve the user’s health, the filtered and ranked factors are interpreted as the equivalent of the ranked user efforts that are based on an improvement to the first vitality metric). Paragraph [0072] also teaches that at a block 425, the system ranks the identified factors (i.e., ranking the user efforts). For example, the system may rank more highly those factors with a higher strength of correlation to the positive health change (i.e., the user efforts are ranked based on an improvement to the first vitality metric).); and
- calculate a second a second vitality metric based on the rank and the improvement (Swartz, paragraph[0020]; Paragraph [0020] teaches that system can provide, on an on-going basis, recommended changes to contextual factors that are likely to cause an improvement in the health of the individual (i.e., the ranked user efforts). To do so, the system continues to monitor the various direct and contextual factors associated with the individual. Recently captured factors are used to extend the health vector associated with the individual. Based on the extensible health vector, the system assesses the health of the individual and refreshes the health score trend of the individual (i.e., calculating a second vitality metric based on the ranked user efforts and their projected improvement to the first vitality metric).), wherein the second vitality metric is an updated vitality metric determined as a function of an updated biotic extraction received after the biotic extraction (as described in claims 1 and 11) (Swartz, paragraphs [0021] and [0034]; Paragraph [0021] teaches that the system may update the health assessment model based on additional data characterizing the health changes of individuals, including receiving updated indications of overall health of monitored (i.e., receiving an updated biotic extraction). Paragraph [0034] generally teaches that the system determines the changes in the health of the individual based on the periodically generated health score (i.e., calculating a second vitality metric, where the first time that the health score is generated was previously interpreted as the equivalent of the first vitality metric, and the next time that the health score is generated is interpreted as the equivalent of calculating a second vitality metric) by applying the health assessment model to each updated health vector (i.e., the second vitality metric is calculated using the updated biotic extraction).), wherein calculating the second vitality metric further comprises using the trained metric machine learning model (as described in claims 1 and 11) (Swartz, paragraph [0032]; Paragraph [0032] teaches that the health assessment model (i.e., the trained metric machine learning model) is applied 130 by the system, on a continuous or periodic bases, to the health vector to generate a current health score 140 of the individual and the system stores each assessed health score to construct a health score trend (i.e., calculating a second vitality metric, where the second health score in the health score trend is interpreted as the equivalent of calculating a second vitality metric).)…
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- Swartz does not explicitly teach, however, in analogous art of systems and methods for calculating, displaying, and modifying a personalized health score, Solari (Pub. No. US 2018/0233223) teaches a system and method, configured to:
- determine a simulated metric as a function of the generated first vitality metric of the user, wherein determining the simulated metric further comprises perturbing a biotic parameter present in the first vitality metric (as described in claims 1 and 11) (Solari, paragraph [0061]; Paragraph [0061] teaches that the system calculates a plurality of scores for each food item (i.e., determining a simulated metric). For example, the system may calculate one nutritional health score that shows the impact the indicated amount of a particular food item would have. The system may also calculate a nutritional health score that is determined to be the optimum score that can be achieved by consuming the particular food item. For example, if the indicated item is 1/4 pound of chicken consumed in a day, the system may calculate and display a nutritional health score of 56 for 1/4 pound of chicken in the day and may further indicate that an optimum score of 68 can be achieved by consuming more chicken, where that score can be achieved by consuming 1/2 pound of chicken in the day (i.e., determining the simulated metric comprises perturbing a biotic parameter present in the first vitality metric). Here, the system described in Solari is about to simulate different health scores by selecting different amounts of the type of food that is/will be consumed (i.e., perturbing a biotic parameter present in the first vitality metric). NOTE: Claim Interpretation – Based on Applicant’s disclosure in paragraph [0013] of the specification, the term “biotic parameter” is interpreted as being any variable element of data relating to an element resent in the user’s biotic extraction [e.g., Boolean categories and identifiers (i.e., ‘yes/no’, ‘true/false’, etc.), qualitative elements such as the presence or absence of exercise, names of the types of exercise, numerical values, coordinates, functions, etc.]. See Applicant’s specification, as filed on July 12, 2022, paragraph [0013]. Therefore, one of ordinary skill in the art would recognize that the different food items described in Solari is the equivalent of a “biotic parameter” as described in Applicant’s claimed invention, because the different food items described in Solari are variable elements of data that relate to food the user consumes (i.e., variable elements related to food the user consumers is also data that relates to the user’s biotic extraction, because the food that the user consumes relates to a user’s overall health and physiology).); and
- provide, to the user, the first vitality metric and at least a user effort that produces the simulated metric (as described in claims 1 and 11) (Solari, paragraph [0063]; Paragraph [0063] teaches that the device 100 illustrated in Figure 1 corresponds to one or more servers and/or computing devices that provide some or all of the following functions, including: calculating and displaying component or aggregate nutritional health scores (i.e., providing the first vitality metric); and making recommendations of foods or other consumables that can be consumed to help individuals reach optimal nutritional health scores (i.e., providing at least a user effort that produces a simulated metric).); and
- wherein calculating the second vitality metric further comprises recalculating the first vitality metric as a function of prior determinations corresponding to a magnitude of numerical impact of the ranked at least a user effort (as described in claims 1 and 11) (Solari, paragraphs [0061] and [0168]; Paragraph [0061] teaches that the system may calculate one nutritional health score that shows the impact the indicated amount of a particular food item would have (i.e., calculating the first vitality metric as a function of the magnitude of numerical impact of the user effort, where the impact that an amount of a particular food item would have on the nutritional health score is interpreted to be the equivalent of a determination corresponding to a magnitude of numerical impact of the user effort). The system may also calculate a nutritional health score that is determined to be the optimum score (i.e., an example of calculating a second vitality metric) that can be achieved by consuming the particular food item (i.e., calculating the second vitality metric is based on recalculating the first vitality metric based on the numerical impact of the user effort if the user consumes a certain amount of the particular food item). Paragraph [0168] generally teaches that the nutrients may be sorted by their nutrient health score (i.e., the user effort is ranked based on a magnitude of their numerical impact on the second vitality metric).); and
- updating the first vitality metric by recalculating, using the trained metric machine-learning model iteratively, the first vitality metric as soon as one identified user effort of the at least a user effort is performed (as described in claims 1 and 11) (Solari, paragraphs [0037] and [0153]; Paragraph [0153] generally teaches that the health scores (i.e., the first vitality metric) may be updated to reflect the diet score and optimal score of the new diet (i.e., the vitality metric is updated by recalculating the first vitality metric based on the at least one user effort, where the user effort is whatever the user consumes in the new diet). Specifically, paragraph [0153] teaches that scores in area 604 are correspondingly updated to reflect the diet score and optimal score of the new diet (i.e., the vitality metric is updated by recalculating the first vitality metric based on the at least one user effort, where the user effort is whatever the user consumes in the new diet). Paragraph [0037] teaches that these features are beneficial for providing nutritional and health advice to users based on their calculated nutritional health scores.).
Therefore, it would have been obvious to one of ordinary skill in the art of systems and methods for calculating, displaying, and modifying a personalized health score at the time of the effective filing date of the claimed invention to modify the system and method taught by Swartz, to incorporate steps and features directed to (i) simulating a plurality of health scores for consuming different food items; (ii) displaying the aggregate health score; (iii) making recommendations for how the user can improve their health score; (iv) calculating an optimum nutritional health score that can be achieved based on recommended certain amount of a particular food item that the user should consume; and (v) updating the plurality of health scores based on food the user consumes in a new diet, as taught by Solari, in order to provide nutritional and health advice to users based on their calculated nutritional health scores. See Solari, paragraph [0037]; see also MPEP § 2143 G.
- Further, the combination of: Swartz, as modified in view of Solari, does not explicitly teach, however, in analogous art of medical treatment and simulation systems and methods, Stocker et al. (Pub. No. US 2006/0272652) teaches a system and method, wherein determining the simulated metric further comprises:
- inputting the first vitality metric and the user actions into a simulation machine-learning process (as described in claims 1 and 11) (Stocker, paragraph [0059]; Paragraph [0059] teaches that the simulation engine 150 (i.e., a simulation machine-learning model) receives the adjusted input, event or activity (i.e., inputting the first vitality metric and the user actions to a simulation machine-learning model). Originally, the user may have to physically input meals eaten by the patient recently (i.e., inputting the user actions into the simulation machine-learning process). The user may review the graphs displayed by the charting and display module 110 and decide to modify one of the inputs, events, or activities, for example, carbohydrates consumed or insulin delivered to the patient (i.e., examples of user actions). Illustratively, the user may wish to create a scenario where he or she adjusts the basal rate. After the adjustment has been made, the virtual patient software 105 simulates the patient's response in terms of blood glucose level. The adjusted input, event, or activity is transferred to the simulation engine 150 from the user interface control module 120. The simulation engine 150 receives the adjusted input, event, or activity and calculates the patient's estimated blood glucose level response to the adjusted input, event, or activity.); and
- determining, as a function of an output of the simulation machine-learning model, the simulated metric (as described in claims 1 and 11) (Stocker, paragraphs [0059] and [0061]; Paragraph [0059] teaches that the simulation engine 150 calculates the patient’s estimated blood glucose level response to the adjusted input, event, or activity (i.e., determining simulated metrics as a function of output from the simulation machine-learning model). Paragraph [0061] teaches that this feature is beneficial for improving a patient’s decision making by viewing the reaction of seeing simulations.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical treatment and simulation systems and methods at the time of the effective filing date of the claimed invention to further modify the system and method taught by Swartz, as modified in view of Solari, to incorporate steps and features directed to: (i) simulating patient metrics; and (ii) determining simulated metrics using a simulation engine, as taught by Stocker, in order to help improve a patient’s decision making by viewing the reaction of seeing simulations. See Stocker, paragraph [0061]; see also MPEP § 2143 G.
- Still further, the combination of: Swartz, as modified in view of: Solari and Stocker, does not explicitly teach, however, in analogous art of personalized health recommendation systems and methods, Coles et al. (Pub. No. US 2018/0211723) teaches a system and method, wherein providing the first vitality metric and the at least a user effort further comprises:
- determining spatial data corresponding to the user (as described in claims 1 and 11) (Coles, paragraph [0047]; Paragraph [0047] teaches that in operation of the “on the go” component, if a user is away from home, a GPS feature can be used to locate restaurants that are nearby; and
- calculating a path to a location corresponding to the at least a user effort (as described in claims 1 and 11) (Coles, paragraph [0047]; Paragraph [0047] teaches that a GPS feature can be used to locate restaurants that are nearby and automatically scans the restaurants' menu information to recommend a restaurant for the user to eat at and determines particular meals to order that will fit the user's macronutrient targets. This “on the go” component may also communicate with the Avatar macronutrient tracking and meal generation part of the system. The system may also utilize the GPS feature to determine navigation to and give directions to a selected restaurant by the user (i.e., calculating a path to a location corresponding to the at least a user effort). Paragraph [0047] teaches that this feature is beneficial for automatically directing a user to the nearest place that fits the user’s preferences and suggests the meal to order.).
Therefore, it would have been obvious to one of ordinary skill in the art of personalized health recommendation systems and methods at the time of the effective filing date of the claimed invention to further modify the system and method taught by Swartz, as modified in view of: Solari and Stocker, to incorporate steps and features directed to: (i) determining the user’s location and locating restaurants near the user; and (ii) utilizing a GPS feature to determine navigation to and give directed to a recommended restaurant, as taught by Coles, in order to automatically direct a user to the nearest place that fits the user’s preferences and suggest the meal to order. See Coles, paragraph [0047]; see also MPEP § 2143 G.
Regarding claims 2 and 12,
- The combination of Swartz, as modified in view of: Solari; Stocker; and Coles, teaches the limitations of: claim 1 (which claim 2 depends on), and claim 11 (which claim 12 depends on), as described above.
- Coles further teaches a system and method, wherein:
- the biotic extraction further comprises data containing information of a user interaction with an ecological environment (as described in claims 2 and 12) (Coles, paragraph [0044]; Paragraph [0044] generally teaches that the system may include cardio programs that can track a user’s movement and speed during the workout via GPS (i.e., receiving information of a user interaction with an ecological environment) and communicate the same to the server.).
The motivations and rationales to modify the health recommendations system and method taught by Swartz, in view of: Solari; Stocker; and Coles, described in the analysis of the obviousness rejection of claims 1 and 11 above similarly apply to this obviousness rejection, and are incorporated herein by reference.
Regarding claims 3 and 13,
- The combination of Swartz, as modified in view of: Solari; Stocker; and Coles, teaches the limitations of: claim 1 (which claim 3 depends on), and claim 11 (which claim 13 depends on), as described above.
- Solari teaches an apparatus and method, wherein:
- perturbing the biotic parameter further comprises selecting a value of the biotic parameter, wherein the value of the biotic parameter is sampled from a range of values in a random manner (as described in claims 3 and 13) (Solari, paragraph [0145]; Paragraph [0145] teaches when the system determines that the user has selected one of the nutrients, the disclosed system randomly selects a set of foods that would both improve the user’s overall nutritional health score and also improve the nutrient health score for the selected nutrient (i.e., perturbing the biotic parameter comprises selecting a value of the biotic parameter, wherein the value of the biotic parameter is sampled from a range of values in a random manner).).
The motivations and rationales to modify the health recommendations system and method taught by Swartz, in view of: Solari; Stocker; and Coles, described in the analysis of the obviousness rejection of claims 1 and 11 above similarly apply to this obviousness rejection, and are incorporated herein by reference.
Regarding claims 4 and 14,
- The combination of Swartz, as modified in view of: Solari; Stocker; and Coles, teaches the limitations of: claim 1 (which claim 4 depends on), and claim 11 (which claim 14 depends on), as described above.
- Solari teaches an apparatus and method, wherein:
- the simulated metric is an output describing the first vitality metric as a function of perturbing a parameter that is affected by the at least a user effort (as described in claims 4 and 14) (Solari, paragraph [0150]; Paragraph [0150] teaches that indicator 704 represents a maximum health score (i.e., the simulated metric)/caloric intake that could be achieved by eating more of the current diet in the given time period of one day. That is, given that the current diet consists of spinach, a maximum health score of 72 could be achieved by eating approximately 550 kcal worth of raw spinach in a day (i.e., the simulated metric is an output describing the vitality metric as a function of perturbing a parameter that is affected by the user effort).).
The motivations and rationales to modify the health recommendations system and method taught by Swartz, in view of: Solari; Stocker; and Coles, described in the analysis of the obviousness rejection of claims 1 and 11 above similarly apply to this obviousness rejection, and are incorporated herein by reference.
Regarding claims 5 and 15,
- The combination of Swartz, as modified in view of: Solari; Stocker; and Coles, teaches the limitations of: claim 1 (which claim 5 depends on), and claim 11 (which claim 15 depends on), as described above.
- Solari teaches an apparatus and method, wherein:
- the simulation machine-learning process further comprises calculating an output for the simulated metric for all values within a range of values corresponding to a parameter in the biotic extraction data (as described in claims 5 and 15) (Solari, paragraph [0060]; Paragraph [0060] teaches that after determining the ranges of nutrients that are optimal for a particular individual at a particular caloric intake range in a particular time, and after knowing at least one consumed or to-be-consumed food item in that time period, the system calculates one or more nutritional health scores for the individual (i.e., calculating the output for a simulated metric). These nutritional health scores indicate the nutritional impact of the indicated food item. In general, these scores are calculated by determining, for each nutrient tracked by the system, whether the nutrient content of the food item falls within the optimal or healthy range for that nutrient (i.e., calculating all values for a simulated metric within a range of values corresponding to a parameter in the biotic extraction data).).
The motivations and rationales to modify the health recommendations system and method taught by Swartz, in view of: Solari; Stocker; and Coles, described in the analysis of the obviousness rejection of claims 1 and 11 above similarly apply to this obviousness rejection, and are incorporated herein by reference.
Regarding claims 6 and 16,
- The combination of Swartz, as modified in view of: Solari; Stocker; and Coles, teaches the limitations of: claim 1 (which claim 6 depends on), and claim 11 (which claim 16 depends on), as described above.
- Solari further teaches an apparatus and method, wherein:
- the simulation machine-learning process further comprises a computational simulation by randomly perturbing parameters and determining an effect on the first vitality metric to determine which parameters result in an outcome that is a same or different than a first input vitality metric (as described in claims 6 and 16) (Solari, paragraph [0145]; Paragraph [0145] teaches when the system determines that the user has selected one of the nutrients, the disclosed system randomly selects a set of foods that would both improve the user’s overall nutritional health score and also improve the nutrient health score for the selected nutrient (i.e., randomly perturbing parameters and determining their effects on the vitality metric in order to determining which parameters result in a different outcome than the first input vitality metric).).
The motivations and rationales to modify the health recommendations system and method taught by Swartz, in view of: Solari; Stocker; and Coles, described in the analysis of the obviousness rejection of claims 1 and 11 above similarly apply to this obviousness rejection, and are incorporated herein by reference.
Regarding claims 7 and 17,
- The combination of Swartz, as modified in view of: Solari; Stocker; and Coles, teaches the limitations of: claim 1 (which claim 7 depends on), and claim 11 (which claim 17 depends on), as described above.
- Solari teaches an apparatus and method, further comprising:
- displaying to a user, the first vitality metric and the at least a user effort (as described in claims 7 and 17) (Solari, paragraph [0063]; Paragraph [0063] teaches that the device 100 illustrated in FIG. 1 corresponds to one or more servers and/or other computing devices that provide some or all of the following functions: […] (d) calculating and displaying component or aggregate nutritional health scores (i.e., displaying the first vitality metric to the user); and/or (e) making recommendations of foods or other consumables that can be consumed to help individuals reach optimal nutritional health scores (i.e., displaying the user effort to the user).).
The motivations and rationales to modify the health recommendations system and method taught by Swartz, in view of: Solari; Stocker; and Coles, described in the analysis of the obviousness rejection of claims 1 and 11 above similarly apply to this obviousness rejection, and are incorporated herein by reference.
Regarding claims 8 and 18,
- The combination of Swartz, as modified in view of: Solari; Stocker; and Coles, teaches the limitations of: claim 1 (which claim 8 depends on), and claim 11 (which claim 18 depends on), as described above.
- Solari teaches an apparatus and method, wherein:
- determining the simulated metric further comprises utilizing the generated first vitality metric of the user and the at least a user effort (as described in claims 8 and 18) (Solari, paragraph [0063]; Paragraph [0063] teaches that the device 100 illustrated in FIG. 1 corresponds to one or more servers and/or other computing devices that provide some or all of the following functions: […] (d) calculating and displaying component or aggregate nutritional health scores (i.e., the first vitality metric); and/or (e) making recommendations of foods or other consumables that can be consumed to help individuals reach optimal nutritional health scores (i.e., the simulated vitality metric is generated based on the first vitality metric and the user effort).).
The motivations and rationales to modify the health recommendations system and method taught by Swartz, in view of: Solari; Stocker; and Coles, described in the analysis of the obviousness rejection of claims 1 and 11 above similarly apply to this obviousness rejection, and are incorporated herein by reference.
Regarding claims 9 and 19,
- The combination of Swartz, as modified in view of: Solari; Stocker; and Coles, teaches the limitations of: claim 1 (which claim 9 depends on), and claim 11 (which claim 19 depends on), as described above.
- Swartz teaches an apparatus and method, wherein:
- the biotic parameter further comprises data relating to an element in the biotic extraction (as described in claims 9 and 19) (Swartz, paragraph [0046]; Paragraph [0046] teaches that sensor 210b illustrates a smart watch which may be used to monitor the heart rate and other biometric data of the individual (i.e., biotic parameters that relate to elements within the biotic extraction).).
The motivations and rationales to modify the health recommendations system and method taught by Swartz, in view of: Solari; Stocker; and Coles, described in the analysis of the obviousness rejection of claims 1 and 11 above similarly apply to this obviousness rejection, and are incorporated herein by reference.
Regarding claims 10 and 20,
- The combination of Swartz, as modified in view of: Solari; Stocker; and Coles, teaches the limitations of: claim 1 (which claim 9 depends on), and claim 11 (which claim 19 depends on), as described above.
- Cole teaches a system and method, further comprising:
- receiving an indication from the user that the at least a user effort has been performed (as described in claims 10 and 20) (Coles, paragraph [0044]; Paragraph [0044] teaches that the system may include cardio programs that can track a user’s movement and speed during the workout via GPS and communicate the same to the server. The server may be programmed to processes the information and create a notice and send a prompt to the user to speed up or slow down (i.e., user efforts), in response to calculating a measurements for the total distance, average speed, interval speed, and energetic output (this data may be fed back and/or stored in the server to utilize for recommendations). Also, progressions can be measured (i.e., receiving indications from the user of the user efforts which have been performed) and adapted to get users closer to a user selected fitness goal.).
- Solari teaches a system and method, further comprising:
- generating the second vitality metric as a function of the at least a user effort using the metric machine-learning model, wherein generating the second vitality metric further comprises determining how the at least a user effort has impacted a numerical parameter corresponding to the first vitality metric (as described in claims 10 and 20) (Solari, paragraphs [0152] and [0177]; Paragraph [0152] teaches that Figure 7 includes an active tab (area 604 of Figure 7) that lists the current score for the actual diet (a value of 37) (i.e., the first vitality metric) and the optimal value that could be attained by eating more of the current diet (a value of 72) (i.e., generating a second vitality metric as a function of the at least a user effort). Paragraph [0177] teaches that the system can suggest foods to add (i.e., user efforts) to further increase the nutritional health score. The user can also manually add items to the menu, and the system displays the impact on the nutritional health score and the actual caloric intake (i.e., displaying the generated second vitality metric by determining how the user effort has impacted a numerical parameter corresponding to the first vitality metric, where “displaying how the food items (i.e., user efforts) impact the caloric intake range (i.e., a numerical parameter corresponding to the first vitality metric)” shows the user and determines how the user effort has impacted a numerical parameter corresponding to the first vitality metric.).); and
- identifying a numerical difference between the first vitality metric and the second vitality metric, wherein determining the numerical difference includes determining how the at least a user effort impacted the second vitality metric (as described in claims 10 and 20) (Solari, paragraph [0160], FIG. 8; Paragraph [0160] teaches that the system displays one or more food items in that area that, if consumed, would boost the user’s nutritional health score by a given amount (i.e., determining how the at least a user effort impacts the second vitality metric). For example, in the embodiment of Figure 8, consuming the recommended fish (sardines), which is indicated as containing Vitamin D and Vitamin B5, would improve the user’s nutritional health score by 9 points (i.e., identifying a numerical difference between the first vitality metric and the second vitality metric).).
The motivations and rationales to modify the health recommendations system and method taught by Swartz, as modified in view of: Solari and Stocker, described in the analysis of the obviousness rejection of claims 1 and 11 above similarly apply to this obviousness rejection, and are incorporated herein by reference.
Prior Art Made of Record and Not Relied Upon
The following prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure.
- Leabman (Pub. No. US 2020/0193326): Leabman discloses a method for training a model for use in monitoring a health parameter. See Leabman, paragraph [0003]. Paragraph [0003] further discloses that the method involves receiving control data that corresponds to a control element, wherein the control data corresponds to a health parameter of a person (i.e., retrieving a biotic extraction pertaining to a user, as described in claims 1 and 11). Paragraphs [0062] and [0069] disclose that the method uses various types of sensing devices, including various types of wearable devices, such as a smart watch, for tracking a person’s movements (i.e., retrieving a biotic extraction pertaining to a user, as described in claims 1 and 11). Paragraph [0164] discloses that the system includes a machine learning engine 2760 (i.e., a metric machine-learning model, as described in claims 1 and 11) that is configured to process raw data that is received form the sensor system 2710, e.g., as raw data records, and the control data received from the control element 2764 to learn a correlation, or correlations, that provides acceptable correspondence to a health parameter suc