Patent Publication Number: US-2023147239-A1

Title: System and method for providing fertility enhancing dietary recommendations in individuals with ovulatory disorders or at risk of ovulatory disorders

Description:
FIELD OF THE INVENTION 
     The present invention presents new and innovative methods and systems for personalized, real-time diet and lifestyle recommendations for users that are seeking to improve their own fertility. 
     In a preferred embodiment, the present invention relates to novel dietary recommendations for improving fertility and conception in individuals, especially women at risk of ovarian disorders or diagnosed with ovarian disorders, in particular, with Polycystic Ovary Syndrome (PCOS). 
     BACKGROUND TO THE INVENTION 
     Ovulatory disorders are the most common form of infertility in women, affecting nearly 40% of women in child-bearing age. Ovulatory disorders directly affect the ovaries&#39; ability to release the egg. Symptoms may include the absence of regular periods, called anovulation, or irregular menses due to a hormonal imbalance, severe stress, high endurance or excessive exercising, extreme weight (both over-weight and under-weight), thyroid dysfunction, insulin resistance and eating disorders. https://www.azfertility.com/your-miracle/fertility-basics/causes-of-infertility/ovulatory-disorders/ 
     Polycystic ovary syndrome (PCOS) also known as hyperandrogenic anovulation or Stein-Leventhal syndrome is a hormonal disorder causing enlarged ovaries with small cysts on the outer edges. PCOS is one of the most common conditions in reproductive aged women affecting 8-20% of reproductive-aged women. Signs and symptoms of PCOS include irregular or no menstrual periods, heavy periods, excess body and facial hair, acne, pelvic pain, difficulty getting pregnant, and patches of thick, darker, velvety skin. Symptoms may be exacerbated by obesity. 
     While the cause of PCOS is unknown, it is linked to elevated androgen, elevated insulin, increased inflammation and there are genetic factors which may predispose certain individuals to PCOS. Diagnosis is based on two of the following three findings: no ovulation, high androgen levels, and ovarian cysts. 
     Common treatments for PCOS include fertility medications to help induce ovulation. These medications can be either oral medications or injectable medications. In addition, treatments for the secondary symptoms to control elevated insulin levels, such as metformin, or anti-androgen medication to control elevated androgen levels have been prescribed (Teede et al. 2018, Human Reproduction, 33(9): 1602-18). 
     One major problem with the state of the art concerning diet and fertility is that the dietary recommendations are far too general as they do not relate to the different, specific medical conditions which affect fertility of individuals with ovulatory disorders. 
     Another problem is that studies on fertility and diet often investigate only single nutrients at a time or food groups without putting them in the context of an entire diet over a day or entire meal and they do not provide recommended intake amounts to be consumed per day or for each specific medical condition which affects fertility. 
     The present invention addresses the deficiencies in the state of the art by providing new and innovative methods and systems for personalized, real-time diet and lifestyle recommendations for users. 
     In particular, the present invention addresses the specific condition of enhancing fertility and conception in individuals both at risk of ovulatory disorders and those diagnosed with ovulatory disorders, by providing a novel, consolidated dietary recommendations which combine: 
     specific dietary components recommended to be consumed daily 
     specific dietary intake amounts 
     specific recommendations on avoiding certain dietary components 
     specific recommendations on lifestyle components 
     SUMMARY OF THE INVENTION 
     The present invention presents new and innovative methods and systems for personalized, real-time diet and lifestyle recommendations for users that are seeking to improve their own fertility. 
     In several embodiments, a method and system is provided that includes requesting and receiving a plurality of user attributes, comparing the plurality of user attributes to a corresponding plurality of evidence-based fertility benchmarks, determining a plurality of fertility support opportunities based on the plurality of user attributes and the comparison to the corresponding plurality of evidence-based fertility benchmarks, identifying a plurality of fertility enhancing recommendations based on the plurality of fertility support opportunities, and presenting at least one of the plurality of fertility enhancing recommendations. 
     In a preferred embodiment of the invention, the system and method present fertility enhancing recommendations are for increasing fertility in a individual at both at risk of ovulatory disorders and diagnosed with ovulatory disorders, in particular, PCOS. 
    
    
     
       DESCRIPTION OF FIGURES 
         FIG.  1    illustrates a system according to an embodiment of the present invention. 
         FIG.  2    illustrates system components according to exemplary embodiments of the present invention. 
         FIG.  3    illustrates system components according to an exemplary embodiment of the present invention. 
         FIGS.  4 A and  4 B  illustrates an example plurality of dietary recommendations for a user according to an exemplary embodiment of the present invention. 
         FIG.  4 A  illustrates daily dietary recommendations for the individual&#39;s diet for individuals at risk of developing ovulatory disorders. 
         FIG.  4 B  illustrates daily dietary recommendations for the individual&#39;s diet for individuals who have been diagnosed with an ovulatory disorder, such as PCOS. 
         FIG.  5    illustrates a method according to an embodiment of the present invention. 
         FIGS.  6 A and  6 B  illustrate a method according to an embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     To promote user fertility, it may be useful to provide a customized diet and lifestyle plan to users interested enhancing fertility. Therefore, a customized, integrated approach is necessary to provide maximum benefit to enhance chances of conception. 
     One method of providing this level of personalization is to receive information from a individual regarding certain medical conditions or diseases and the current status of conception to compare with a historical evidence-based fertility database to generate recommended diet and lifestyle options that will help improve the patient&#39;s fertility based on the provided information. 
     In several embodiments of the invention, the patient&#39;s fertility history is documented to generate recommended diet and lifestyle options that will help improve the patient&#39;s fertility and chances of conception. 
     An example system may be beneficial if it can provide user support throughout the entire conception journey, from the early stages of planning to the final stages of conception. As such, this example system would be useful should it provide constant, around the clock access to both virtual and personal fertility, lifestyle, nutrition, and exercise coaches. Furthermore, an example system may provide recommendations to manage anxiety, reduce stress, or provide specific supplementation, all of which are also linked to a user&#39;s fertility. 
       FIG.  1    illustrates a system  100  according to an embodiment of the present disclosure. The system  100  includes a user device  102  and a recommendation system  104 . The user device  102  may be implemented as a computing device, such as a computer, smartphone, tablet, smartwatch, or other wearable through which an associated user can communicate with the recommendation system  104 . The user device  102  may also be implemented as, e.g., a voice assistant configured to receive voice requests from a user and to process the requests either locally on a computer device proximate to the user or on a remote computing device (e.g., at a remote computing server). 
     The recommendation system  104  includes one or more of a display  106 , an attribute receiving unit  108 , an attribute comparison unit  110 , an evidence-based diet and lifestyle recommendation engine  112 , an attribute analysis unit  114 , an attribute storing unit  116 , a memory  118 , and a CPU  120 . Note, that in some embodiments, a display  106  may additionally or alternatively be located within the user device  102 . In an example, the recommendation system  104  may be configured to receive a request for a plurality of fertility enhancing recommendations  140 . For example, a user may install an application on the user device  102  that requires the user to sign up for a recommendation service. By signing up for the service, the user device  102  may send a request for the fertility enhancing recommendations  140 . In a different example, the user may use the user device  102  to access a web portal using user-specific credentials. Through this web portal, the user may cause the user device  102  to request fertility enhancing recommendations from the recommendation system  104 . 
     In another example, the recommendation system  104  may be configured to request and receive a plurality of user attributes  122 . For example, the display  106  may be configured to present an attribute questionnaire  124  to the user. The attribute receiving unit  108  may be configured to receive the user attributes  122 . In one example, the attribute receiving unit  108  may receive a plurality of answers  126  based on the attribute questionnaire  124 , and based on the plurality of answers, determine the plurality of user attributes  122 . For example, the attribute receiving unit  108  may receive answers to the attribute questionnaire  124  suggesting that the diet of the user is equivalent to the recommended dietary allowance (“RDA”) and then determine the user attributes  122  to be equivalent to the RDA, such as 500 mg/day of Vitamin C. In another example, the user device attribute receiving unit  108  may directly receive the user attributes  122  from the user device  102 . 
     In another example, the attribute receiving unit  108  may be configured to receive the test results of a home-test kit, the results of a standardized health test administered by a medical professional, the results of a self-assessment tool used by the user, or the results of any external or third party test. Based on the results from any of these tests or tools, the attribute receiving unit  108  may be configured to determine the user attributes  122 . For example, this may be measurements of the nutrient levels in blood or urine of the user which may be compared to standardized nutrient levels. 
     The recommendation system  104  may be further configured to compare the plurality of user attributes  122  to a corresponding plurality of evidence-based fertility benchmarks  128 . For example, the attribute comparison unit  110  may be configured to determine a user fertility segment  130 . In an example, the user fertility segment  130  may be one of a concerned planner, a healthy planner, a challenged conceiver, and a healthy conceiver. In this example, a concerned planner represents a segment in which a user is experiencing medical issues related to fertility health and is currently in a family planning stage, a healthy planner represents a segment in which a user is considered to have a healthy fertility status and currently in a family planning stage, a challenged conceiver represents a segment in which a user is experiencing medical issues related to fertility health and is actively trying to conceive, and a healthy conceiver represents a segment in which a user is considered to have a healthy fertility status and is actively trying to conceive. 
     In a preferred example, the user fertility segment could be much more specific. For example, the user fertility segment could be a user at risk of ovulatory disorders or diagnosed with ovulatory disorders, in particular, PCOS. The user fertility segment could be further specified as a user with a high BMI, high androgen levels, high insulin levels. 
     Furthermore, the attribute comparison unit  110  may be further configured to determine a fertility benchmark set  132  based on the user fertility segment  130 . For example, if the attribute comparison unit  110  determines that a user falls into the ovulatory disorders user fertility segment  130 , based on the plurality of user attributes  122 , the attribute comparison unit  110  may select a fertility benchmark set  132  that has been created and defined according to the specific needs of a user undergoing a particular medical treatment, such as ovarian stimulation. 
     The comparison unit  110  may be further configured to select, from this determined fertility benchmark set  132 , the evidence-based fertility benchmarks  128  and compare the now selected evidence-based fertility benchmarks  128  to each of the corresponding user attributes  122 . For example, when the fertility benchmark set  132  has been determined, in response to the determination, the attribute comparison unit  110  may compare a user attribute  122  that represents the user&#39;s vitamin C intake to an evidence based fertility benchmark  128  that represents a benchmark vitamin C intake, determining whether the user is below, at, or above the benchmark vitamin C intake. Though this example is based on a concrete, numerical comparison, another example of a benchmark comparison may be qualitative and different depending on a person. For example, a user attribute  122  may indicate that the user is currently experiencing higher than normal levels of stress. An example benchmark related to a user stress level may indicate that an average or low level of stress is desired and thus, the user attribute  122  indicating a higher level of stress is determined to be below that of the benchmark. As different users experience differing levels of stress, even under the same circumstances, such a comparison requires a customized approach. 
     In addition, during the comparison from the prior example, the attribute comparison unit  110  may be configured to determine a user fertility score  134  based on the comparison between the evidence-based fertility benchmarks  128  and the user attributes  122 . For example, the attribute comparison unit  110  may determine a user fertility score of 95/100 if the user attributes  122  very nearly meet all or most of the corresponding evidence-based fertility benchmarks  128 . In another example, a score may be represented through lettering grades, symbols, or any other system of ranking that allows a user to interpret how well their current attributes rate amongst benchmarks. This user fertility score  134  may be presented through the display  106 . 
     The recommendation system  104  may be further configured to determine a plurality of fertility support opportunities  138  based on the plurality of user attributes  122  and the comparison to the corresponding plurality of evidence-based fertility benchmarks  128 . In one example, the attribute comparison unit  110  may determine fertility support opportunities  138  for every user attribute  122  that does not meet the corresponding evidence-based fertility benchmark. In this example, a corresponding evidence-based fertility benchmark  128  may require a user have an intake of 3 g/day of l-carnitine, whereas the user attribute may indicate the user is only receiving 2 g/day of l-carnitine. Therefore, the attribute comparison unit  110  may determine an increase in l-carnitine intake to be a fertility support opportunity  138 . 
     In another example, the attribute comparison unit  110  may be configured to identify a first set of user attributes  136  comprised of each of the plurality of user attributes  122  that are below the corresponding one of the plurality of evidence-based fertility benchmarks  128  as well as identify a second set of user attributes  136  comprised of each of the plurality of user attributes  122  that are greater than or equal to the corresponding evidence-based fertility benchmarks  128 . While the first set of user attributes  136  is determined similarly to the above given example, the second set of user attributes  136  differs in that, although the associated user does not appear to have a deficiency, there may be opportunities to support fertility by recommending the user maintain current practices or opportunities to further improve upon them. Accordingly, the recommendation system  104  may determine opportunities to support fertility based on which attributes  122  populate either sets  136 . 
     The recommendation system  104  may be further configured to identify a plurality of fertility enhancing recommendations  140  based on the plurality of fertility support opportunities  138 . For example, the evidence-based diet and lifestyle recommendation engine  112  may be configured to be cloud-based. The recommendation engine  112  may comprise one or more of a plurality of databases  142 , a plurality of dietary restriction filters  144 , and an optimization unit  146 . Based on the plurality of opportunities  138 , the recommendation engine  112  may identify the plurality of fertility enhancing recommendations  140  according to the one or more of plurality of databases  142 , the dietary restriction filters  144 , and the optimization unit  146 . 
     In another example, the recommendation system  104  may be configured to provide continuous recommendations, based on prior user attributes. For example, the recommendation system  104  may comprise, in addition to the previously discussed elements, an attribute storing unit  116  and an attribute analysis unit  114 . The attribute storing unit  116  may be configured to, responsive to the attribute receiving unit  108  receiving the plurality of user attributes  122 , add the received user attributes  122  to an attribute history database  148  as a new entry based on when the plurality of user attributes  122  were received. For example, if user attributes  122  are received by the attribute receiving unit  108  on a first day, the attribute storing unit  116  will add the received user attributes  122  to a cumulative attribute history database  148  noting the date of entry, in this case the first day. Later, if user attributes  122  are received by the attribute receiving unit  108  on a second day, e.g. the next day, the attribute storing unit  116  will also add these new attributes to the attribute history database  148 , noting that they were received on the second day, while also preserving the earlier attributes from the first day. 
     This attribute analysis unit  114  may be configured to analyze the plurality of user attributes  122  stored within the attribute history database  148 , wherein analyzing the stored plurality of user attributes  122  comprises performing a longitudinal study  150 . Continuing the earlier example, the attribute analysis unit  114  may perform a longitudinal study of the user attributes  122  from each of the first day, the second day, and every other collection of user attributes  122  found within the attribute history database  148 . The evidence based diet and lifestyle recommendation engine  112  may be further configured to generate a plurality of fertility enhancing recommendations  140  based on at least the stored user attributes  122  found within the attribute history database  148  and the analysis performed by the attribute analysis unit  114 . 
     In an embodiment, the attribute analysis unit  114  is further configured to repeatedly analyze the plurality of user attributes  122  stored within the attribute history database  148  responsive to the attribute storing unit  116  adding a new entry to the attribute history database  148 , essentially re-analyzing all of the data within the attribute history database  148  immediately after new user attributes  122  are received. Similarly, the evidence based diet and lifestyle recommendation engine  112  may be further configured to repeatedly generate the plurality of fertility enhancing recommendations  140  responsive to the attribute analysis unit  114  completing an analysis, thereby effectively generating new fertility enhancing recommendations  140  that consider all past and present user attributes  122  each time a new set of user attributes  122  is received. 
       FIG.  2    illustrates an example database containing a plurality of user attributes  122 . For example, the user attributes  122  may be populated by information regarding one or more of age  202 , gender  204 , weight  206 , height  208 , activity level  210 , food sensitivities  212 , preferred diet  214 , fertility status  216 , fertility-related medical conditions  218 , co-morbidities  220 , and lifestyle choices  222 . Some examples of food sensitivities  212  include lactose, eggs, nuts, shellfish, soy, fish, and gluten sensitivities. Some non-limiting examples of a preferred diet  214  includes vegetarian, vegan, Mediterranean, kosher, halal, paleo, low carbohydrate, and low fat diets. 
     In a preferred embodiment, the diet is a low carbohydrate diet. 
     Some non-limiting examples of fertility-related medical conditions  218  include polycystic ovary syndrome, premature ovarian insufficiency, endometriosis, recurring pregnancy loss, undergoing IVF, semen abnormality, misuse of anabolic steroids and protein supplements, erectile dysfunction, hormonal imbalance, low testosterone, and prostate issues. 
     In a preferred embodiment, the fertility-related medical condition  218  is ovulatory disorders, in particular PCOS. 
     Some non-limiting examples of co-morbidities  220  include diabetes, obesity, high blood pressure, high cholesterol, celiac, and heartburn. Some non-limiting examples of lifestyle choices  222  may include sleeping habits such as the typical hours of sleep per night, stress attributes such as the level of stress currently experienced by the user or typical levels of stress experienced, whether the user smokes, the number of alcoholic drinks typically consumed, exercise frequency, or any other lifestyle choices  222  that may have a bearing on fertility. 
       FIG.  3    illustrates an example embodiment of an evidence-based diet and lifestyle recommendation engine  112 . In an example embodiment, the evidence-based diet and lifestyle recommendation engine  112  comprises a plurality of databases  142 , a plurality of dietary filter restrictions  144 , and an optimization unit  146 . The plurality of databases  142  may include a database comprised of one or more of recipes  302 , food items  304 , food products  306 , and diet tips  308 . The dietary filter restrictions  144  may comprise filters for one or more of food sensitivities  310 , preferred diets  312 , fertility-related conditions  314 , and co-morbidities  316 . The optimization unit  146  may contain optimization rules based on one or more of caloric intake  318 , food groups  310 , and specific nutrients  312 . 
       FIG.  4    illustrates an example plurality of dietary and lifestyle recommendations according to an exemplary embodiment of the present disclosure. This dietary recommendation example 400 details specific recommendations that may be presented to a user after a plurality of fertility enhancing recommendations  140  have been determined by the recommendation system  104 . Specifically, example 400 details the fertility enhancing recommendations  140  as determined for a user that has a specific fertility-related medical condition  218 . Specifically, example 400 represents the fertility enhancing recommendations as determined for a user that has ovulatory disorders or is at risk of ovulatory disorders. 
     Other recommendations  140  may be to simply avoid, or increase, consumption of a particular food item. Similarly, the recommendations  140  may include recommendations to consume certain substances moderately or to prefer one substance over another. Though there are many different types of recommendations  140  found within the example 400, one should appreciate that any type of qualitative or quantitative recommendation may be made regarding these food items and nutrients. 
     Furthermore, the recommendation system  400  may generate fertility enhancing recommendations  140  that include lifestyle changes, such as altering activity level, increasing the number of hours of rest per night, taking action to reduce stress, or similar lifestyle-affecting actions. For example, a high level of stress may negatively affect a user&#39;s fertility. Such stress may derive from the relationship between the partners actively trying to conceive. Some example fertility enhancing recommendations  140  may include suggestions for methods by which the couple may decrease tensions in the relationship in order to alleviate stress. In another example, the fertility enhancing recommendations  140  may include recommendations to increase the amount of time a user rests, including sleeping habit recommendations. These recommendations may range from general recommendations, such as instruction to get more sleep, to more detailed recommendations, including specific exercise routines, specific diets and recipes, or suggested dates for visits to a medical professional. 
     In addition, in another embodiment, the fertility enhancing recommendations  140  generated by the recommendation system  104  may include specific recommendations for a product. For example, the recommendation system  104  may access a database containing information on a variety of supplements in the market. Then, based on its own analysis or through the use of third party research, the recommendation system  104  may analyze the different options for a specific supplement, such as Vitamin C, to determine that a specific 500 mg supplement from a first brand, Brand A, is the most beneficial supplement as compared to other 500 mg vitamin C supplements available from a second, third, and fourth brand. Such an analysis may be conducted based on the quality of the supplements, the cost of the supplements, known side effects, method of manufacture, or any other factors that may distinguish the supplement provided by one brand from a supplement provided by another brand. The recommendation system  104  may provide similar recommendations as related to food items, such as a particular type or brand of apple, and any other category of product which may require the user to select one of multiple available options. 
       FIG.  5    illustrates an example embodiment of a method  500  of the presently disclosed method, as was discussed above in relation to the system  100 . The method  500  may be implemented in a system, such as the system  100 , or on a CPU. For example, the method may be implemented by one or more of the attribute receiving unit  108 , the attribute analysis unit  114 , the attribute storing unit  116 , the attribute comparison unit  110 , the evidence-based diet and lifestyle recommendation engine  112 , or the user device  102 . The method  500  may also be implemented by a set of instructions stored on a computer readable medium that, when executed by a processor, cause the computer system to perform the method. For example, all or part of the method  500  may be implemented by the CPU  120  and memory  118 . Although the examples below are described with reference to the flowchart illustrated in  FIG.  5   , many other methods of performing the acts associated with  FIG.  5    may be used. For example, the order of some of the blocks may be changed, certain blocks may be combined with other blocks, one or more of the blocks may be repeated, and some of the blocks described may be optional. 
     Block  502  can include requesting and receiving a plurality of user attributes  122 . For example, a display  106  may present an attribute questionnaire  124  to solicit answers  126 , to which the user device  102  provides the answers  126  to then be selected as user attributes  122 . In block  504 , comparisons of the plurality of user attributes  122  to a corresponding plurality of evidence-based fertility benchmarks  128  may occur. Based on these comparisons, at block  506 , a plurality of fertility support opportunities  138  can be determined based on the plurality of user attributes  122  and the comparison to the corresponding plurality of evidence-based fertility benchmarks  128 . At block  508 , an embodiment of method  500  may identify a plurality of fertility enhancing recommendations  140  based on the plurality of fertility support opportunities  138 . For example, the evidence-based diet and lifestyle recommendation engine  112  may comprise a cloud-based system trained to interpret fertility support opportunities to provide recommendations  140 . Lastly, at block  510 , at least one of the plurality of fertility enhancing recommendations  140  can be presented. 
       FIGS.  6 A and  6 B  disclose an exemplary embodiment of a method  600  of the presently disclosed method. The method  600  may be implemented in a system, such as the system  100 , or on a CPU. For example, the method may be implemented by one or more of the attribute receiving unit  108 , the attribute analysis unit  114 , the attribute storing unit  116 , the attribute comparison unit  110 , the evidence-based diet and lifestyle recommendation engine  112 , or the user device  102 . The method  600  may also be implemented by a set of instructions stored on a computer readable medium that, when executed by a processor, cause the computer system to perform the method. For example, all or part of the method  600  may be implemented by the CPU  120  and memory  118 . Although the examples below are described with reference to the flowchart illustrated in  FIG.  6   , many other methods of performing the acts associated with  FIG.  6    may be used. For example, the order of some of the blocks may be changed, certain blocks may be combined with other blocks, one or more of the blocks may be repeated, and some of the blocks described may be optional. 
     Block  602  may include receiving a request for a plurality of fertility enhancing recommendations  140 . For example, a user may submit a request for the fertility enhancing recommendations  140  through any number of methods, including: opening an application on the user device  102 , making a formal request through an application on the user device  102 , submitting a request for periodic fertility enhancing recommendations  140  through the user device  102 , signing into an online account through a web browser, making a formal request through a web browser, or submitting a request for periodic fertility enhancing recommendations  140  through the web browser. 
     At block  604 , the recommendation system  104  may request and receive a plurality of user attributes  122 . For example, the recommendation system  104  may present an attribute questionnaire  124  to the user. This attribute questionnaire  124  may be a standard questionnaire or a questionnaire that is customized based on known preliminary attributes, or answers to prior questions. In another example, the recommendation system  104  may request the plurality of user attributes  122  by providing a list of available home test kits, that a user may use at home. Then, after the test has been performed, the recommendation system  104  may receive the results from the test and, based on these results, determine the user attributes  122  related to such a test. For example, the home test kit may be an application to track the timing of the ovulation cycle of the user to determine the best dates for conception which may be monitored by a further application on an additional user device. 
     In another example, at block  604 , the recommendation system  104  may provide a self-assessment tool. Similar to the prior example, the user will may make use of this self-assessment tool, submitting the results to the recommendation system  104 . Again, based on the received results, the recommendation system  104  may determine the user attributes  122  based on the test. In yet another example, the recommendation system  104  may request the user have a standardized health test performed by a medical professional. In this example, the results of this performed health test may be submitted to the recommendation system  104 , which thereby determines the user attributes  122  based on the results. Though some specific examples as to external tests have been given, these examples are non-limiting as the recommendation system  104  may be configured to receive results of any external or third party test in order to determine the corresponding user attributes  122 . 
     In Block  606 , the recommendation system  104  may be configured to compare the plurality of user attributes  122  to a corresponding plurality of evidence-based fertility benchmarks  128 . For example, these evidence-based fertility benchmarks  128  may include standardized benchmarks, as in benchmarks that are given to all, regardless of individual variances. In another example, these benchmarks  128  may be customized based on a particular user&#39;s history or goals. For example, if a healthy user is trying to improve his or her fertility and the current user attributes  122  exceeds all standard evidence-based fertility benchmarks  128 , the recommendation system  104  may be configured to determine a customized fertility benchmark set  132  for which the particular user should aim. In contrast, in another example, a different user that is far below a standard evidence-based fertility benchmark  128  may be compared to a different, lower benchmark value as a manner of inspiring progress and providing milestones. 
     The example method, at Block  608 , may be configured to determine a plurality of fertility support opportunities  138  based on the plurality of user attributes  122  and the comparison to the corresponding plurality of evidence-based fertility benchmarks  128 . For example, the recommendation system  104  may determine that a user attribute  122  corresponds to an above optimal stress level. Based on this comparison, the recommendation system  104  may determine a fertility support opportunity  138  to reduce stress. In another example, the recommendation system  104  may determine that the user has not yet seen a medical professional, and, as such, determine a fertility support opportunity  138  to visit a medical professional. 
     At Block  610 , the recommendation system  104  may identify a plurality of fertility-enhancing recommendations  140  based on at least the plurality of fertility support opportunities  138 . For example, the recommendation system may determine a plurality of similar prior cases by analyzing the attribute history database  148 , identifying similarities between the user attributes  122  received and the plurality of prior user attributes within the attribute history database  148 . For example, the recommendation system  104  may identify that the user attributes  122  detail a user with an above average BMI and other similarities that correspond to a particular group of past users and therefore the cases of those member of that particular group of past users are determined as similar prior cases. 
     Furthermore, in this example, the recommendation system  104  may determine a plurality of prior case results based on the plurality of similar prior cases. As detailed previously, the attribute history database  148  may comprise corresponding recommendations associated with prior user attributes, and the effectiveness of these corresponding recommendations. As such, the recommendation system  104  may analyze the corresponding recommendations and their effectiveness as associated with the particular group of past users to determine a plurality of prior case results. 
     Furthermore, in this example, the recommendation system  104  may determine successful recommendations and a plurality of unsuccessful recommendations based on a plurality of prior case results. For example, the recommendation system  104  may have recommended the users in that particular group of past users increase exercise levels in some cases and decrease food consumption in other cases. Based on prior case results as determined based on the attribute history database  148 , the recommendation system  104  may determine that the recommendations for decreasing food consumption were not very successful, yet increasing exercise levels proved to be very successful and, as such, determines that increasing exercise levels is a successful recommendation whereas decreasing food consumption is an unsuccessful recommendation. By conducting analysis of these prior user attributes, the recommendation selection and the effectiveness of corresponding recommendations, the recommendation system  104  may identify trends associated with different subset patient populations, thereby creating and validating a plurality of lifestyle interventions. These examples of successful and unsuccessful recommendations are nonlimiting, as different groups may experience different levels of success to the same recommendations. 
     In addition, the recommendation system  104  may be configured to determine a plurality of fertility enhancing recommendations based on the plurality of successful recommendations and the plurality of unsuccessful recommendations. For example, the recommendation system  104  may be configured to only recommend the plurality of successful recommendations. In another example, the recommendation system  104  may still recommend any of the unsuccessful recommendations. The recommendation system  104  may make these recommendations based on any number of reasons, including a slight difference in the user attributes  122  as compared to the prior user attributes, a lack of insufficient data to support a true unsuccessful recommendation, or data supporting that, although unsuccessful, the recommendation is popular and often followed through by users. In another example, the recommendation system  104  may recommend less than all of the plurality of successful recommendations. In an example, the decision for selecting which of the plurality of recommendations to present generate may be performed by an Al. 
     In another example, successful recommendations may be based on guidelines associated with particular medical conditions, such as a user that has ovulatory disorders. In that case, these guidelines would be determined as successful recommendations. 
     At Block  612 , the recommendation system may present at least one of the plurality of fertility-enhancing recommendations  140 . At Block  614 , the recommendation system  104  may receive a recommendation selection chosen from the presented at least one of the plurality of fertility enhancing recommendations  140 . For example, a user may be presented with three fertility-enhancing recommendations  140 , to reduce caffeinated soft drinks to less than 2 servings per day, to increase exercise, and to reduce carbohydrate intake to less than 54% of the total daily energy consumption. The user may select one, two, or all three of these options. In an example, the user may use the user device  102  to select the fertility-enhancing recommendations  140  to increase exercise and to reduce caffeinated soft drink consumption. As such, the recommendation system  104  receives, from the user device  102 , these two selected recommendations as the recommendation selection. In another example, the user may not select any of the presented recommendations, at which point the recommendation system  104  may generate and present a different plurality of fertility enhancing recommendations  140 . 
     In another example, after the user reviews the presented fertility enhancing recommendations  140 , the user may submit a request to contact a fertility coach. For example, the user may be undecided as to how to implement the recommendations or may simply have a question for which the user seeks an answer. In some instances, the recommendation system  104  may determine that the question for which the user seeks an answer may be adequately answered by a virtual coach, and thereby provides access to and interaction with said virtual coach. In other instances, the recommendation system  104  may determine that the question will be best handled by a personal coach, a living individual, and thereby provides access to and interaction with said personal coach. 
     At Block  616 , the recommendation system  104  may store the plurality of user attributes  122  and the recommendation selection in the attribute history database  148 . For example, the recommendation system  104  may store all user attributes  122  received on a first day, along with the recommendation selection received on that same first day. These user attributes  122  and recommendation selection may then be accessed by the recommendation system  104  in the future when analyzing the attribute history database  148 . 
     The recommendation system  104  may obtain at least one recommendation result at Block  618 . In an example, the user may submit a recommendation result through the user device  102 . This result may include a qualitative or quantitative rating as selected by the user. In another example, the recommendation system  104  may receive a future plurality of user attributes  122  and, at that time, compare the received future user attributes with the previously received user attributes, now prior user attributes, within the attribute history database  148 . Based on this comparison, the recommendation system  104  may determine a recommendation result, such as decreased or increased BMI. After obtaining this recommendation result, the recommendation system  104  may store the at least one recommendation result in the attribute history database  148 , corresponding to the prior recommendation selection. This recommendation system  104  may then await another request for fertility enhancing recommendations  140 , and at that time, perform the method  600  again at Block  602 . 
     Such an example method as disclosed in  FIGS.  6 A and  6 B  allow for the continuous, customized, integrated recommendation system  104  to endlessly improve upon recommendations as the attribute history database  148  grows in size. Through this growth, the recommendation system  104 , and in some embodiments, the evidence-based diet and lifestyle recommendation engine  112 , will have an ever-expanding set of data from which it can derive fertility enhancing recommendations  140 , with increasing particularity regarding what users receive which recommendations. 
     In another aspect, a method of treatment may comprise using any of the above described systems or methods to generate any one or more of the fertility enhancing recommendations  140 , diet and lifestyle recommendations, or specific supplementation recommendations. Furthermore, the method of treatment may comprise administering a treatment based on at least the any of one or more of the fertility enhancing recommendations  140 , diet and lifestyle recommendations, or specific supplementation recommendations to a user. For example, when the recommendation system  104  determines a fertility enhancing recommendation  140  that comprises increasing a user&#39;s l-carnitine intake from 2 g/day to 3 g/day by way of a 1 g l-carnitine supplement, an example method of treatment may comprise administering a treatment comprising a 1 g l-carnitine supplement to the user each day. 
     All of the disclosed methods and procedures described in this disclosure can be implemented using one or more computer programs or components. These components may be provided as a series of computer instructions on any conventional computer readable medium or machine-readable medium, including volatile and non-volatile memory, such as RAM, ROM, flash memory, magnetic or optical disks, optical memory, or other storage media. The instructions may be provided as software or firmware, and may be implemented in whole or in part in hardware components such as ASICs, FPGAs, DSPs, or any other similar devices. The instructions may be configured to be executed by one or more processors, which when executing the series of computer instructions, performs or facilitates the performance of all or part of the disclosed methods and procedures. 
     It should be understood that various changes and modifications to the examples described here will be apparent to those skilled in the art. Such changes and modifications can be made without departing from the spirit and scope of the present subject matter and without diminishing its intended advantages. It is therefore intended that such changes and modifications be covered by the appended claims. 
     EXAMPLES 
     Example 1: Dietary and Lifestyle Recommendations for Individuals at Risk of Developing Ovulatory Disorders 
     The following table describes the daily dietary recommendations for the individual&#39;s diet for individuals at risk of developing ovulatory disorders, including PCOS. 
     
       
         
           
               
               
               
               
               
             
               
                   
               
               
                   
                   
                 Supplements 
                   
                   
               
               
                 Diet and lifestyle 
                 Dietary 
                 or Diet (or 
               
               
                 components 
                 recommendation 
                 both) 
                 Tips for the consumer 
                 References 
               
               
                   
               
             
            
               
                 Caffeinated soda 
                 &lt;2 drinks/day 
                 Diet 
                 Women may benefit from 
                 1 
               
               
                 drinks 
                   
                   
                 drinking low amounts of 
               
               
                   
                   
                   
                 caffeinated soft drinks to 
               
               
                   
                   
                   
                 reduce the risk of 
               
               
                   
                   
                   
                 developing ovulatory 
               
               
                   
                   
                   
                 infertility such as PCOS. 
               
               
                 Carbohydrates 
                 &lt;54% of energy 
                 Diet 
                 Eating small amounts of 
                 2 
               
               
                   
                 and glycaemic 
                   
                 carbohydrates and 
               
               
                   
                 load &lt;114 
                   
                 preferring foods with low 
               
               
                   
                   
                   
                 glycaemic loads may 
               
               
                   
                   
                   
                 positively influence 
               
               
                   
                   
                   
                 ovulation. 
               
               
                 Low fat dairy 
                 ≥2 servings/day 
                 Diet 
                 High-fat dairy foods may 
                 3 
               
               
                 foods 
                   
                   
                 be better than low-fat 
               
               
                   
                   
                   
                 dairy foods to avoid 
               
               
                   
                   
                   
                 ovulation disorders. 
               
               
                 Energy 
                 n.a. 
                 Diet 
                 Athletes need to ensure 
                 4 
               
               
                   
                   
                   
                 energy balance to 
               
               
                   
                   
                   
                 maintain normal 
               
               
                   
                   
                   
                 ovulation. 
               
               
                 Folate 
                 at least about 
                 Diet and 
                 Women may benefit from 
                 5 
               
               
                   
                 735 μg/day 
                 supplement 
                 a high folate 
               
               
                   
                   
                   
                 consumption to avoid 
               
               
                   
                   
                   
                 sporadic anovulation. 
               
               
                   
               
            
           
         
       
     
     Example 2: Dietary and Lifestyle Recommendations for Individuals Diagnosed with an Ovulatory Disorder 
     The following table describes the daily dietary recommendations for the individual&#39;s diet for individuals who have been diagnosed with an ovulatory disorder. 
     These dietary recommendations improve pregnancy rates or intermediate reproductive outcomes in women with ovulatory disorders, including PCOS. 
     
       
         
           
               
               
               
               
               
             
               
                   
               
               
                   
                   
                 Supplements 
                   
                   
               
               
                 Diet and lifestyle 
                 Dietary 
                 or Diet (or 
               
               
                 components 
                 recommendation 
                 both) 
                 Tips for the consumer 
                 References 
               
               
                   
               
             
            
               
                 Caloric 
                 About 1200 kcal 
                 Diet 
                 Obese women with 
                  6-10 
               
               
                 restriction for 
                   
                   
                 PCOS should follow a 
               
               
                 obese 
                   
                   
                 weight loss diet as it may 
               
               
                 individuals 
                   
                   
                 improve symptoms of 
               
               
                   
                   
                   
                 PCOS. 
               
               
                 Caloric 
                 5-10% weight 
                 Lifestyle 
                 Lifestyle changes that 
                 11-30 
               
               
                 restriction and 
                 loss 
                   
                 result in sustained weight 
               
               
                 exercise for 
                   
                   
                 reduction may alleviate 
               
               
                 obese 
                   
                   
                 symptoms of PCOS. 
               
               
                 individuals 
               
               
                 Omega-3 PUFA 
                 Omega-3-PUFA- 
                 Diet 
                 Women with PCOS should 
                 31 32 
               
               
                 Vitamin D and 
                 500 to 2000 mg/day 
                   
                 follow a healthy diet with 
               
               
                 chromium 
                 Vitamin D in the 
                   
                 adequate fish consumption 
               
               
                   
                 dose of at least 
                   
                 or taking supplements for 
               
               
                   
                 about 50,000 IU 
                   
                 omega-3 PUFA, as well as 
               
               
                   
                 once per two 
                   
                 Vitamin D and chromium, if 
               
               
                   
                 weeks over 8 
                   
                 deficient. 
               
               
                   
                 weeks; 
               
               
                   
                 Chromium in the 
               
               
                   
                 dose of 200 mcg to 
               
               
                   
                 1000 mcg per day 
               
               
                 Inositol 
                 1.2 to 4 g myo- 
                 Supplement 
                 Inositol may improve 
                 21 33-40 
               
               
                   
                 inositol 
                   
                 menstrual cycles, ovulation 
               
               
                   
                 0.6 to 1.2 g of di- 
                   
                 and metabolic changes in 
               
               
                   
                 chiro-inositol 
                   
                 polycystic ovary syndrome. 
               
               
                   
                   
                   
                 Inositol as adjunct to low 
               
               
                   
                   
                   
                 dose gonadotropin may 
               
               
                   
                   
                   
                 improve pregnancy rates in 
               
               
                   
                   
                   
                 insulin-resistant females 
               
               
                   
                   
                   
                 with POCS. 
               
               
                 L-carnitine 
                 At least about 3 g 
                 Supplement 
                 L-carnitine may be 
                 41 
               
               
                   
                   
                   
                 beneficial as adjunct to 
               
               
                   
                   
                   
                 standard of care in women 
               
               
                   
                   
                   
                 suffering from PCOS. 
               
               
                 Vitamin D 
                 Up to 50,000 IU 
                 Diet and 
                 A healthy diet that avoids 
                 42-46 
               
               
                   
                 once weekly 
                 Supplement 
                 Vitamin D deficiency and/or 
               
               
                   
                   
                   
                 supplementation with 
               
               
                   
                   
                   
                 Vitamin D may be beneficial 
               
               
                   
                   
                   
                 for the ovarian reserve in 
               
               
                   
                   
                   
                 late reproductive aged 
               
               
                   
                   
                   
                 women. 
               
               
                   
               
            
           
         
       
     
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