System and method for providing dietary and lifestyle recommendations for improving fertility

Abstract

Methods and systems for generating evidence-based dietary and lifestyle recommendations are presented. In one embodiment, a method is provided that includes requesting and obtaining a plurality of user attributes. The method may further include comparing the user attributes to corresponding evidence-based fertility criteria and determining pregnancy support opportunities based on the comparison of the user attributes and the corresponding evidence-based fertility criteria. In one embodiment, the method may also include identifying fertility enhancement recommendations based on the fertility support opportunities and presenting at least one of the plurality of fertility enhancement recommendations.

Description

Technical Field

[0001]

Background Art

[0002]

[0001] Many couples of childbearing age have problems with infertility. Such problems are caused by physical or mental medical conditions, or may simply be caused by the passage of time because the risk of infertility increases with age. There are many physical and mental medical conditions that can affect fertility, such as excessive stress, sperm abnormalities, endometriosis, and so on. In some cases, couples may feel uncomfortable seeing a healthcare provider about the possibility of fertility problems, or may not even realize that they should see a healthcare provider. In such cases, if a couple hesitates to see a healthcare provider, their fertility may be further impaired by increased stress, tension between the couple, and the possibility of a delay in detecting other fertility-related medical conditions. In other cases, couples with fertility problems may need recommendations for changing their lifestyle or diet. Although not widely known, there is a strong relationship between a couple's lifestyle and nutritional choices and their fertility. Therefore, couples experiencing infertility need a system that provides customized and integrated recommendations for them throughout all stages, starting from the planning stage of pregnancy.

Summary of the Invention

[0003]

[0002] This disclosure presents novel and innovative methods and systems for individualized real-time diet and lifestyle recommendations for users desiring improved fertility. In one embodiment, a plurality of user attributes are requested and obtained, and the plurality of user attributes are compared with corresponding plurality of evidence-based fertility criteria, and based on the comparison between the plurality of user attributes and the corresponding plurality of evidence-based fertility criteria, a plurality of pregnancy support opportunities are determined, and based on the plurality of pregnancy support opportunities, a plurality of fertility improvement recommendations are identified, and at least one of the plurality of fertility improvement recommendations is presented.

[0004]

[0003] The features and advantages described herein are not comprehensive, and specifically, many additional features and advantages will become apparent to those skilled in the art upon consideration of the drawings and description. Further, it should be noted that the language used herein is selected solely for the purpose of readability and instruction, and does not limit the scope of the subject matter of the present invention.

Brief Description of the Drawings

[0005]

[0004]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6A

Figure 6B

Modes for Carrying Out the Invention

[0006]

[0010] To promote a user's fertility, it may be useful to provide customized plans for diet and lifestyle to users and couples interested in family planning. For example, even under similar circumstances, different users may experience different deficiencies and fertility levels. Therefore, a customized and integrated approach is essential to provide the greatest benefit to a couple's chance of pregnancy. One way to provide this level of customization is to obtain information from the patient regarding certain relevant attributes and the current status of pregnancy, compare it with a past evidence-based fertility database, and create recommended diet and lifestyle options to improve the patient's fertility based on the information provided.

[0007]

[0011] It may be beneficial if an example of the system can provide family support throughout the pregnancy process from the initial stage to the final stage of pregnancy planning. Therefore, this example of the system would be useful if it can provide constant 24-hour access to virtual coaches and personal coaches for pregnancy, lifestyle, nutrition, and exercise. Further, an example of the system may provide recommendations for dealing with anxiety, reducing stress, or providing specific nutritional supplementation methods (all of which are also related to the user's fertility).

[0008]

[0012] FIG. 1 shows 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 with which the relevant user can communicate with the recommendation system 104. The user device 102 may also be implemented as a voice assistant configured to, for example, obtain voice requests from the user and process the requests on a computer device near the user or on a remote computing device (e.g., a remote computing server).

[0009]

[0013] The recommendation system 104 includes one or more of a display 106, an attribute acquisition unit 108, an attribute comparison unit 110, an evidence-based diet and lifestyle recommendation engine 112, an attribute analysis unit 114, an attribute storage unit 116, a memory 118, and a CPU 120. It should be noted that in some embodiments, the display 106 may additionally or alternatively be disposed within the user device 102. In one example, the recommendation system 104 may be configured to obtain requests for a plurality of pregnancy improvement recommendations 140. For example, the user may install an application on the user device 102 that requests registration with the recommendation service. By registering for this service, the user device 102 may send a request for the pregnancy improvement recommendation 140. In another 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 request the pregnancy improvement recommendation from the recommendation system 104 to be sent to the user device 102.

[0010]

[0014] In another example, the recommendation system 104 may be configured to request and obtain 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 acquisition unit 108 may be configured to acquire the user attributes 122. In one example, the attribute acquisition unit 108 may acquire a plurality of responses 126 based on the attribute questionnaire 124 and determine a plurality of user attributes 122 based on the plurality of responses. For example, the attribute acquisition unit 108 may acquire a response in the attribute questionnaire 124 indicating that the user's diet is equivalent to the recommended dietary allowance (RDA), and then determine that the user attribute 122 is equivalent to the RDA such as 100 mg / day of vitamin C. In another example, the user device attribute acquisition unit 108 may directly acquire the user attributes 122 from the user device 102.

[0011]

[0015] In another example, the attribute acquisition unit 108 may be configured to acquire the test results of a home test kit, the results of a standard health examination performed 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. The attribute acquisition unit 108 may be configured to determine the user attribute 122 based on the results from any of these tests or tools.

[0012]

[0016] The recommendation system 104 may be further configured to compare a plurality of user attributes 122 with a corresponding plurality of fertility criteria 128 that are evidence-based. For example, the attribute comparison unit 110 may be configured to determine a user fertility classification 130. In one example, the user fertility classification 130 may be any of a person who is planning and anxious, a person who is planning and healthy, a person who is having difficulty in attempting pregnancy, a person who is healthy during the attempt of pregnancy. In this example, a person who is planning and anxious refers to a classification where the user has a medical problem related to the health of fertility and is currently in the family planning stage. A person who is planning and healthy refers to a classification where the user has a healthy fertility constitution and is currently considered to be in the family planning stage. A person who is having difficulty in attempting pregnancy refers to a classification where the user has a medical problem related to the health of fertility and is currently actively attempting pregnancy. A person who is healthy during the attempt of pregnancy refers to a classification where the user has a healthy fertility constitution and is currently considered to be actively attempting pregnancy.

[0013]

[0017] In another example, the user fertility classification may be more specific than this. For example, some user fertility classifications may include those with abnormal semen, low testosterone, endometriosis, high body mass index (BMI), or any other indication that can be a factor in determining the user fertility classification.

[0014]

[0018] Furthermore, the attribute comparison unit 110 may be further configured to determine a set of fertility criteria 132 based on the user fertility classification 130. For example, if the attribute comparison unit 110 determines, based on a plurality of user attributes 122, that a user falls into the user fertility classification 130 of a person who is planning and healthy, the attribute comparison unit 110 may select a set of fertility criteria 132 created and defined according to the specific needs of a person who is planning and healthy. In another example, the comparison unit 110 may select a different set of fertility criteria 132 if the user is determined to be a person who is in the process of pregnancy trial and healthy. In yet another example, the comparison unit 110 may select a set of fertility criteria 132 corresponding to a user who is receiving a specific medical treatment such as in vitro fertilization ("IVF").

[0015]

[0019] The comparison unit 110 may be further configured to select a plurality of evidence-based fertility criteria 128 from the determined set of fertility criteria 132 and compare the selected evidence-based fertility criteria 128 with each of the corresponding user attributes 122. For example, when the set of fertility criteria 132 is determined, in response to that determination, the attribute comparison unit 110 compares a user attribute 122 representing the user's vitamin C intake with an evidence-based fertility criterion 128 representing a reference vitamin C intake, and may determine whether the user's intake is less than, equal to, or more than the reference vitamin C intake. This example is based on a concrete numerical comparison, but another example of a reference comparison is qualitative and may vary from person to person. For example, the user attribute 122 may indicate that the user is currently under more stress than normal levels. An example of a criterion related to the user's stress level may indicate that an average or low level of stress is desirable, and thus, a user attribute 122 indicating a higher level of stress is determined to be below that criterion. Since different users experience different levels of stress, even under the same circumstances, such comparisons require a customized approach.

[0016]

[0020] Furthermore, during the comparison in the aforementioned example, the attribute comparison unit 110 may be configured to determine a user pregnancy score 134 based on a comparison between the evidence-based pregnancy criteria 128 and the user attributes 122. For example, if the user attributes 122 almost completely meet all or most of the corresponding pregnancy criteria 128 that are evidence-based, the attribute comparison unit 110 may determine a user pregnancy score of 95 / 100. In another example, the score may be represented in a letter grade, symbol, or other ranking system so that the user can interpret how their current attributes are ranked among the criteria. This user pregnancy score 134 may be presented through the display 106.

[0017]

[0021] The recommendation system 104 may be further configured to determine a plurality of pregnancy support opportunities 138 based on a comparison between a plurality of user attributes 122 and the corresponding plurality of evidence-based pregnancy criteria 128. In one example, the attribute comparison unit 110 may determine a pregnancy support opportunity 138 for all user attributes 122 that do not meet the corresponding evidence-based pregnancy criteria. In this example, the corresponding evidence-based pregnancy criteria 128 may require the user to consume 500 mg / day of vitamin C, while the user attributes may indicate that the user is only consuming 200 mg / day of vitamin C. Accordingly, the attribute comparison unit 110 may determine an increase in vitamin C intake as the pregnancy support opportunity 138.

[0018]

[0022] In another example, the attribute comparison unit 110 may be configured to identify a first set 136 of user attributes consisting of each of a plurality of user attributes 122 that are less than the corresponding criteria among a plurality of evidence-based pregnancy criteria 128, and a second set 136 of user attributes consisting of each of a plurality of user attributes 122 that are greater than or equal to the corresponding evidence-based pregnancy criteria 128. The first set 136 of user attributes is determined in the same manner as the example given above, but the second set 136 of user attributes is different in that, although the associated user does not appear to have deficiencies, there may be an opportunity to support pregnancy by recommending that the user maintain current practices, or an opportunity to further improve current practices. Accordingly, the recommendation system 104 may determine an opportunity to support pregnancy based on which attributes 122 fall into which set 136.

[0019]

[0023] The recommendation system 104 may be further configured to identify a plurality of pregnancy improvement recommendations 140 based on a plurality of pregnancy support opportunities 138. For example, the evidence-based diet and lifestyle recommendation engine 112 may be cloud-based. The recommendation engine 112 may include one or more of a plurality of databases 142, a plurality of diet restriction filters 144, and an optimization unit 146. The recommendation engine 112 may identify a plurality of pregnancy improvement recommendations 140 according to one or more of the plurality of databases 142, diet restriction filters 144, and optimization unit 146 based on the plurality of opportunities 138.

[0020]

[0024] In another example, the recommendation system 104 may be configured to provide ongoing recommendations based on previous user attributes. For example, in addition to the aforementioned elements, the recommendation system 104 may include an attribute storage unit 116 and an attribute analysis unit 114. In response to the attribute acquisition unit 108 acquiring a plurality of user attributes 122, the attribute storage unit 116 may be configured to add the acquired user attributes 122 as new entries to the attribute history database 148 based on when the plurality of user attributes 122 were acquired. For example, if the user attribute 122 is acquired by the attribute acquisition unit 108 on the first day, the attribute storage unit 116 adds the acquired user attribute 122 to the cumulative attribute history database 148 with the entry date noted, which in this example is the first day. Thereafter, when the user attribute 122 is acquired by the attribute acquisition unit 108 on the second day, e.g., the next day, the attribute storage unit 116 further adds these new attributes to the attribute history database 148 with the note that they were acquired on the second day, while also preserving the attributes from the previous first day.

[0021]

[0025] This attribute analysis unit 114 may be configured to analyze a plurality of user attributes 122 stored in the attribute history database 148, and analyzing the stored plurality of user attributes 122 includes conducting a long-term survey 150. Continuing with the above example, the attribute analysis unit 114 may conduct a long-term survey of the user attributes 122 from each of the sets of user attributes 122 from the first day, from the second day, and all other user attributes 122 found in the attribute history database 148. The evidence-based diet and lifestyle recommendation engine 112 may be further configured to generate a plurality of fertility improvement recommendations 140 based at least on the stored user attributes 122 found in the attribute history database 148 and the analysis performed by the attribute analysis unit 114.

[0022]

[0026] In one embodiment, the attribute analysis unit 114 repeatedly analyzes a plurality of user attributes 122 stored in the attribute history database 148 in response to the attribute storage unit 116 adding a new entry to the attribute history database 148, and is further configured to re-analyze all of the data in the attribute history database 148 virtually immediately after a new user attribute 122 is obtained. Similarly, the evidence-based diet and lifestyle recommendation engine 112 may be further configured to repeatedly generate a plurality of pregnancy improvement recommendation cases 140 in response to the attribute analysis unit 114 completing the analysis, thereby effectively generating a new pregnancy improvement recommendation case 140 taking into account all past and current user attributes 122 each time a new set of user attributes 122 is obtained.

[0023]

[0027] FIG. 2 shows an exemplary database including a plurality of user attributes 122. For example, the user attributes 122 include information regarding one or more of age 202, gender 204, weight 206, height 208, activity level 210, food allergies 212, dietary preferences 214, pregnancy status 216, pregnancy-related medical conditions 218, co-existing conditions 220, and lifestyle choices 222. Some examples of food allergies 212 include lactose, egg, nut, shellfish, soy, fish, and gluten allergies. Some non-limiting examples of dietary preferences 214 include vegetarian, vegan, Mediterranean, kosher, halal, paleo, low-carbohydrate, and low-fat diets. Some non-limiting examples of pregnancy-related medical conditions 218 include polycystic ovary syndrome, premature ovarian insufficiency, endometriosis, recurring pregnancy loss, during IVF treatment, semen abnormalities, misuse of anabolic steroids and protein supplements, erectile dysfunction, hormonal imbalance, low testosterone, and prostate problems. Some non-limiting examples of co-existing conditions 220 include diabetes, obesity, hypertension, high cholesterol, celiac disease, and heartburn. Some non-limiting examples of lifestyle choices 222 may include sleep habits such as normal nighttime sleep duration, stress attributes such as the level of stress the current user is experiencing or the level of stress the user normally experiences, whether the user smokes, the number of alcoholic beverages the user normally consumes, the frequency of exercise, or any other lifestyle choice 222 that may have affected the pregnancy status.

[0024]

[0028] Figure 3 shows an exemplary embodiment of the evidence-based diet and lifestyle recommendation engine 112. In one exemplary embodiment, the evidence-based diet and lifestyle recommendation engine 112 includes a plurality of databases 142, a plurality of diet filter restrictions 144, and an optimization unit 146. The plurality of databases 142 may include databases consisting of one or more of recipes 302, food items 304, food products 306, and diet tips 308. The diet filter restrictions 144 may include filters for one or more of food allergies 310, diet preferences 312, pregnancy-related conditions 314, and co-existing diseases 316. The optimization unit 146 may include optimization rules based on one or more of calorie intake 318, food groups 310, and specific nutrients 312.

[0025]

[0029] Figure 4 shows examples of a plurality of diet and lifestyle recommendations according to an exemplary embodiment of the present disclosure. This example 400 of diet recommendations shows the details of specific recommendations, which may be presented to the user after a plurality of pregnancy improvement recommendations 140 are determined by the recommendation system 104. Specifically, example 400 shows the details of the pregnancy improvement recommendations 140 determined for a user having a specific pregnancy-related medical condition 218. Specifically, example 400 shows the pregnancy improvement recommendations determined for a user having low testosterone. As seen in Figure 4, an example 140 of a pregnancy improvement recommendation may include a recommendation for a specific amount of nutrient per day, such as 10 mg / day of boron, over a period of 4 weeks. Further, another recommendation 140 may include a certain amount of a specific food item, such as 500 grams to 600 grams / day of fenugreek over a period of 6 - 8 weeks. Other recommendations 140 may simply avoid or increase the intake of specific food items, such as in the case of processed meat, bread and pastry, dairy products, desserts, trans fats, and dietary fiber. Similarly, the recommendations 140 may include recommendations for moderate intake of a substance, or recommendations for preferentially choosing one substance over another. Although many different types of recommendations 140 are seen in example 400, it should be understood that any type of qualitative or quantitative recommendation may be made regarding these food items and nutrients.

[0026]

[0030] Furthermore, the recommendation system 400 may generate pregnancy improvement recommendations 140 that include changes in lifestyle habits such as changes in activity levels, increased number of hours of rest at night, stress reduction actions, or similar actions that affect lifestyle habits. For example, high levels of stress can have an adverse effect on a user's pregnancy. Such stress may stem from the relationship between partners who are actively trying to conceive. Some examples 140 of pregnancy improvement recommendations may include suggestions on how a couple can reduce the tension in their relationship in order to reduce stress. In another example, the pregnancy improvement recommendations 140 may include recommendations for increasing the time the user rests, including recommendations for sleep habits. These recommendations may range from general recommendations such as instructions to get more sleep to more detailed recommendations including specific exercise routines, specific meals and recipes, or suggestions for days to visit a healthcare provider. In another example, the pregnancy improvement recommendations 140 may include recommendations for hydration and recommendations for avoiding various types of toxins in the environment (such as water, food, products, air, etc.).

[0027]

[0031] Furthermore, in another embodiment, the fertility improvement recommendation 140 generated by the recommendation system 104 may include specific recommendations for products. For example, the recommendation system 104 may access a database containing information about various nutritional supplements in the market. The recommendation system 104 may then analyze multiple different options of a specific nutritional supplement, such as vitamin C, based on its own analysis or through the use of third-party research, and determine that a specific 500 mg nutritional supplement from a first brand, namely brand A, is the most beneficial nutritional supplement compared to other 500 mg vitamin C nutritional supplements available from a second brand, a third brand, and a fourth brand. Such an analysis may be based on the quality of the nutritional supplement, the price of the nutritional supplement, known side effects, manufacturing methods, or any other factor that can distinguish the nutritional supplement provided by one brand from that provided by another brand. The recommendation system 104 may provide similar recommendations for food items such as a specific type or brand of apples, and also for any other category of products for which a user may need to select one of the multiple available options. The recommendation system 104 may also provide recommendations for products or foods through a custom diet plan or diet plan recipe, but may also provide a connection to a platform for food ordering and delivery (e.g., Grubhub).

[0028]

[0032] FIG. 5 shows an exemplary embodiment of method 500 of the method of the present disclosure, as described above in connection with system 100. Method 500 may be implemented within a system such as system 100 or on a CPU. For example, this method may be implemented by one or more of attribute acquisition unit 108, attribute analysis unit 114, attribute storage unit 116, attribute comparison unit 110, evidence-based diet and lifestyle recommendation engine 112, or user device 102. Method 500 may also be implemented by a set of instructions stored on a computer-readable medium that, when executed by a processor, causes the computer system to perform this method. For example, all or part of method 500 may be implemented by CPU 120 and memory 118. The following examples are described with reference to the flowchart shown in FIG. 5, but many other methods that perform the operations 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 described blocks may be optional.

[0029]

[0033] Block 502 can include requesting and obtaining a plurality of user attributes 122. For example, the display 106 may present an attribute questionnaire 124 that asks for an answer 126, to which the user device 102 provides an answer 126, which is then selected as the user attribute 122. At block 504, a comparison may occur between the plurality of user attributes 122 and the corresponding plurality of fertility criteria 128 that are evidence-based. At block 506, based on these comparisons, a plurality of pregnancy support opportunities 138 can be determined based on the comparison between the plurality of user attributes 122 and the corresponding plurality of fertility criteria 128 that are evidence-based. At block 508, an embodiment of method 500 may identify a plurality of fertility improvement recommendations 140 based on the plurality of pregnancy support opportunities 138. For example, the evidence-based diet and lifestyle recommendation engine 112 may include a cloud-based system trained to interpret pregnancy support opportunities and provide recommendations 140. Finally, at block 510, at least one of the plurality of fertility improvement recommendations 140 can be presented.

[0030]

[0034] Figures 6A and 6B disclose an embodiment example of method 600 of the method of the present disclosure. Method 600 may be implemented within a system such as system 100 or on a CPU. For example, this method may be implemented by one or more of the attribute acquisition unit 108, the attribute analysis unit 114, the attribute storage unit 116, the attribute comparison unit 110, the evidence-based diet and lifestyle recommendation engine 112, or the user device 102. Method 600 may also be implemented by a set of instructions stored on a computer-readable medium that, when executed by a processor, causes the computer system to perform this method. For example, all or part of method 600 may be implemented by the CPU 120 and the memory 118. The following examples are described with reference to the flowchart shown in FIG. 6, but many other methods that perform the operations related to 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 described blocks may be optional.

[0031]

[0035] Block 602 may include obtaining a request for a plurality of pregnancy improvement recommendations 140. For example, the user may submit a request for the pregnancy improvement recommendation 140 through several methods, including opening an application on the user device 102, making an official request through an application on the user device 102, submitting a regular request for the pregnancy improvement recommendation 140 through the user device 102, registering for an online account through a web browser, making an official request through a web browser, or submitting a regular request for the pregnancy promotion plan 140 through the web browser.

[0032]

[0036] At block 604, the recommendation system 104 may request and obtain 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 customized based on known preliminary attributes or based on answers to previous questions. In another example, the recommendation system 104 may request a plurality of user attributes 122 by providing a list of available home test kits such as a testosterone test kit that the user can use at home. Then, after the test is performed, the recommendation system 104 may obtain the results from the testosterone test and determine the user attributes 122 related to such a test based on these results.

[0033]

[0037] In another example, at block 604, the recommendation system 104 may provide a self-assessment tool. Similar to the previous example, the user may use this self-assessment tool and submit the results to the recommendation system 104. Again, based on the obtained 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 to undergo a standard health examination performed by a medical professional. In this example, the results of this performed health examination may be submitted to the recommendation system 104, whereby the recommendation system 104 determines the user attributes 122 based on the results. Although some specific examples regarding external tests have been shown, these examples are non-limiting as the recommendation system 104 may be configured to obtain the results of any external or third-party test to determine the corresponding user attributes 122.

[0034]

[0038] At block 606, the recommendation system 104 may be configured to compare a plurality of user attributes 122 with a corresponding plurality of evidence-based fertility criteria 128. For example, these evidence-based fertility criteria 128 may include standardized criteria as criteria given to all regardless of individual differences. In another example, these criteria 128 may be customized based on the history or goals of a particular user. For example, if a healthy user is trying to improve their fertility and the current user attributes 122 exceed all standard evidence-based fertility criteria 128, the recommendation system 104 may be configured to determine a customized set of fertility criteria 132 that the particular user should aim for. In contrast, in another example, if a user different from the above example is far below the standard evidence-based fertility criteria 128, this user may be compared with a lower different criterion value as a way to encourage progress and provide milestones.

[0035]

[0039] This example method may be configured at block 608 to determine a plurality of pregnancy support opportunities 138 based on a comparison of a plurality of user attributes 122 and a corresponding plurality of evidence-based fertility criteria 128. For example, the recommendation system 104 may determine that the user attribute 122 corresponds to a stress level higher than optimal. Based on this comparison, the recommendation system 104 may determine a pregnancy support opportunity 138 to reduce stress. In another example, the recommendation system 104 may determine that the user has not yet visited a healthcare provider and thus may determine a pregnancy support opportunity 138 to visit a healthcare provider.

[0036]

[0040] At block 610, the recommendation system 104 may identify a plurality of fertility enhancement recommendation cases 140 based on at least a plurality of pregnancy support opportunities 138. For example, the recommendation system may analyze the attribute history database 148 and determine a plurality of similar prior cases by identifying the similarity between the obtained user attributes 122 and the plurality of prior user attributes in the attribute history database 148. For example, the recommendation system 104 may identify that the user attributes 122 detail a user having a BMI higher than average and other similarities corresponding to a specific past user group, and thus, the cases of the members of that specific past user group are determined as similar prior cases.

[0037]

[0041] Further, 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 above, the attribute history database 148 may include corresponding recommendations related to the prior user attributes and the effectiveness of these corresponding recommendations. Thus, the recommendation system 104 may analyze the corresponding recommendations related to that specific past user group and their effectiveness to determine a plurality of prior case results.

[0038]

[0042] Furthermore, in this example, the recommendation system 104 may determine successful recommendations and a plurality of unsuccessful recommendations based on a plurality of precedent case results. For example, the recommendation system 104 may have recommended to users in that particular past user group to increase the exercise level in one case and to reduce food intake in other cases. Based on the precedent case results determined based on the attribute history database 148, the recommendation system 104 may determine that the recommendation to reduce food intake was not very successful but increasing the exercise level was very successful, and thus, it may be determined that increasing the exercise level is a successful recommendation while reducing food intake is an unsuccessful recommendation. By analyzing these precedent user attributes, selected recommendations, and the effectiveness of the corresponding recommendations, the recommendation system 104 may identify trends related to different subsets of the patient population, and thereby create and verify a plurality of lifestyle intervention plans. Since different groups may experience different levels of success for the same recommendation, these examples of successful and unsuccessful recommendations are non-limiting.

[0039]

[0043] Furthermore, the recommendation system 104 may be configured to determine a plurality of pregnancy improvement recommendations based on a plurality of successful recommendations and a plurality of unsuccessful recommendations. For example, the recommendation system 104 may be configured to recommend only a 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 such recommendations based on any number of reasons, including that the difference in user attribute 122 when compared to the precedent user attributes is small, the lack of insufficient data that would be the basis for a true unsuccessful recommendation, or that the recommendation, although it failed, was popular and often practiced by users. In another example, the recommendation system 104 may recommend only some, but not all, of the plurality of successful recommendations. In one example, the determination of which of the plurality of recommendations to present may be made by AI.

[0040]

[0044] In another example, the recommended actions may be based on guidelines related to a specific medical condition, such as a user undergoing IVF treatment. In that case, these guidelines would be determined as the recommended actions.

[0041]

[0045] At block 612, the recommendation system may present at least one of the plurality of fertility improvement recommendations 140. At block 614, the recommendation system 104 may obtain a selected recommendation, selected from at least one of the presented plurality of fertility improvement recommendations 140. For example, for a certain user, three fertility improvement recommendations 140 such as reducing alcohol consumption, increasing exercise, and increasing fruit intake may be presented. The user may select one, two, or all three of these options. In one example, the user may use the user device 102 to select the fertility improvement recommendations 140 of increasing exercise and increasing fruit intake. Thus, the recommendation system 104 obtains these two selected recommendations from the user device 102 as the selected recommendations. In another example, the user may not select any of the presented recommendations, and at that point, the recommendation system 104 may generate and present another plurality of fertility improvement recommendations 140.

[0042]

[0046] In another example, after the user has considered the presented fertility improvement recommendations 140, the user may submit a request to contact a pregnancy coach. For example, the user may be unable to decide on how to implement the recommendations, or there may simply be a question for which the user is seeking an answer. In some cases, the recommendation system 104 may determine that the question the user is seeking an answer to can be appropriately answered by a virtual coach, and thereby provide access to and interaction with the virtual coach. In other cases, the recommendation system 104 may determine that the question is best handled by a personal coach, i.e., a live individual, and thereby provide access to and interaction with the personal coach.

[0043]

[0047] In block 616, the recommendation system 104 may store the plurality of user attributes 122 and the selected recommendation proposals in the attribute history database 148. For example, the recommendation system 104 may store all the user attributes 122 obtained on the first day together with the selected recommendations obtained on the same first day. These user attributes 122 and selected recommendation proposals may then be accessed by the recommendation system 104 in the future when analyzing the attribute history database 148.

[0044]

[0048] In block 618, the recommendation system 104 may obtain at least one recommendation result. In one example, the user may submit a recommendation result via the user device 102. This result may include qualitative or quantitative evaluations selected by the user. In another example, the recommendation system 104 may obtain a plurality of future user attributes 122, and at that time, compare the obtained future user attributes with the previously obtained user attributes in the attribute history database 148, that is, the current preceding user attributes. Based on this comparison, the recommendation system 104 may determine a recommendation result such as a decrease or increase in BMI. After obtaining this recommendation result, the recommendation system 104 may store at least one recommendation result in the attribute history database 148 corresponding to the previous selected recommendation proposal. The recommendation system 104 may then wait for another request for the pregnancy improvement recommendation 140, and at that time, may perform method 600 again at block 602.

[0045]

[0049] In the example of the method as disclosed in FIGS. 6A and 6B, the continuous and customized integrated recommendation system 104 can be improved indefinitely with respect to recommendations as the size of the attribute history database 148 increases. During this increase, the recommendation system 104 may also, in some embodiments, the evidence-based diet and lifestyle recommendation engine 112 has a continuously expanding set of data from which the pregnancy improvement recommendation 140 can be derived while increasing the particularity regarding which user obtains which recommendation.

[0046]

[0050] In another aspect, a treatment method may include using any of the systems or methods described above to generate one or more of the pregnancy improvement recommendation 140, diet and lifestyle recommendations, or specific nutritional supplementation recommendations. Further, the treatment method may include treating a user based at least on one or more of the pregnancy improvement recommendation 140, diet and lifestyle recommendations, or specific nutritional supplementation recommendations. For example, if the recommendation system 104 determines a pregnancy improvement recommendation 140 that includes increasing a user's vitamin C intake from 200 mg / day to 500 mg / day by a 300 mg vitamin C dietary supplement, an example of the treatment method may include treating the user daily with a treatment that includes the 300 mg vitamin C dietary supplement.

[0047]

[0051] 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 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, and any other similar devices. The instructions may be configured to be performed by one or more processors that, when executing the series of computer instructions, perform all or part of the methods and procedures of this disclosure or facilitate their performance.

[0048]

[0052] It should be understood that various changes and modifications to the embodiments described herein will be apparent to those skilled in the art. Such changes and variations can be made without departing from the spirit and scope of the subject matter of the present invention and without sacrificing the intended advantages. Therefore, such changes and variations are intended to be included within the scope of the appended claims. The method and system of the present disclosure may have the following configurations. [Claim 1] A step of requesting and obtaining a plurality of user attributes; A step of comparing the plurality of user attributes with corresponding plurality of fertility criteria that are evidence-based; A step of determining a plurality of pregnancy support opportunities based at least on the comparison between the plurality of user attributes and the corresponding plurality of fertility criteria that are evidence-based; A step of identifying a plurality of fertility improvement recommendations based at least on the plurality of pregnancy support opportunities; Including a step of presenting at least one of the plurality of fertility improvement recommendations, A method for supporting a family's pregnancy. [Claim 2] The method according to claim 1, wherein the plurality of user attributes are obtained from a standard health examination. [Claim 3] The method according to claim 2, wherein the standard health examination includes the use of a home health kit. [Claim 4] The method according to claim 2, wherein the standard health examination is performed by a medical professional. [Claim 5] The step of requesting and obtaining the plurality of user attributes includes A step of presenting an attribute questionnaire; A step of obtaining a plurality of answers based on the attribute questionnaire; The method according to claim 1, including a step of determining the plurality of user attributes based at least on the plurality of answers. [Claim 6] The method according to claim 5, wherein the attribute questionnaire is customized based on a plurality of preliminary attributes. [Claim 7] The method according to claim 5, wherein the attribute questionnaire is repeatedly customized for a certain user based on a plurality of previously obtained questionnaire answers. [Claim 8] The method according to claim 1, wherein the plurality of user attributes include information related to at least one of age, gender, weight, height, activity level, food allergies, food preferences, fertility constitution, lifestyle choices, any fertility-related medical conditions, and any co-existing diseases. [Claim 9] The step of comparing the plurality of user attributes with corresponding plurality of fertility criteria that are evidence-based includes A step of determining a user fertility classification based at least on the plurality of user attributes; A step of determining a set of fertility criteria based at least on the user's fertility classification; A step of selecting the set of fertility criteria as the corresponding plurality of fertility criteria that are evidence-based; comparing each of the plurality of user attributes with a corresponding one of the plurality of evidence-based fertility criteria; The method according to claim 1. [Claim 10] the fertility classification of the user is determined as any one of a person planning and anxious, a person planning and healthy, a person having difficulty in trying to conceive, and a person healthy in trying to conceive; a person planning and anxious means a classification representing that the user has a medical problem related to fertility health and is currently in the family planning stage; a person planning and healthy means a classification representing that the user has a healthy fertility constitution and is currently considered to be in the family planning stage; a person having difficulty in trying to conceive means a classification representing that the user has a medical problem related to fertility health and is currently actively trying to conceive; a person healthy in trying to conceive means a classification representing that the user has a healthy fertility constitution and is currently considered to be actively trying to conceive; The method according to claim 9. [Claim 11] The method according to claim 10, wherein the step of comparing the plurality of user attributes with corresponding plurality of evidence-based fertility criteria further includes determining a fertility score of the user based at least on the comparison. [Claim 12] The step of determining the plurality of pregnancy support opportunities includes: identifying a first set of user attributes consisting of each of the plurality of user attributes that are less than the corresponding criteria among the plurality of evidence-based fertility criteria; identifying a second set of user attributes consisting of each of the plurality of user attributes that are equal to or greater than the corresponding evidence-based fertility criteria; determining the plurality of pregnancy support opportunities based at least on the identified first set of user attributes and the second set of user attributes; The method according to claim 1. [Claim 13] The step of determining the plurality of pregnancy support opportunities based at least on the identified first set of user attributes and the second set of user attributes includes: for each of the first set of user attributes, identifying an opportunity to improve the relevant user attribute; for each of the second set of user attributes, identifying an opportunity to maintain or improve the relevant user attribute; The method according to claim 12. [Claim 14] The step of identifying a plurality of fertility improvement recommendations based at least on the plurality of pregnancy support opportunities includes: providing the plurality of pregnancy support opportunities to a cloud-based artificial intelligence service; obtaining, from the cloud-based artificial intelligence service, a plurality of pregnancy improvement recommendations based on the pregnancy support opportunities provided to the cloud-based artificial intelligence service; The method according to claim 1. [Claim 15] wherein presenting at least one of the plurality of pregnancy improvement recommendations includes: determining a set of recommendations that can be presented based at least on the obtained pregnancy improvement recommendations; presenting the set of recommendations that can be presented; The method according to claim 1. [Claim 16] a memory; a CPU; a display configured to present an attribute questionnaire to a user; an attribute acquisition unit configured to obtain a plurality of user attributes based at least on the attribute questionnaire; an attribute comparison unit configured to compare the obtained plurality of user attributes with a corresponding plurality of pregnancy attribute criteria; an evidence-based diet and lifestyle recommendation engine configured to generate a plurality of pregnancy improvement recommendations based at least on the comparison of the plurality of user attributes and the corresponding plurality of pregnancy criteria; wherein the display is further configured to present at least one of the plurality of pregnancy improvement recommendations to the user; A system for generating pregnancy improvement promotion plans. [Claim 17] The system according to claim 16, wherein the plurality of user attributes includes information regarding at least one of age, gender, weight, height, activity level, food allergies, food preferences, pregnancy constitution, lifestyle choices, any pregnancy-related medical conditions, and any co-existing conditions. [Claim 18] wherein the attribute comparison unit: determines a user pregnancy classification based at least on the plurality of user attributes; determines a set of pregnancy criteria based at least on the user's pregnancy classification; is further configured to select the set of pregnancy criteria as the corresponding plurality of pregnancy criteria that are evidence-based; The system according to claim 16. [Claim 19] wherein the attribute comparison unit is further configured to determine the user pregnancy classification as any one of a person planning and anxious, a person planning and healthy, a person having difficulty during pregnancy attempts, and a person healthy during pregnancy attempts; Those who are in the planning stage and have concerns refer to the category where the user has medical problems related to reproductive health and is currently in the family planning stage. Those who are in the planning stage and are healthy refer to the category where the user has a healthy reproductive constitution and is currently considered to be in the family planning stage. Those who are having difficulties during pregnancy attempts refer to the category where the user has medical problems related to reproductive health and is currently actively attempting pregnancy. Those who are healthy during pregnancy attempts refer to the category where the user has a healthy reproductive constitution and is currently considered to be actively attempting pregnancy. The system according to claim 17. [Claim 20] The system according to claim 16, wherein the attribute comparison unit is further configured to determine a user's reproductive score based at least on the comparison. [Claim 21] The evidence-based diet and lifestyle recommendation engine A plurality of databases including one or more of recipes, specific food items, products, and diet tips, A plurality of filters based on dietary restrictions including one or more of food allergies, dietary preferences, reproductive-related conditions, and co-existing diseases, And an optimization unit configured to optimize the plurality of reproductive improvement recommendations based on one or more of calorie intake, food groups, and nutrients. The system according to claim 16. [Claim 22] The system according to claim 16, wherein the evidence-based diet and lifestyle recommendation engine is configured as a cloud-based AI. [Claim 23] The steps of requesting and obtaining a plurality of user attributes and behaviors, Comparing the plurality of user attributes and behaviors with the attributes and behaviors of a plurality of reproductive criteria, Determining a reproductive evaluation based at least on the comparison, And presenting the reproductive evaluation. A method for evaluating an individual's reproductive ability. [Claim 24] The method according to claim 23, wherein the plurality of user attributes and behaviors include information regarding one or more of age, gender, weight, height, activity level, food allergies, dietary preferences, reproductive constitution, lifestyle choices, any reproductive-related medical conditions, and any co-existing diseases. [Claim 25] The step of comparing the plurality of user attributes and behaviors with the attributes and behaviors of a plurality of reproductive criteria Determining a user reproductive category based at least on the plurality of user attributes, Determining a set of reproductive criteria based at least on the user's reproductive category. Selecting the set of fertility criteria as the corresponding plurality of evidence-based fertility criteria; comparing each of the plurality of user attributes with a corresponding one of the plurality of evidence-based fertility criteria. The method according to claim 23. [Claim 26] The fertility classification of the user is determined as any one of a person who is planning and anxious, a person who is planning and healthy, a person who is having difficulty in attempting pregnancy, and a person who is healthy in attempting pregnancy; A person who is planning and anxious means that the user has a medical problem related to fertility health and currently represents a category at the family planning stage; A person who is planning and healthy means that the user has a healthy fertility constitution and is currently regarded as being at the family planning stage; A person who is having difficulty in attempting pregnancy means that the user has a medical problem related to fertility health and currently represents a category actively attempting pregnancy; A person who is healthy in attempting pregnancy means that the user has a healthy fertility constitution and is currently regarded as actively attempting pregnancy. The method according to claim 23. [Claim 27] A memory; A CPU; An attribute acquisition unit configured to acquire a plurality of diet and lifestyle attributes; In response to the attribute acquisition unit acquiring the plurality of diet and lifestyle attributes, an attribute storage unit configured to add the plurality of diet and lifestyle attributes as a new entry to an attribute history database based at least on when the plurality of diet and lifestyle attributes were acquired; An attribute analysis unit configured to analyze the plurality of diet and lifestyle attributes stored in the attribute history database, wherein analyzing the stored plurality of diet and lifestyle attributes includes conducting a long-term survey; An evidence-based diet and lifestyle recommendation engine configured to generate a plurality of fertility improvement recommendations based at least on the stored plurality of diet and lifestyle attributes and the analysis performed by the attribute analysis unit; A display configured to present at least one of the plurality of fertility improvement recommendations. The attribute analysis unit is further configured to repeatedly analyze the plurality of diet and lifestyle attributes stored in the attribute history database in response to the attribute storage unit adding the new entry to the attribute history database. The evidence-based diet and lifestyle recommendation engine is further configured to repeatedly generate the plurality of fertility improvement recommendation plans in response to the attribute analysis unit completing the analysis. A continuously available system for generating fertility improvement recommendation plans. [Claim 28] The system according to claim 27, wherein the plurality of diet and lifestyle attributes include information related to one or more of age, gender, weight, height, activity level, food allergies, food preferences, fertility constitution, any fertility-related medical condition, and any co-existing conditions. [Claim 29] Within the attribute analysis unit, analyzing the plurality of stored diet and lifestyle attributes includes comparing the plurality of stored diet and lifestyle attributes with corresponding plurality of evidence-based fertility criteria, and determining a plurality of pregnancy support opportunities based at least on the plurality of stored diet and lifestyle attributes and the comparison with the corresponding plurality of evidence-based fertility criteria. The system according to claim 27. [Claim 30] Within the attribute analysis unit, comparing the plurality of stored user attributes with corresponding plurality of evidence-based fertility criteria includes determining a user fertility classification based at least on the plurality of stored diet and lifestyle attributes, determining a set of fertility criteria based at least on the user's fertility classification, selecting the set of fertility criteria as the corresponding plurality of evidence-based fertility criteria, and comparing each of the plurality of diet and lifestyle attributes with a corresponding one of the plurality of evidence-based fertility criteria. The system according to claim 29. [Claim 31] Within the attribute analysis unit, the user's fertility classification is determined as any one of a person planning and anxious, a person planning and healthy, a person having difficulty during a pregnancy attempt, and a person healthy during a pregnancy attempt. A person planning and anxious means that the user has a medical problem related to fertility health and currently represents a classification at the family planning stage. A person who is planning and healthy refers to a category where the user has a healthy pregnancy constitution and is currently considered to be in the family planning stage. A person who has difficulties during pregnancy attempts refers to a category where the user has medical problems related to pregnancy health and is currently actively attempting pregnancy. A person who is healthy during pregnancy attempts refers to a category where the user has a healthy pregnancy constitution and is currently considered to be actively attempting pregnancy. The system according to claim 30. [Claim 32] The step of obtaining requests for dietary and lifestyle recommendations for improving pregnancy. The step of requesting and obtaining a plurality of user attributes and behaviors. The step of performing automated real-time data analysis of the plurality of user attributes and behaviors. The step of generating a plurality of dietary and lifestyle recommendations based on at least the analysis of the plurality of user attributes. Including the step of providing the generated plurality of dietary and lifestyle recommendations. A computer-implemented method for generating dietary and lifestyle recommendations for promoting pregnancy. [Claim 33] The step of obtaining a plurality of user dietary attributes. The step of comparing the dietary attributes of the plurality of users with a plurality of corresponding pregnancy dietary criteria. The step of determining a plurality of food intake deficiencies based on at least the comparison between the dietary attributes of the plurality of users and the plurality of corresponding pregnancy dietary criteria. The step of generating a plurality of specific nutritional supplementation recommendations based on the plurality of food intake deficiencies. Including the step of presenting the plurality of nutritional supplementation recommendations. A method for providing a specific nutritional supplementation method for promoting pregnancy. [Claim 34] The step of obtaining a plurality of user attributes and behaviors. The step of comparing the plurality of user attributes and behaviors with a plurality of corresponding pregnancy attributes and behavior criteria. The step of determining a plurality of stress reduction opportunities based on at least the comparison between the plurality of user attributes and behaviors and the plurality of corresponding pregnancy attributes and behavior criteria. The step of generating a plurality of dietary and lifestyle recommendations based on at least the plurality of stress reduction opportunities. Including the step of presenting the plurality of dietary and lifestyle recommendations. A method for reducing stress from infertility. [Claim 35] The step of obtaining requests for a plurality of pregnancy improvement recommendations. The step of requesting and obtaining a plurality of user attributes. The step of comparing the plurality of user attributes with a plurality of corresponding pregnancy criteria that are evidence-based. determining a plurality of pregnancy support opportunities based at least on said plurality of user attributes and said comparison with a corresponding plurality of fertility criteria that are evidence-based; identifying a plurality of fertility improvement recommendations based at least on said plurality of pregnancy support opportunities, said plurality of user attributes, and an attribute history database; presenting at least one of said plurality of fertility improvement recommendations; obtaining a selected recommendation selected from at least one of said presented plurality of fertility improvement recommendations; storing said plurality of user attributes and said selected recommendation in said attribute history database; obtaining at least one recommendation result; including storing said at least one recommendation result in said attribute history database, A method for providing a customized integrated approach to promoting family pregnancy. [Claim 36] The step of requesting and obtaining a plurality of user attributes includes providing a list of available home test kits; obtaining a plurality of results from at least one of said available home test kits; including determining a plurality of user attributes based at least on said plurality of results, The method according to claim 35. [Claim 37] The step of requesting and obtaining a plurality of user attributes includes providing a self-assessment tool; obtaining a plurality of results from said self-assessment tool; including determining said plurality of user attributes based at least on said plurality of results, The method according to claim 35. [Claim 38] The step of requesting and obtaining a plurality of user attributes includes obtaining a plurality of user attributes from a standard health examination, said standard health examination being performed by a medical professional, The method according to claim 35. [Claim 39] The method according to claim 35, wherein said attribute history database includes a plurality of prior user attributes, a plurality of corresponding recommendations, and the effectiveness of said corresponding recommendations. [Claim 40] The step of identifying a plurality of fertility improvement recommendations based at least on said plurality of pregnancy support opportunities, said plurality of user attributes, and an attribute history database includes determining a plurality of similar prior cases by analyzing said attribute history database, said step of analyzing said attribute history including identifying similarities between said plurality of user attributes and said plurality of prior user attributes, a step of determining a plurality of similar prior cases, determining a plurality of precedent results based on the plurality of similar precedent cases, and the corresponding plurality of corresponding recommendations and the effectiveness of the corresponding recommendations; determining a plurality of successful recommendations based on the plurality of precedent results; determining a plurality of unsuccessful recommendations based on the plurality of precedent results; including determining the plurality of pregnancy improvement recommendations based on the plurality of successful recommendations and the plurality of unsuccessful recommendations; The method according to claim 35. [Claim 41] The step of identifying a plurality of pregnancy improvement recommendations based at least on the plurality of pregnancy support opportunities, the plurality of user attributes, and the attribute history database, includes identifying a plurality of guidelines for diet and lifestyle related to a specific medical condition; and determining a plurality of pregnancy improvement recommendations based on the plurality of guidelines for diet and lifestyle. The method according to claim 35. [Claim 42] The method according to claim 35, wherein the plurality of user attributes include attributes corresponding to a user's sleep habits, the level of stress received, metrics of the relationship with the user's partner, current medication or dietary supplements, and the history of medical staff visits. [Claim 43] The method according to claim 35, wherein the plurality of pregnancy improvement recommendations include warnings about potential pregnancy problems for the user. [Claim 44] The method according to claim 35, wherein the plurality of pregnancy improvement recommendations include recommendations for contacting medical staff. [Claim 45] obtaining a plurality of user attributes; comparing the plurality of user attributes with corresponding plurality of pregnancy criteria that are evidence-based; determining a plurality of pregnancy support opportunities based at least on the comparison between the plurality of user attributes and the corresponding plurality of pregnancy criteria that are evidence-based; identifying a plurality of pregnancy improvement recommendations based at least on the plurality of pregnancy support opportunities, the plurality of user attributes, and the attribute history database; presenting at least one of the plurality of pregnancy improvement recommendations; including obtaining a request for contacting a pregnancy coach. A method for assisting a family member's pregnancy. [Claim 46] further including determining a virtual coach for pregnancy based on the plurality of pregnancy improvement recommendations; and providing access to the virtual coach for pregnancy. The method according to claim 45. [Claim 47] A step of determining a pregnancy personal coach based on the plurality of pregnancy improvement recommendations; A step of providing access to the pregnancy personal coach, further comprising the method according to claim 45. The method according to claim 45. [Claim 48] A memory; A CPU; An attribute acquisition unit configured to acquire a plurality of dietary and lifestyle attributes; In response to the attribute acquisition unit acquiring the plurality of dietary and lifestyle attributes, an attribute storage unit configured to add the plurality of dietary and lifestyle attributes as a new entry to an attribute history database based at least on when the plurality of dietary and lifestyle attributes were acquired; An attribute analysis unit configured to analyze the plurality of dietary and lifestyle attributes stored in the attribute history database, the attribute analysis unit including performing a long-term investigation to analyze the stored plurality of dietary and lifestyle attributes; An evidence-based dietary and lifestyle recommendation engine configured to generate a plurality of pregnancy improvement recommendations based at least on the stored plurality of dietary and lifestyle attributes and the analysis performed by the attribute analysis unit; A display configured to present at least one of the plurality of pregnancy improvement recommendations; A recommendation acquisition unit configured to acquire a selected recommendation; A recommendation tracking unit, In response to the recommendation acceptance unit acquiring the plurality of recommendations that have been executed, the selected recommendation is stored in the attribute history database in association with the plurality of dietary and lifestyle attributes that are stored; Acquiring at least one recommendation result; A recommendation tracking unit configured to store the at least one recommendation result in the attribute history database, including; The attribute analysis unit is further configured to repeatedly analyze the plurality of dietary and lifestyle attributes stored in the attribute history database in response to the attribute storage unit adding the new entry to the attribute history database; The evidence-based dietary recommendation engine is further configured to repeatedly generate the plurality of pregnancy improvement recommendations in response to the attribute analysis unit completing the analysis; A continuously available system for generating pregnancy improvement recommendations.

Claims

1. A step of requesting and obtaining a plurality of user attributes; A step of comparing the plurality of user attributes with a corresponding plurality of pregnancy criteria; A step of determining one or more pregnancy criteria that the user does not currently meet based on the comparison between the plurality of user attributes and the corresponding plurality of pregnancy criteria; A step of determining a plurality of pregnancy support opportunities based on the one or more pregnancy criteria that the user does not currently meet; A step of identifying a plurality of pregnancy improvement recommendations based on at least the plurality of pregnancy support opportunities; A step of presenting at least one of the plurality of pregnancy improvement recommendations, wherein the plurality of user attributes includes information related to at least one of age, gender, weight, height, activity level, food allergies, dietary preferences, pregnancy constitution, lifestyle choices, any pregnancy-related medical conditions, and any co-existing conditions; A method performed by a computer for assisting in the pregnancy of a family member.

2. The method according to claim 1, wherein the plurality of user attributes are obtained from a standard health examination.

3. The method according to claim 2, wherein the standard health examination includes the use of a home health kit.

4. The method according to claim 2, wherein the standard health examination is performed by a medical professional.

5. The step of requesting and obtaining the plurality of user attributes includes: A step of presenting an attribute questionnaire; A step of obtaining a plurality of answers based on the attribute questionnaire; The method according to claim 1, further including a step of determining the plurality of user attributes based on at least the plurality of answers.

6. The method according to claim 5, wherein the attribute questionnaire is customized based on a plurality of preliminary attributes.

7. The method according to claim 5, wherein the attribute questionnaire is repeatedly customized for a user based on a plurality of previously obtained questionnaire answers.

8. The step of comparing the plurality of user attributes with a corresponding plurality of pregnancy criteria includes: A step of determining a user pregnancy classification based on at least the plurality of user attributes; A step of determining a set of pregnancy criteria based on at least the user's pregnancy classification; A step of selecting the set of pregnancy criteria as the corresponding plurality of pregnancy criteria; The method according to claim 1, further including a step of comparing each of the plurality of user attributes with a corresponding one of the plurality of pregnancy criteria. The method according to claim 1.

9. The pregnancy status of the user is determined as any one of those who are planning and anxious, those who are planning and healthy, those who are having difficulty in attempting pregnancy, and those who are healthy in attempting pregnancy, Those who are planning and anxious refer to the category where the user has medical problems related to reproductive health and is currently in the family planning stage. Those who are planning and healthy refer to the category where the user has a healthy reproductive constitution and is currently considered to be in the family planning stage. Those who are having difficulty in attempting pregnancy refer to the category where the user has medical problems related to reproductive health and is currently actively attempting pregnancy. Those who are healthy in attempting pregnancy refer to the category where the user has a healthy reproductive constitution and is currently considered to be actively attempting pregnancy. The method according to claim 8.

10. The method according to claim 9, further comprising determining a user's pregnancy score based at least on the comparison in the step of comparing the plurality of user attributes with corresponding plurality of pregnancy criteria.

11. The step of determining the plurality of pregnancy support opportunities includes identifying a first set of user attributes each consisting of the plurality of user attributes that are less than the corresponding criteria among the plurality of pregnancy criteria, identifying a second set of user attributes each consisting of the plurality of user attributes that are equal to or greater than the corresponding pregnancy criteria, and determining the plurality of pregnancy support opportunities based at least on the identified first set of user attributes and the second set of user attributes. The method according to claim 1.

12. The step of identifying at least one plurality of pregnancy improvement recommendations based at least on the plurality of pregnancy support opportunities includes providing the plurality of pregnancy support opportunities to a cloud-based service, and obtaining from the cloud-based service a plurality of pregnancy improvement recommendations based on the pregnancy support opportunities provided to the cloud-based service. The method according to claim 1.

13. The step of presenting at least one of the plurality of pregnancy improvement recommendations includes determining a set of recommendations that can be presented based at least on the obtained pregnancy improvement recommendations, and presenting the set of recommendations that can be presented. The method according to claim 1.

14. obtaining a request for a plurality of pregnancy improvement recommendations, and obtaining a plurality of user attributes by requesting. comparing the plurality of user attributes with a corresponding plurality of fertility criteria; determining, based on the comparison of the plurality of user attributes and the corresponding plurality of fertility criteria, one or more fertility criteria that the user does not currently meet; determining a plurality of pregnancy support opportunities based on the one or more fertility criteria that the user does not currently meet; identifying a plurality of fertility improvement recommendations based on at least the plurality of pregnancy support opportunities, the plurality of user attributes, and an attribute history database; presenting at least one of the plurality of fertility improvement recommendations; obtaining a selected recommendation selected from at least one of the presented plurality of fertility improvement recommendations; storing the plurality of user attributes and the selected recommendation in the attribute history database; obtaining at least one recommendation result; storing the at least one recommendation result in the attribute history database, and wherein the plurality of user attributes includes information regarding at least one of age, gender, weight, height, activity level, food allergies, dietary preferences, fertility constitution, lifestyle choices, any fertility-related medical conditions, and any co-existing conditions; A method performed by a computer for providing a customized integrated approach for promoting family pregnancy.

15. The step of requesting and obtaining a plurality of user attributes includes providing a list of available home test kits; obtaining a plurality of results from at least one of the available home test kits; and determining a plurality of user attributes based on at least the plurality of results. The method according to claim 14.

16. The step of requesting and obtaining a plurality of user attributes includes providing a self-assessment tool; obtaining a plurality of results from the self-assessment tool; and determining the plurality of user attributes based on at least the plurality of results. The method according to claim 14.

17. The step of requesting and obtaining a plurality of user attributes includes obtaining a plurality of user attributes from a standard health examination, wherein the standard health examination is performed by a medical professional. The method according to claim 14.

18. The method according to claim 14, wherein the attribute history database includes a plurality of previous user attributes, a plurality of corresponding recommendations, and the effectiveness of the corresponding recommendations.

19. The step of identifying a plurality of pregnancy improvement recommendations based on at least the plurality of pregnancy support opportunities, the plurality of user attributes, and the attribute history database includes a step of determining a plurality of similar precedent cases by analyzing the attribute history database, wherein the step of analyzing the attribute history includes a step of identifying the similarity between the plurality of user attributes and the plurality of precedent user attributes, a step of determining a plurality of similar precedent cases; a step of determining a plurality of precedent case results based on the plurality of similar precedent cases, and the corresponding plurality of corresponding recommendations and the effectiveness of the corresponding recommendations; a step of determining a plurality of successful recommendations based on the plurality of precedent case results; a step of determining a plurality of unsuccessful recommendations based on the plurality of precedent case results; and a step of determining the plurality of pregnancy improvement recommendations based on the plurality of successful recommendations and the plurality of unsuccessful recommendations. The method according to claim 18.

20. The step of identifying a plurality of pregnancy improvement recommendations based on at least the plurality of pregnancy support opportunities, the plurality of user attributes, and the attribute history database includes a step of identifying a plurality of dietary and lifestyle guidelines related to a specific medical condition; and a step of determining a plurality of pregnancy improvement recommendations based on the plurality of dietary and lifestyle guidelines. The method according to claim 14.

21. The method according to claim 14, wherein the plurality of user attributes include attributes corresponding to a user's sleep habits, the level of stress received, a metric of the relationship with the user's partner, current medication or dietary supplements, and the history of medical staff visits.

22. The method according to claim 14, wherein the plurality of pregnancy improvement recommendations include warnings about potential pregnancy problems for the user.

23. The method according to claim 14, wherein the plurality of pregnancy improvement recommendations include recommendations for contacting medical staff.

24. a step of obtaining a plurality of user attributes; a step of comparing the plurality of user attributes with a corresponding plurality of pregnancy criteria; a step of determining one or more pregnancy criteria that the user currently does not meet based on the comparison between the plurality of user attributes and the corresponding plurality of pregnancy criteria; a step of determining a plurality of pregnancy support opportunities based on the one or more pregnancy criteria that the user currently does not meet. identifying a plurality of pregnancy improvement recommendations based at least on the plurality of pregnancy support opportunities, the plurality of user attributes, and the attribute history database; presenting at least one of the plurality of pregnancy improvement recommendations; obtaining a request for contacting a pregnancy coach; and the plurality of user attributes includes information regarding at least one of age, gender, weight, height, activity level, food allergies, food preferences, pregnancy constitution, lifestyle choices, any pregnancy-related medical conditions, and any co-existing conditions. A method performed by a computer for assisting a family member's pregnancy.

25. further comprising determining a virtual coach for pregnancy based on the plurality of pregnancy improvement recommendations; providing access to the virtual coach for pregnancy. The method according to claim 24.

26. further comprising determining a personal coach for pregnancy based on the plurality of pregnancy improvement recommendations; providing access to the personal coach for pregnancy. The method according to claim 24.

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