| CPC G06Q 30/0631 (2013.01) [A61B 5/441 (2013.01); A61K 8/00 (2013.01); A61N 5/0616 (2013.01); A61Q 19/005 (2013.01); A61Q 19/007 (2013.01); A61Q 19/008 (2013.01); A61Q 19/08 (2013.01); A61Q 19/10 (2013.01); G01N 33/6881 (2013.01); G06N 20/00 (2019.01); G06Q 30/0282 (2013.01); G16H 20/00 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); G16H 50/70 (2018.01); G16H 50/80 (2018.01); A61B 5/411 (2013.01); A61B 2505/05 (2013.01); A61F 2007/0052 (2013.01); A61N 2007/0034 (2013.01); G01N 2800/00 (2013.01); G06T 2207/30088 (2013.01)] | 20 Claims |

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1. A host server comprising a memory storing executable instructions and a processor configured to load the executable instructions from the memory to instantiate a service to communicably couple with a client application instantiated by a client device operated by a user, the service configured to:
receive a user dataset comprising demographic information of the user, the demographic information comprising a geographic location of the user;
generate and cause to be executed a query based on the geographic location to a database service storing location-specific historical environment data;
receive a result from the database service, the result comprising historical environmental information about the geographic location of the user, the historical geographic information comprising water hardness information, seasonal temperature information, and seasonal ultraviolet light exposure information;
iteratively, for each respective personal care concern of a set of at least two personal care concerns:
query the database service to retrieve a respective set of diagnostic indicators specific to the respective personal care concern;
determine, for each respective diagnostic indicator of the respective set of diagnostic indicators, a respective credibility metric representing a first statistical likelihood that based on the user dataset at least one of the user or the geographic location exhibits the respective diagnostic indicator; and
determine, based on each credibility metric, a respective aggregate confidence metric representing a second statistical likelihood that the user is likely to experience the respective personal care concern;
ingest by a predictive model service comprising a trained predictive model, the user dataset, each respective aggregate confidence metric, and the historical geographic information, the trained predictive model trained against a dataset derived from customer review sentiment data correlating review author demographics and geographies against estimated ingredients and ingredient quantities of products reviewed by those review authors;
receive from the predictive model service, a combined set of ingredients to include in a personal care product therapeutic to at least one personal care concern of the set of personal care concerns for which a respective aggregate confidence metric satisfies a threshold; and
generate a formulation for a custom personal care product by selecting a base from a set of personal care bases and at least one additive from a set of personal care base additives, at least one of the selected based and the one or more selected additives containing the combined set of ingredients;
cause a custom product to be manufactured by combining the selected personal care base and the selected one or more additives; and
provide the custom product to the end user.
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