The goal of the planned work is to build a system for assessing and predicting the potential of gametes and embryos, as well as human reproductive capabilities, based on spectral data obtained from the objects of investigation, followed by prospective validation of the developed system. The study protocol consists of a retrospective and prospective stages. The task of the retrospective stage is to study gametes, embryos, and endometrium using the declared methods and build a machine learning model, determine the predictive capabilities of the obtained models. The task of the prospective stage is to determine the practical efficiency of applying models for making clinically significant decisions in infertility treatment with IVF. Hypothesis of the study: at the moment, a large number of approaches and protocols for deselecting and selecting embryos / gametes, assessing endometrial receptivity has been proposed. Approaches related to deselection are mainly based on determining the genetic constitution (aneuploidy) of the investigated object. However, there are no models linking such testing results and the outcome of infertility treatment with clinically significant effectiveness. There are many publications when, after transferring aneuploid embryos, pregnancy develops with a healthy fetus. It is known that the concordance of aneuploidy test results between the internal cell mass and trophoblasts is about 60%. Moreover, when using PGT-a, the birth rate among women with a single available blastocyst is reduced twice. Approaches related to selection, i.e. predicting a positive outcome of treatment, are built on morphological, morphometric, metabolic, and gene expression approaches. However, their effectiveness either has not been proven, or has (if it has) relatively low predictive importance. This is due to the fact that, from the point of modern views on reproductive biology, for the occurrence and development of successful pregnancy, it is necessary to combine factors that belong to gametes, embryos, and the maternal organism. Also, other undetectable technical or other circumstances may play a role in influencing the chance of ongoing pregnancy. Therefore, for effective prediction of a positive outcome, it is necessary to develop and apply complex models that take into account variables from different sources, from all parties involved. However, there will always be additional variability, caused by a series of unspecified or difficult to specify factors, which makes the task of such prediction quite challenging. In this connection, predicting a negative outcome of treatment (deselecting objects) seems more sensible, as it is entirely feasible for cases, where the cause of the negative outcome is attributable to this object (for example, the state of the embryo). This will not only optimize patient care protocols (for example, not to transfer obviously incapable to implant embryos), but also determine the possible cause of the negative outcome in each specific case, and in a population scale determine the share of variability of the phenomenon (development of ongoing pregnancy), which may be related to a specific object. The last one is necessary for adequate development and testing of new therapeutic and diagnostic methods