Because of the complexity of cancer‐immune system interactions, combinations of biomarkers will be required for predicting individual patient responses to treatment and for monitoring combination strategies to overcome treatment resistance. To this end, the “immunogram” has been proposed as a comprehensive framework to capture all relevant immunological variables. Here, we developed a method to convert transcriptomic data into immunogram scores (IGS). This immunogram includes 10 molecular profiles, consisting of innate immunity, priming and activation, T cell response, interferon γ (IFNG) response, inhibitory molecules, regulatory T cells, myeloid‐derived suppressor cells (MDSCs), recognition of tumor cells, proliferation, and glycolysis. Using genes related to these 10 parameters, we applied single‐sample gene set enrichment analysis (ssGSEA) to 9417 bulk RNA‐Seq data from 9362 cancer patients with 29 different solid cancers in The Cancer Genome Atlas (TCGA). Enrichment scores were z‐score normalized (Z) for each cancer type or the entire TCGA cohort. The IGS was defined by the formula IGS = 3 + 1.5 × Z so that patients would be well distributed over a range of scores from 1 to 5. The immunograms constructed in this way for all individual patients in the entire TCGA cohort can be accessed at “The RNA‐Seq based Cancer Immunogram Web” (https://yamashige33.shinyapps.io/immunogram/). We propose a novel scoring and visualization method for “immunogram” to assess the cancer immunity status of individual patients using the large TCGA dataset and RNA‐Seq of each patient. Immunograms for each individual will be a useful tool for precision immuno‐oncology, although their application needs to be validated in clinical trials.