Trademark fixes

uxlfoundation · Oct 30, 2024 · 34386e6 · 34386e6
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diff --git a/docs/source/deprecation.rst b/docs/source/deprecation.rst
@@ -18,7 +18,7 @@
 Deprecation Notice
 ==================
 
-This page provides information about the deprecations of a specific oneDAL functionality.
+This page provides information about the deprecations of a specific Intel(R) oneAPI Data Analytics Library (oneDAL) functionality.
 
 Java* Interfaces
 ****************

diff --git a/docs/source/onedal/algorithms/clustering/kmeans.rst b/docs/source/onedal/algorithms/clustering/kmeans.rst
@@ -52,7 +52,7 @@ Expression :math:`\|\cdot\|` denotes :math:`L_2` `norm
 
 .. note::
    In the general case, :math:`d` may be an arbitrary distance function. Current
-   version of the oneDAL spec defines only Euclidean distance case.
+   version of the Intel(R) oneAPI Data Analytics Library (oneDAL) specification defines only Euclidean distance case.
 
 
 .. _kmeans_t_math_lloyd:

diff --git a/docs/source/onedal/algorithms/decomposition/pca.rst b/docs/source/onedal/algorithms/decomposition/pca.rst
@@ -76,7 +76,7 @@ The PCA algorithm can be trained using either the covariance or the correlation
 The choice of covariance matrix or correlation matrix is application-dependent.
 More specifically, if scaling of the features is important for a problem,
 which is often the case, using the correlation matrix to compute principal components is more appropriate.
-By default, oneDAL uses the correlation matrix to compute the principal components. It is possible
+By default, Intel(R) oneAPI Data Analytics Library (oneDAL) uses the correlation matrix to compute the principal components. It is possible
 to use the covariance matrix by passing "precomputed" as method and feeding a covariance matrix as input
 to the PCA algorithm. To compute the covariance matrix, the :ref:`Covariance <alg_covariance>` algorithm can be used.
 

diff --git a/docs/source/onedal/gpu_support.rst b/docs/source/onedal/gpu_support.rst
@@ -23,7 +23,7 @@ See the differences in CPU and GPU support below.
 
 GPU-Supported Targets
 *********************
-OneDAL is designed to work with Intel(R) GPUs specifically, but it could potentially 
+Intel(R) oneAPI Data Analytics Library (oneDAL) is designed to work with Intel(R) GPUs specifically, but it could potentially 
 run on other hardware platforms if a SYCL runtime is available.
 
 .. tabularcolumns::  |\Y{0.5}|\Y{0.5}|

diff --git a/docs/source/onedal/introduction.rst b/docs/source/onedal/introduction.rst
@@ -16,7 +16,7 @@
 
 Introduction
 ************
-oneDAL provides redesigned versions of interfaces that account for multi-device targets.
+Intel(R) oneAPI Data Analytics Library (oneDAL) provides redesigned versions of interfaces that account for multi-device targets.
 For example, CPU and GPU, distributed SPMD interfaces, and many more.
 
 Algorithms Support

diff --git a/docs/source/onedal/spmd/index.rst b/docs/source/onedal/spmd/index.rst
@@ -40,7 +40,7 @@ accordance with the input.
   :width: 800
   :alt: Typical SPMD flow
 
-  Example of SPMD Flow in oneDAL
+  Example of SPMD Flow in Intel(R) oneAPI Data Analytics Library (oneDAL).
 
 .. _communicator_operations: