Spacy v2.0.1 custom NER: How to improve training of existing model

I implemented custom NER with bellow trained data first time and it gives me good prediction with Name and PrdName. I mentioned code bellow.

if __name__ == '__main__':
TRAIN_DATA = [
            ('My Name is Rajesh', {'entities': [(11, 17, 'Name')]}),
            ('My Name is Bakul', {'entities': [(11, 16, 'Name')]}),
            ('My Name is Pritam', {'entities': [(11, 17, 'Name')]}),
            ('My Name is Rakesh', {'entities': [(11, 17, 'Name')]}),
            ('My Name is Jayeeta', {'entities': [(11, 18, 'Name')]}),
            ('this is the price of bag', {'entities': [(21, 24, 'PrdName')]}),
            ('what is the price of ball?', {'entities': [(21, 25, 'PrdName')]}),
            ('what is the price of jegging?', {'entities': [(21, 28, 'PrdName')]}),
            ('what is the price of t-shirt?', {'entities': [(21, 28, 'PrdName')]}),
              ]

iterations = 20
try:
    model = 'live_ner_model'
    nlp = spacy.load(model)  # load existing spacy model
except:
    model = None
    print("Exception")
    nlp = spacy.blank('en')  # create blank Language class
    print("Created blank 'en' model")

if 'ner' not in nlp.pipe_names:
    ner = nlp.create_pipe('ner')
    nlp.add_pipe(ner)
    print("Create NER")
else:
    ner = nlp.get_pipe('ner')
    print("Exhisting NER")

# Add new entity labels to entity recognizer
for _, annotations in TRAIN_DATA:
    for ent in annotations.get('entities'):
        ner.add_label(ent[2])

# get names of other pipes to disable them during training
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'ner']
with nlp.disable_pipes(*other_pipes):  # only train NER
    optimizer = nlp.begin_training()
    for itn in range(iterations):
        print("Statring iteration " + str(itn))
        random.shuffle(TRAIN_DATA)
        losses = {}
        for text, annotations in TRAIN_DATA:
            nlp.update(
                [text],  # batch of texts
                [annotations],  # batch of annotations
                drop=0.2,  # dropout - make it harder to memorise data
                sgd=optimizer,  # callable to update weights
                losses=losses)
        print(losses)

# Save model
output_dir = 'live_ner_model'
if output_dir is not None:
    output_dir = Path(output_dir)
    if not output_dir.exists():
        output_dir.mkdir()
    nlp.meta['name'] = model  # rename model
    nlp.to_disk(output_dir)
    print("Saved model to", output_dir)

# Test the saved model
output_dir = 'live_ner_model'
print("Loading from", output_dir)

nlp2 = spacy.load('live_ner_model')
test_text = """
   what is the price of cup. My Name is Rahim
"""
doc2 = nlp2(test_text)
for ent in doc2.ents:
    print(ent.label_, ent.text)

But when I am trying to trained with some new data which has entity with only PrdName or any other new entity excluding Name in existing model. Then Name entity prediction goes wrong. I think this issue arises as I updated trained data excluding Name entity.

So is there any way we can improve training by not affecting existing training. Can someone share the idea? If possible please share a sample code.

Environment: Anaconda, spacy=v2.0.1, python=3.7