The name of this schema pattern

Not every approach to common problems has a name. Certainly at least not a commonly agreed name. The term "design patterns" applies just as well to common database designs as it does to OOP. I would say data modellers are a little less enamoured with the idea of design patterns than OOP programmers.

I'm not familiar with a commonly agreed name for the design approach that you're describing. I call it transactional logging.

As an aside, the price in the sale item table is not technically redundant, even if the price hasn't changed. The price in the product table is the current book price, the price in the sale item table is the actual price charged. There is no way of knowing for sure what price was actually charged from looking at book price. Even if you have a book price history table it might have been possible, depending on your system's rules, for the price to be overridden manually. The two data attributes are semantically different, even if they happen to be coincidentally equal for some period of time.

It's called Entity-Attribute-Value (also sometimes 'name-value pairs') and it's a classic case of "a round peg in a square hole" when people use the EAV pattern in a relational database.

Here's a list of why you shouldn't use EAV:

• You can't use data types. It doesn't matter if the value is a date, a number or money (decimal). It's always going to be coerced to varchar. This can be anything from a minor performance problem to a massive gut-ache (ever had to chase down a one-cent variation in a monthly roll-up report?).
• You can't (easily) enforce constraints. It requires a ridiculous amount of code to enforce "Everyone needs to have a height between 0 and 3 metres" or "Age must be not null and >= 0", as opposed to the 1-2 lines that each of those constraints would be in a properly-modelled system.
• Related to above, you can't easily guarantee that you get the information you need for each client (age might be missing from one, then the next might be missing their height etc.). You can do it, but it's a hell of a lot more difficult than SELECT height, weight, age FROM Client where height is null or weight is null.
• Related again, duplicate data is a lot harder to detect (what happens if they give you two ages for one client? De-EAVing the data, as below, will give you two rows of results if you have one attribute doubled. If one client has two separate entries for two attributes, you'll get four rows from the query below).
• You can't even guarantee that the attribute names are consistent. "Age_yr" might become "AGE_IN_YEARS" or "age". (Admittedly this is less of a problem when you're receiving an extract versus when people are inserting data, but still.)
• Any sort of nontrivial query is a complete disaster. To relationalise a three-attribute EAV system so you can query it in a rational fashion requires three joins of the EAV table.

Compare:

SELECT cID.ID AS [ID], cH.Value AS [Height], cW.Value AS [Weight], cA.Value AS [Age]
FROM (SELECT DISTINCT ID FROM Client) cID 
      LEFT OUTER JOIN 
    Client cW ON cID.ID = cW.ID AND cW.Metric = "Wt_kg" 
      LEFT OUTER JOIN 
    Client cH ON cID.ID = cH.ID AND cW.Metric = "Ht_cm" 
      LEFT OUTER JOIN 
    Client cA ON cID.ID = cA.ID AND cW.Metric = "Age_yr"

To:

SELECT c.ID, c.Ht_cm, c.Wt_kg, c.Age_yr
FROM Client c

Here's a (very short) list of when you should use EAV:

• When there's absolutely no way around it and you have to support schema-less data in your database.
• When you just need to store "stuff" and don't expect to have to need it in a more structured form. Beware, though, the monster called "changing requirements".

I know I just spent this entire post detailing why EAV is a terrible idea in most cases - but there are a few cases where it's needed/unavoidable. however, most of the time (including the example above), it's going to be far more hassle than it's worth. If you have a requirement for wide support of EAV-type data input, you should look at storing them in a key-value system, e.g. Hadoop/HBase, CouchDB, MongoDB, Cassandra, BerkeleyDB.