Subject Matter Patentability for Bioinformatics Patent Applications Principles & Practice

1. Subject Matter Patentability for Bioinformatics Patent Applications Principles & Practice Gregory L. Maurer Klarquist Sparkman, LLP AIPLA Spring Meeting 2008 Biotechnology/Emerging Technologies Committees

2. Subject Matter Patentability for Bioinformatics Patent Applications • Importance of subject matter patentability • Differences from “regular” software • Kinds of claims available • Two common pitfalls: One solution • Impact of recent cases

3. Theme: Spectrum of §101 positions • Zealous representation is great, but . . . • Include conservative §101 positions.

4. Importance • Failure can be disastrous • Shipwrecked case • Narrow coverage • Advocacy Issue • Unique issue requiring attention/preparation • Law is in constant flux – subjective tests • Good advocate makes a difference

5. Importance: Advocacy • Is software patentable? • Imagine approaching the issue in 1965. • Any changes between 1965 and 1988? • Any changes between 1988 and today? • Any lessons to be learned?

6. Importance: Advocacy • Allowed claim from case filed in 1965: In a data processing system including a plurality of magnetic tape units for serially storing data signal combinations and having means for reading and writing signals during reeling thereof in a forward direction . . . a first iterative control loop means having means for initiating operation of said sort performing means to sort sets of said data signal combinations. . .

7. Importance: Advocacy • Allowed claim from case filed in 1988: In a data processing system including sensing means for sensing an image and converting said image into input image data, preprocessing means connected to receive said input image data for filtering noise from said input image data, and data conversion means connected to receive said filtered input image data for converting said filtered input image data into output data, . . . , a data processing method of converting said input image data into said output data when said filter means is n-sized comprising the steps of:

8. Practice Take Away • The law will change, but . . . • Useful, innovative inventions can still be protected if presented properly.

9. Difference from “Regular” Software • Different Art Unit • Technology Center 1630 • Cases tend to be huge • Patent practitioner has more responsibility • No other person may completely understand • “Cutting edge” • Describe practical applications in detail • Careful: May be seen as mental process

10. Typical “Regular” Software Claim A method of compressing a digital image comprising: determining a recurring pattern of values in the digital image; storing the recurring pattern of values for a first occurring occurrence of the recurring pattern of values; and for subsequent occurrences of the recurring pattern of values, storing a reference to the recurring pattern of values in place of the recurring pattern of values.

11. Bioinformatics: Mental Process? A method of determining a set of co-dependent genes comprising: identifying a set of one or more genes having related gene expression data; and removing at least one gene from the set of one or more genes based on a surplus information relationship between the at least one gene and other genes in the set. Would adding “computer-implemented” save?

12. Bioinformatics: Practical Application? A method of determining a set of co-dependent genes comprising: identifying a set of one or more genes having related gene expression data; and removing at least one gene from the set of one or more genes based on a surplus information relationship between the at least one gene and other genes in the set.

13. Practice Take Away • Not everyone loves software patents, so . . . • Be prepared for § 101 brick wall. Have backup positions. • Not everyone is familiar with your subfield, so • If “cutting edge,” understand how invention fits into bioinformatics ecosystem. Be prepared to limit to identified practical applications.

14. Kinds of Claims • Method (of finding gene relationship) • Apparatus (computer programmed to . . .) • Beauregard (computer-readable media) • User Interface (to accept commands) • Data Structure (for storing data) • Means-plus-function (Aristocrat) • Others (assay, kit, API, business aspects, etc.)

15. Practice Take Away • Claim diversity is advised, but . . . • It can be expensive.

16. Pitfall: “Floating” Claim A method comprising: generating a quad tree from gene expression data for respective genes in a gene set; identifying a most heteroskadastic gene out of the quad tree; and removing the most heteroskadastic gene from the genes in the set, yielding a reduced set of genes.

17. More Solid Version A method of identifying an outlier gene in a set of co-determined genes comprising: generating a quad tree from gene expression data for respective genes in the set; identifying a most heteroskadastic gene in the quad tree as contributing zero information to codetermination; removing the most heteroskadastic gene from the genes in the set, yielding a reduced set of genes; and identifying an outlier gene via application of applying a fast Fourier transform on gene expression data for respective genes in the reduced set of genes.

18. Even More Solid Versions • Add “outputting” a gene identifier • Add language about an assay/gene chip • Add language about purpose of assay

19. Pitfall: “Parameter” Claim A method comprising: generating a first data structure from gene expression data for respective genes in a gene set; for a plurality of genes in the gene set, determining a first parameter for respective genes out of a set of genes and storing the first parameter in the first data structure as associated with its respective gene; based on a gene having a highest value for the first parameter, storing an identity of the gene having the highest value in a second data structure; and for a gene identified by the second data structure, performing an operation on the set of genes, whereby the gene set is reduced in size.

20. More Solid Version A method of identifying an outlier gene in a set of co-determined genes comprising: generating a quad tree from gene expression data for respective genes in the set; identifying a most heteroskadastic gene in the quad tree as contributing zero information to codetermination; removing the most heteroskadastic gene from the genes in the set, yielding a reduced set of genes; and identifying an outlier gene via application of a fast Fourier transform on gene expression data for respective genes in the reduced set of genes.

21. Practice Take Away • If there is no clear practical application . . . • The claim is in trouble. (“Practical” application changes as the field evolves and is relative to the bioinformatics ecosystem.)

22. Impact of Recent Cases • In re Nuijten (Fed. Cir. Sept. 20, 2007) • “Signal Claim” invalid • Four categories and “Vacuum” rationale • Take away: Include definition/examples of “computer-readable media” or Examiner may allege it covers a “signal.”

23. Impact of Recent Cases • In re Comiskey (Fed. Cir. Sept. 20, 2007) • “Mandatory arbitration resolution” • “Mental process” not patentable • Claim seems to have more than mere mental acts, but no “machine.” • Take away: Make sure specification describes that actions are performed by machine or “tool.”

24. Impact of Recent Cases • In re Bilski (Fed. Cir. en banc arguments May 8, 2008) • “Series of market participant transactions balances the risk position” • 5 Questions • “Technological arts” test? • “Physical transformation” test? • Take away: Be prepared for adverse decision or adverse application of decision.

25. Thank You Resources • MPEP § 2106 (Subject Matter Eligibility) • 2005 Examiner Guidelines (Subject Matter) • www.uspto.gov/go/og/2005/week47/patgupa.htm • Federal Circuit Decisions • http://www.uspto.gov/go/com/sol/fedcirappeals.htm • Listen to the Oral Arguments • http://www.cafc.uscourts.gov/oralarguments/