From the PHOSITA to the MOSITA: Will “Secondary Considerations” Save Pharmaceutical Patents from Artificial Intelligence?


Artificial intelligence systems are being increasingly employed in pharmaceutical R&D to develop new drugs and medical treatments. In such a scenario, the patentability of new pharmaceutical inventions seems more and more problematic, given that the computational power of AI increases the likelihood that a new chemical composition is deemed to be obvious. In this article I argue that with the advent of AI-generated inventions both EU and US patent law cannot rely exclusively on the traditional standard of the “person having ordinary skill in the art” to evaluate the non-obviousness condition of patentability. However, I also maintain that a legislative reform is not necessary. Rather, judges should start to more strongly consider the so-called “secondary considerations” of non-obviousness that have been intermittently and inconsistently applied both in US and EU case law.

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  1. 1.

    On the topic of Big Data and AI see in general Ottolia 2017.

  2. 2.

    See Koza et al. (1999), pp. 5–7; Plotkin (2009), p. 61; Sanseverino (2018); Banterle (2018); Ramalho (2018).

  3. 3.

    Todd (2005); Fleming (2018); Klucznik et al. (2018); Schönberger (2019).

  4. 4.

    Japsen (2016).

  5. 5.

    Sharman (2018).

  6. 6.

    See for instance Abbott (2018), p. 6, according to which “the non-obviousness bar will continue to rise as machines inevitably become increasingly sophisticated. Taken to its logical extreme and given there is no limit to how intelligent computers would become, it may be that every invention will one day be obvious to commonly used computers. That would mean no more patents would be issued without some radical change to current patentability criteria”.

  7. 7.

    Samore (2013), p. 116.

  8. 8.

    Chisum (2006); Angelini (2007).

  9. 9.

    The WTO currently comprises 164 member states, with the notable exceptions of Iran, Iraq, Syria, Sudan, Algeria, Azerbaijan, Serbia, Bosnia and Herzegovina, Turkmenistan, North Korea, and a few others.

  10. 10.

    Abraham (1995); Lee (2008); Yang (2013).

  11. 11.

    Angelini (2007), p. 42.

  12. 12.

    For a comment on Art. 52 EPC see Ottolia (2016a).

  13. 13.

    For a comment on Art. 56 EPC see Ottolia (2016b); see also Cornish et al. (2013), p. 191; Reitzig (2005); Dolder, Ann and Buser (2014).

  14. 14.

    See Chin (1959); Barton (2003).

  15. 15.

    Witherspoon (1980); Duffy (2007); D. Chisum (2006), § 5.02[6].

  16. 16.

    Darrow (2009), p. 227.

  17. 17.

    Eisenberg (2004), p. 886.

  18. 18.

    Thomas (2011), p. 2071; Abramowicz and Duffy (2011); Lemley (2017).

  19. 19.

    Chiang (2008).

  20. 20.

    Ramalho (2018), p. 1. On the issues related to non-obviousness evaluation see Wagner and Strandburg (2006); Mandel (2008); Simon (2013), p. 332. See also Judge Learned Hand’s lamentation in Harries v. Air King Prods. Co., 183 F.2d 158, 163 (2d Cir. 1950), according to which the non-obviousness requirement is “as fugitive, impalpable, wayward and vague a phantom as exists in the whole paraphernalia of legal concepts”.

  21. 21.

    Mandel (2008), p. 63; see also Schneider (1978); Allison and Lemley (1998), p. 209; Reitzig (2005); Wagner and Strandburg (2006); Durie and Lemley (2008), p. 990, underlying that “the non-obviousness requirement […] is in dispute in almost every case, and it is responsible for invalidating more patents than any other patent rule”; Sheets (2011), who maintains that “the lion share of patent validity matters litigated in federal courts involves the issue of obviousness”.

  22. 22.

    Atal and Bar (2010); Chisum (1976); Franzosi (2005).

  23. 23.

    The first use of the acronym PHOSITA seems to appear in Soans (1966), p. 438.

  24. 24.

    On the notion of PHOSITA see, inter alia, Eisenberg (2004); Jonathan J. Bouchard (2007); Darrow (2009).

  25. 25.

    See for instance Xiang et al. (2013), according to which “the laws of inventive step and non-obviousness, as applied by the European Patent Office (EPO), the State Intellectual Property Office (SIPO) of the People’s Republic of China (PRC) and the United States Patent and Trademark Office (USPTO), have many similarities and some subtle differences”; Kageyama (2016).

  26. 26.

    Howlett and Christie (2003). On the patentability of DNA Est sequence, see Ottolia (2005).

  27. 27.

    EPO, Guidelines for Examination, G.VII.5. This Approach is not mandated by the EPC, but has been inferred inter alia from Art. 42 of the implementing Regulation, which reads: “c) disclose the invention, as claimed, in such terms that the technical problem (even if not expressly stated as such) and its solution can be understood, and state any advantageous effects of the invention with reference to the back-ground art”; see Leith (2014), pp. 173–75. On the evaluation of inventive step in EU patent law see generally Ammendola (1981); Floridia (2016); England (2018).

  28. 28.

    See EPO, Guidelines for Examination, G.VII.5.3.

  29. 29.

    Graham v. John Deere Co. of Kansas City, 383 U.S. 1 (1966).

  30. 30.

    Ibid, p. 17.

  31. 31.

    Thomas (2011), p. 2072: “A proposed invention was obvious if a teaching, suggestion, or motivation in the prior art pointed to the invention”.

  32. 32.

    Simic (2009).

  33. 33.

    Durie and Lemley (2008), p. 998; Sheets (2011), p. 11.

  34. 34.

    On this topic see generally Quinlan (2014); Khanna and Gulati (2018).

  35. 35.

    Tresansky (1991); Maera (2002); Wang and Hsiao (2010), pp. 18–19.

  36. 36.

    See for instance Dystar Textilfarben GmbH & Co. v. C.H. Patrick Co., 464 F.3d 1356.

  37. 37.

    For this thesis see Simon (2013), p. 103, according to which “as access to searchable information and computing capabilities expand, it might appear that very few inventions are nonobvious enough to merit patent protection”; Clifford (2018), p. 35, who maintains that “as the use of AI becomes omnipresent in the invention process, this will change the evaluation of the obviousness under §103”. See also Abbott (2018). For an overview on how the standards for patentable inventions have developed during time, see Prager (1952).

  38. 38.

    See Rowe and Partridge (1993); Kohlhepp (2008), p. 779, according to which: “Already, systems such as genetic algorithms allow computers to autonomously generate ‘real world’ inventions”; Koza (2010); Abbott (2016a), p. 1080, who argues that “Artificial intelligence (‘AI’) has been generating inventive output for decades, and now the continued and exponential growth in computing power is poised to take creative machines from novelties to major drivers of economic growth”.

  39. 39.

    Fraser (2015), p. 319. On the computational power of AI see also Ottolia (2014); Ottolia (2017).

  40. 40.

    “Brute force” is a computational technique based on the trial-and-error method. With this method, computers repeatedly try a huge amount of possibilities until the solution to the problem is found.

  41. 41.

    For this argument see e.g. Abbott (2016b), p. 187, who argues that: “Computational invention is already happening, and it is only a matter of time until it is happening routinely. In fact, it may be only a matter of time until computers are responsible for the majority of innovation and potentially displacing human inventors”.

  42. 42.

    Lim (2019), p. 863.

  43. 43.

    See for instance Kohlhepp (2008), p. 779, who argues that: “As technology continues to accelerate, however, research, discovery, and design work increasingly depend on computer programs to do not only the number-crunching but also the ‘thinking’”. See also Vertinsky and Rice (2002), p. 581; Abbott (2016a); Abbott (2016b).

  44. 44.

    Plotkin (2009). To date, this seems possible especially in the pharmaceutical industry and in genetic engineering. See for instance Kohlhepp (2008), p. 784, according to which: “Recent technologies known as ‘genetic programming’ or ‘evolutionary engineering’ have arguably proven to be the most effective at replicating human creativity”.

  45. 45.

    Abbott (2016a), p. 1080. According to Abbott, in fact: “Computers already are generating patentable subject matter under circumstances in which the computer, rather than a human inventor, meets the requirements to qualify as an inventor”.

  46. 46.

    Ottolia (2017).

  47. 47.

    Plotkin (2009), p. 107.

  48. 48.

    Fraser (2015), p. 321.

  49. 49.

    For a similar argument see Abbott (2018), p. 68, who maintains that: “The impact of the widespread use of inventive machines will be tremendous, not just on innovation, but also on patent law. Right now, patentability is determined based on what a hypothetical, non-inventive, skilled person would find obvious. The skilled person represents the average worker in the scientific field of an invention. Once the average worker uses inventive machines, or inventive machines replace the average worker, then inventive activity will be normal instead of exceptional. If the skilled person standard fails to evolve to reflect the fact that the average worker is inventive, this will result in too lenient a standard for patentability”.

  50. 50.

    See Vertinsky (2018), p. 502, according to which “with thinking machines in the equation, however, policymakers might have to consider whether the PHOSITA should be modified to include thinking machines – perhaps some kind of machine/person combination, or M/PHOSITA”.

  51. 51.

    Kohlhepp (2008), p. 783, who argues that “emerging software enabling artificial creativity directly challenges definitions of invention and inventor, in addition to implicating traditional qualms related to software’s inherent intangibility”. On the subjective elements of patent law see in general Ubertazzi (1983); Ubertazzi (1985).

  52. 52.

    Abbott (2016a), who argues that AI should be elevated from the role of mere tool and be recognized as the true inventor in patent applications.

  53. 53.

    On this topic see Clifford (1997); Hattenbach and Glucoft (2015); Abbott (2016a); Abbott (2016b); Khoury (2016); Ramalho (2018).

  54. 54.

    Abbott (2016a), p. 1082, according to which “patent law is concerned with the creativity of an invention itself rather than the subjective mental process by which an invention may have been achieved”.

  55. 55.

    Plotkin (2009), pp. 105–106; Abbott (2016a), p. 1081, who underlines that “applicants seem not to be disclosing the role of creative computers to the Patent Office – likely as a result of uncertainty over whether a computer inventor would render an invention unpatentable”.

  56. 56.

    See Patent US5852815A.

  57. 57.

    On the Creative Machine see Thaler (2013). Another example is Koza’s “Invention Machine”, who created an invention that was granted a patent by the USPTO on 25 January 2005.

  58. 58.

    For a different thesis, according to which a patent can be obtained only whereas it is the product of a human inventor see, however, Soans (1966), p. 438: “Computer machines have been used for years, but I have never heard of a patent claim which was invented by a computer. A new patentable concept is universally regarded as a mental creation by a human being, or by a group of human beings if a joint invention. It would seem that a computer can be used by Mr. Phosita (see below) to expedite the ex post facto reconstruction of the applicant’s claimed subject matter by a hindsight approach after the invention has been made. But first the invention must be created and programmed by human beings”.

  59. 59.

    As underlined by Abbott (2016b), p. 188 “even if computers cannot be legal inventors, it should still be possible to patent computational inventions. This is because recognition of inventive subject matter can qualify as inventive activity. Thus, individuals who subsequently ‘discover’ computational inventions may qualify as inventors”.

  60. 60.

  61. 61.

    In the specific, the inventions concerned a “food container” and “devices and methods for attracting enhanced attention”.

  62. 62.

    See UK Patent applications GB1816909.4 and GB1818161.0.

  63. 63.

    See EPO Patent Applications EP3564144 and EP3563896.

  64. 64.

    Morgan (2019); Thomsen (2020); Woollacott (2020).

  65. 65.

    Vertinsky and Rice (2002); Kohlhepp (2008); Fraser (2015); Abbott (2016b); Pearlman (2018); Lauber-Rönsberg and Hetmank (2019).

  66. 66.

    See Soans (1966), p. 438, who has defined the PHOSITA as “a (non-statutory) sort of Frankenstein monster created, not by Congress, but by Courts inspired by poachers’ lawyers (enemies of patents)”.

  67. 67.

    Khanna and Gulati (2018), p. 591.

  68. 68.


  69. 69.

    The only reference to the PHOSITA in the TRIPS Agreement is in Art. 29.1, which states that: “Members shall require that an applicant for a patent shall disclose the invention in a manner sufficiently clear and complete for the invention to be carried out by a person skilled in the art and may require the applicant to indicate the best mode for carrying out the invention known to the inventor at the filing date or, where priority is claimed, at the priority date of the application.” Hence in the TRIPS framework the PHOSITA comes into play only for the purpose of assessing the disclosure requirement for patent application. See Khanna and Gulati (2018), p. 592.

  70. 70.

    See for instance Leonard (1974), which underlines the problems arising for lawyers in discussing non-obviousness before the courts.

  71. 71.

    EPO, Guidelines For Examination, Part G, Chapter VII, Person Skilled in the Art, available at

  72. 72.

    EPO, Boards of Appeal, T-1464/05, 37.

  73. 73.

  74. 74.

    See Mast-Foos & Co. v. Stover, 177 U.S 485, 493 (1900).

  75. 75.


  76. 76.

    In re Wood, 599 F.2d 1032, 1037 (Fed. Cir. 1979).

  77. 77.

    In Re Winslow, 365 F.2d 1017, 1020 (Fed. Cir. 1966).

  78. 78.

    See Maera (2002); Tull and Miller (2018), p. 319; Abbott (2018), p. 4.

  79. 79.

    According to Ramalho (2018), p. 1, “when it comes to AI-generated inventions, it is thus in the requirement for patentability of inventive step or non-obviousness that most problems lie”.

  80. 80.

    See Wrzeszczynski et al. (2017) which found that IBM’s Watson is able to interpret a patient’s entire genome sequence in ten minutes, while a team of human experts takes around 160 h.

  81. 81.

    Abbott Labs v. Sandoz, Inc., 544 F.3d 1341, 1352 (Fed. Cir. 2008); see alsoEnvironmental Designs v. Union Oil Co. of Cal., 713 F.2d 693, 696, 218 USPQ 865, 868 (Fed. Cir. 1983), where the Federal Circuit stated that the relevant factor for the determination of the PHOSITA are “(1) educational level of the inventor; (2) types of problems encountered in the art; (3) prior art solutions to those problems; (4) rapidity with which inventions are made; (5) sophistication of the technology; and (6) educational level of active workers in the field”.

  82. 82.

    Simon (2013), p. 117.

  83. 83.

    Vertinsky and Rice (2002), p. 104–105; Darrow (2009), pp. 234–235; Fraser (2015), p. 320; Abbott (2018).

  84. 84.

    Renbarger (2007); Mueller (2008); Atkinson and Jones (2011); Holman, Minssen, and Solovy (2018).

  85. 85.

    Sherkow (2011), pp. 1121–1122.

  86. 86.

    IBM’s AI system Watson is a good example of such a machine. On this topic see Duch, Swaminathan and Meller (2007); Zhang et al. (2017); Fujiwara, Kamada, and Okuno (2018).

  87. 87.

    Ducor (1996), p. 461, who has stated in particular that “[T]he screening of combinatorial molecular libraries by high-throughput receptor assays is potentially powerful enough to render its products (ligands) unpatentable due to obviousness”. On non-obviousness in relation to pharmaceutical patents see Roin (2009); and Outterson (2010), criticizing Roin’s thesis.

  88. 88.

    On combination drugs see e.g. Youn et al. (2015).

  89. 89.

    Leonard (1974), p. 20, according to which: “Despite the high skill level attributed to a PHOSITA by the Supreme Court, in practice the ability attributed to this fictional person has been significantly decreased by the lower courts into what the Supreme Court described in KSR v. Teleflex, as an automaton. However, post KSR, the skill level of these hypothetical persons has been raised to a new height”.

  90. 90.

    Lemley (2017), p. 1375. See also Trask (2008).

  91. 91.

    See e.g. EPO Boards of Appeal, T 889/02; T 542/03; T 1241/03; T 1599/06; T 1364/08.

  92. 92.

    Vertinsky and Rice (2002), p. 596; Plotkin (2009), p. 108; Fraser (2015), p. 320.

  93. 93.

    On secondary considerations see in general McClung (1985); Whelan (1987); Sheets (2011).

  94. 94.

    Samore (2013), p. 121.

  95. 95.

    Robbins (1964); Lai (2012), p. 614.

  96. 96.

    Whelan (1987), pp. 357–358; Darrow (2010), p. 49.

  97. 97.

    Graham v. John Deere at 18–19. See alsoIn re Piasecki, 745 F.2d 1468, 1473-1475 (Fed. Cir. 1984); Safety Car Heating & Lightning Co. v. General Electric Co., 155 F.2d, 937, 939 (2d Cir. 1946). On the “long-felt need” consideration and its development see Miller (2008).

  98. 98.

    EPO, Guidelines for Examination, Part G, Chapter VII., para. 10.3.

  99. 99.

    Haberman v. Jackel International Ltd [1999] EWHC Patents 269.

  100. 100.

    Turner (2015).

  101. 101.

    Darrow (2010), p. 49.

  102. 102.

    Whelan (1987), p. 358.

  103. 103.

    Putney (2013), p. 50 who notes that: “The language of Graham is indisputably permissive and not imperative: secondary considerations […] might be utilized. [They] may have relevancy”. For instance, in US case law the District Court for the Northern District of West Virginia, in Ortho-McNeil Pharm Inc. v. Mylan Labs. Inc., 348 F. Supp. 2d 713, 756–60 (N.D. W. Va. 2004), and the Federal Circuit, in Geo M. Martin Co. v. Alliance Mach. Sys. Int’l LLC, Nos. 2009-1132, 2009-1151, 2010 L 3275967 (Fed. Cir. Aug. 20, 2010), gave considerable relevance to secondary considerations; on the contrary, the same Federal Circuit, in Sundance Inc. v. Demonte Fabricating Ltd., 550 F.3d 1356, 1368 (Fed. Cir. 2008), almost completely dismissed the analysis of the objective indicia.

  104. 104.

    Darrow (2010), p. 51.

  105. 105.

    Whelan (1987), p. 358. See also Merges (1988); Duffy (2007); Merges and Duffy (2007).

  106. 106.

    Walker v. General Motors Corporation 362 F.2d 56, 59 (9th Cir. 1966).

  107. 107.

    Dann v. Johnston, 425 U.S. 219, 230 (1976).

  108. 108.

    Calmar Inc. v. Cook Chem. Co., 380 U.S. 949 (1965).

  109. 109.

    Colgate-Palmolive Co. v. Cook Chemical Co., 383 U.S. 1 (1966).

  110. 110.

    For this thesis see Lunney (2001); Duffy (2008); Wieker (2008); Jongjitirat (2008).

  111. 111.

    Amongst the first advocates of the importance of the secondary considerations was in particular Judge Learned Hand. In B.G. Corp v. Walter Kidde & Co., 79 F.2d, 20, 22 (2d Cir. 1935) he underlined that “when [secondary considerations are] not at hand, we are forced to fabricate a standard as best we can from our naive ignorance; but that is so unsatisfactory an expedient that resort to it should be as sparing as possible. In either case, whether we have evidence, or must grope unguided, those putatively objective principles by which it is so often supposed that invention can be detected are illusion, and the product of unconscious equivocation; the inexorable syllogism which appears to compel the conclusion is a sham”.

  112. 112.

    Stratoflex Inc. v. Aeroquip Corp., 713 F.2d 1530, 1538 (1983).

  113. 113.


  114. 114.


  115. 115.

    Ibid. See alsoTransocean Offshore Deepwater Drilling Inc. v. Maersk Drilling USA Inc., 699 F.3d 1340, 1349 (2012), where the Federal Circuit argued that objective evidence of non-obviousness is an important component of the obviousness inquiry because “evidence of secondary considerations may often be the most probative and cogent evidence in the record. It may often establish that an invention appearing to have been obvious in light of the prior art was not”.

  116. 116.

    This view seems to be shared by Durie and Lemley (2008); Wieker (2008); Thomas (2011); Nock and Gadde (2011); contra Sheets (2011).

  117. 117.

    Putney (2013), p. 46.

  118. 118.

    Contra see, however, Sheets (2011), p. 11, who maintains that the KSR decision “undoubtedly made an initial determination of obviousness easier to justify, which, in turn, lessened the impact of secondary considerations in the analysis”.

  119. 119.

    Darrow (2010), p. 55.

  120. 120.

    Hawley (2014); but contra see Putney (2013), p. 51, according to which “a careful examination of Federal Circuit cases following KSR through June 2012 reveals that, more often than not, secondary considerations were summarily dismissed as insufficient or undercut through a stringent interpretation of the nexus requirement”, and that, in particular, “courts tend to recite and rely on secondary considerations when finding patents nonobvious, but marginalize or disparage them when finding patents obvious and invalid”.

  121. 121.

    Plantronics Inc. v. Aliph Inc., 724 F.3d 1343 (Fed. Cir. 2013). See alsoPfizer Inc. v. Apotex Inc., 480 F.3d 1348, 1360 (Fed. Cir. 2007); Richardson-Vicks Inc. v. Upjohn Co., 1223 F.3d 1476, 1483 (Fed. Cir. 1997).

  122. 122.

    Apple Inc. v. Int‘l Trade Comm’n, 725 F.3d 1356 (Fed. Cir. 2013).

  123. 123.


  124. 124.


  125. 125.


  126. 126.

    ClassCo Inc. v. Apple Inc., 838 F.3d 1214, 1220 (Fed. Cir. 2016).

  127. 127.

    Mandel (2006), p. 1425.

  128. 128.

    Argentum Pharm. LLC et al. v. Research Corp. Tech. Inc.

  129. 129.

    Varian Medical Systems, Inc. v. William Beaumont Hospital.

  130. 130.

    Leo Pharm. Prods. Ltd. v. Rea, 726 F.3d 1346 (Fed. Cir. 2013).

  131. 131.


  132. 132.


  133. 133.

    See Judge Learned Hand’s opinion in Safety Car Heating & Lighting Co. v. Gen. Elec. Co., 155 F.2d 937, 939 (2d Cir. 1946), which highlighted that “[t]he most reliable test is to look at the situation before and after it appears […] Courts, made up of laymen as they must be, are likely either to underrate, or to overrate, the difficulties in making new and profitable discoveries in fields with which they cannot be familiar; and, so far as it is available, they had best appraise the originality involved by the circumstances which preceded, attended and succeeded the appearance of the invention. […] We have repeatedly declared that in our judgment this approach is more reliable then prior conclusions drawn from vaporous, and almost inevitably self-dependent, general propositions”.

  134. 134.

    Durie and Lemley (2008), p. 1005.

  135. 135.

    See for instance Durie and Lemley (2008), p. 1004, which maintain that “secondary considerations represent patentees’ best hope of demonstrating non-obviousness in the post-KSR world for several reasons”.

  136. 136.

    Visser et al. (2018), sub-Art. 56; Holman, Minssen, and Solovy (2018), p. 155.

  137. 137.

    EPO, Case Law of the Boards of Appeal, 8th edn., 2016, I. D.10.1.

  138. 138.

    Boards of Appeal of the EPO, decision of 31 July 2011, case T-877/99, 3.6.4. See also cases T-645/94, T-284/96, T-323/99.

  139. 139.

    Conley (2011).

  140. 140.

    Holman, Minssen and Solovy (2018), p. 155. See also Jaenichen et al. (2006) para 17.1.4.

  141. 141.

    Holman, Minssen, and Solovy (2018), p. 156.

  142. 142.

    Antikainen (2018), p. 247; Hildebrandt (2016); Burrel (2016).

  143. 143.

    Nidhra and Dondeti (2012).

  144. 144.

    As underlined by Darrow (2010), p. 48: “Unfortunately, since Graham the doctrine of secondary considerations has been neither deliberately developed nor evenly applied. No judicial decision or secondary source has established itself as an accepted model for subsequent courts to follow. Acting within this statutory and judicial void, most courts have haphazardly applied whatever secondary considerations parties have troubled themselves to assert, with predictably erratic results”.


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Fabris, D. From the PHOSITA to the MOSITA: Will “Secondary Considerations” Save Pharmaceutical Patents from Artificial Intelligence?. IIC 51, 685–708 (2020).

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  • Artificial intelligence
  • Patent law
  • Pharmaceutical patents
  • Non-obviousness
  • Inventive step
  • Big data