Patent Drafting for Machine Learning: Structural Claim Limitations, Avoiding 101 or 112 Rejections

Transcription

1 Presenting a live 90-minute webinar with interactive Q&A Patent Drafting for Machine Learning: Structural Claim Limitations, Avoiding 101 or 112 Rejections THURSDAY, AUGUST 16, pm Eastern 12pm Central 11am Mountain 10am Pacific Today s faculty features: Gregory Rabin, Patent Attorney, Schwegman Lundberg & Woessner, New York Michael D. Stein, Partner, Baker & Hostetler, Seattle The audio portion of the conference may be accessed via the telephone or by using your computer's speakers. Please refer to the instructions ed to registrants for additional information. If you have any questions, please contact Customer Service at ext. 1.

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5 Patent Drafting for Machine Learning: Structural Claim Limitations, Avoiding 101 or 112 Rejections Greg Rabin August 16, 2018

6 Introduction Greg Rabin Patent attorney at Schwegman, Lundberg, and Woessner, PC, based in Ithaca, NY. 6

7 Disclaimer The subject matter of this presentation is the speaker s opinion only, and does not necessarily correspond to the opinions of the speaker s employer or clients. The information in this presentation may not correspond to constantly changing information/ guidance provided by the courts or the USPTO. 7

8 What is Machine Learning? Machine Learning gives computers the ability to learn without being explicitly programmed. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs. Example applications include filtering, detection of network intruders, optical character recognition (OCR), and computer vision. Source: Wikipedia: Machine Learning [ 8

9 Types of Machine Learning Supervised Learning: The computer is presented with example inputs and their desired outputs, given by a teacher, and the goal is to learn a general rule that maps inputs to outputs. E.g. The animals in the picture are labeled as cat or dog, and the computer trains itself to identify other animals, in other pictures, as either cat or dog. 9

10 Types of Machine Learning Unsupervised Learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning). E.g. Classify the animals below into different types. 10

11 Types of Machine Learning Reinforcement Learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent). The program is provided feedback in terms of rewards and punishments as it navigates its problem space. E.g. Learn to drive 11

12 Example ML Claim Ver. A 1. A method comprising: accessing, at one or more computing devices, an image; determining whether the accessed image includes a cat; and providing an output indicating whether the accessed image includes the cat. 12

13 Example ML Claim Ver. A This claim is directed to cat-recognition technology. It is likely invalid under 35 U.S.C. 101 because it preempts all other cat-recognition technologies. Every algorithm that identifies cats will access an image, determine whether the image includes a cat, and provide a corresponding output. 13

14 Example ML Claim Ver. A This claim might become patentable by Adding claim limitations related to training the machine learning (Ver. B). Adding claim limitations related to the feature vector used in the machine learning (Ver. C). Adding claim limitations related to the output of the machine learning (Ver. D). 14

15 Example ML Claim Ver. B 1. A method comprising: accessing, at one or more computing devices, an image; determining, using an image classification engine stored in one or more memories of the one or more computing devices, whether the accessed image includes a cat, the image classification engine being trained, using unsupervised learning, to classify images into a plurality of classes and being notified, via a graphical user interface (GUI), which classes correspond to cats, the GUI displaying at least one image from each class of the plurality of classes; and providing an output indicating whether the accessed image includes the cat. 15

16 Example ML Claim Ver. B The method claim in Version B does not preempt all use of cat-recognition technology. Rather, the method claim details a specific technique by which the one or more computing devices are trained to recognize cats. 16

17 Example ML Claim Ver. B Other inventors could possibly invent other catrecognition techniques that include different operations/ components from those listed in Version B of claim 1, for example, supervised learning or reinforcement learning could be used. 17

18 Example ML Claim Ver. B It should be noted that the Version B claim might still be invalid and some Examiners may insist on more implementation details being added to the claim before finding it patentable under 35 U.S.C

19 Example ML Claim Ver. C 1. A method comprising: accessing, at one or more computing devices, an image; determining, based on a feature vector comprising a plurality of features that are extracted from the accessed image, whether the accessed image includes a cat, the plurality of features comprising at least an identification of a portion of the accessed image as representing a paw, a symmetry of the paw, and a shape of the paw; and providing an output indicating whether the accessed image includes the cat. 19

20 Example ML Claim Ver. C The method claim in Version C does not preempt all use of cat-recognition technology. Rather, the method claim details a specific technique by which the one or more computing devices recognize cats using explicitly specified features of a feature vector. 20

21 Example ML Claim Ver. C Other inventors could possibly invent other catrecognition techniques that include different operations/ components from those listed in Version C of claim 1. For example, features other than the paw can be used to recognize the cat. 21

22 Example ML Claim Ver. C It should be noted that the Version C claim might still be invalid and some Examiners may insist on more implementation details being added to the claim before finding it patentable under 35 U.S.C

23 Example ML Claim Ver. C In Enfish, LLC v. Microsoft Corp., 2016 WL (Fed. Cir. May 12, 2016)., the Federal Circuit held that claims are directed to patentable subject matter if the focus of the claims is on the specific asserted improvement in computer capabilities. Feature vector of Version C Possibly also the image classification engine of Version B 23

24 Example ML Claim Ver. D 1. A method comprising: accessing, at one or more computing devices, an image; determining whether the accessed image includes a cat; and providing an output indicating whether the accessed image includes the cat, the output comprising overlaying the accessed image with a first text if the accessed image includes the cat, and overlaying the accessed image with a second text, different from the first text, if the accessed image does not include the cat. 24

25 Example ML Claim Ver. D The method claim in Version D does not preempt all use of cat-recognition technology. Rather, the method claim details a specific technique by which the one or more computing devices output information representing that a cat was recognized in an image. 25

26 Example ML Claim Ver. D In Core Wireless Licensing S.A.R.L. v. LG Electronics, Inc., , , decided January 25, 2018, the Court of Appeals for the Federal Circuit held that claims directed to a specific manner of displaying a limited set of information to the user, are not abstract. This decision could potentially be read to cover all software patents that claim novel GUIs. 26

27 Example ML Claim Ver. D Other inventors could possibly invent other catrecognition techniques that include different operations/ components from those listed in Version D of claim 1, for example, the output could be presented in a different format. 27

28 A Real-Life Example from Public PAIR ADDITIONAL DISCLAIMER The real-life example described here is not, in any way, representative of a 101 trend. The speaker makes no guarantees that this claim, or any similar claim, will be upheld in litigation, in review by the Patent Office, or in another patent application. The real-life example is only one example of a published patent. The slides contain excepts from the file wrapper history and do not include the entire history. 28

29 A Real-Life Example from Public PAIR The real-life example corresponds to a US patent application, filed in February 2016, which lead to a patent that issued in August The real-life example relates to uses of machine learning in a question answering system. Application No. 15/053,808 Now US Patent No. 9,720,981 Title: Multiple Instance Machine Learning for Question Answering Systems 29

30 A Real-Life Example from Public PAIR Note: All data used in this presentation related to the above-referenced patent application is copied from Public PAIR, patft.uspto.gov, patents.google.com, and other publicly accessible sources. The speaker and the speaker s employer were not involved in the drafting or prosecution of the above-noted patent application. 30

31 Claim Rejected Under 35 U.S.C A method, in a data processing system, for question answering using multi-instance learning, the method comprising: training an answer ranking multi-instance learned model using a ground truth question and answer-key pair set; receiving an input question from a user; generating one or more candidate answers to the input question, wherein each of the one or more candidate answers has an associated set of supporting passages; determining a confidence value for each of the one or more candidate answers using the answer ranking multi-instance learned model based on the set of supporting passages; ranking the one or more candidate answers by confidence value to form a ranked set of answers; and presenting a final answer from the ranked set of answers, the confidence value for the final answer, and supporting evidence for the final answer to the user. 31

32 Amendment Leading to Patent-Eligibility Under A method, in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to implement a question answering system, for question answering using multi-instance learning, the method comprising: training an answer ranking multi-instance learned model using a ground truth question and answer-key pair set, wherein the data processing system executes in accordance with the answer ranking multi-instance learned model to implement the question answering system, wherein the answer-ranking multi-instance learned model is trained based on whether a passage set returned for each answer in the question and answer-key pairs set collectively does or does not support a correct answer; receiving, by the question answering system, an input question from a user; 32

33 Amendment Leading to Patent-Eligibility Under 101 generating, by a hypothesis generation phase of a question answering pipeline of the question answering system, one or more candidate answers to the input question, wherein each of the one or more candidate answers has an associated set of supporting passages; determining, by a hypothesis and evidence scoring phase of the question answering pipeline of the question answering system, a confidence value for each of the one or more candidate answers using the answer ranking multi-instance learned model based on the set of supporting passages; ranking, by an answer ranking phase of the question answering pipeline of the question answering system, the one or more candidate answers by confidence value to form a ranked set of answers; and presenting, by the question answering system, a final answer from the ranked set of answers, the confidence value for the final answer, and supporting evidence for the final answer to the user. 33

34 Result of this Amendment 35 U.S.C. 101 (patent-eligibility) rejection was withdrawn. 35 U.S.C. 102 (novelty) rejection was withdrawn. A new rejection under 35 U.S.C. 103 (nonobviousness) was entered. The Applicants eventually overcame the 35 U.S.C. 103 rejection, and a US Patent was issued in August

35 Discussion of Claim Amendments The response states: Applicants amend claim 1 to more specifically recite that the data processing system executes in accordance with an answer ranking multiinstance learned model to implement the question answering system and to recite a hypothesis generation phase, a hypothesis and evidence scoring phase, and an answer ranking phase of a question answering pipeline of the question answering system Therefore, independent claim 1 recites a specific configuration of, and improvement to, a data processing system. 35

36 Discussion of Claim Amendments The amended claim specifically recites the training mechanism used, and the phases for generating the answer. (Similar to Version B of the cat-recognition claim). Note that the amended claim does not preempt all question answering solutions because other solutions could use a different training mechanism and, possibly, different phases for generating the answer. 36

37 Take Away Points Claiming features related to the machine training mechanism, feature extraction, or output formats may be sufficient to overcome a 35 U.S.C. 101 rejection for a machine learningrelated patent application. However, nothing is guaranteed, and it is necessary to consider the specific features of any given invention before deciding which claim amendments or arguments would best advance prosecution in view of a 35 U.S.C. 101 rejection. 37

38 101 Rejections by Art Unit 101/Alice rejections are more commonly issued in some technology centers/ art units than in others. One strategy may be to draft a patent application to encourage or avoid its entry into one of the technology centers/ art units. Source: 38

39 Bad Art Units 3600: Transportation, Electronic Commerce, Construction, Agriculture, Licensing and Review. 3700: Mechanical Engineering, Manufacturing and Products. 39

40 Good Art Units 2100: Computer Architecture Software and Information Security. 2800: Semiconductors, Electrical and Optical Systems and Components. 2600: Communications. 2400: Computer Networks, Multiplex, Cable and Cryptography/Security. 1600: Biotechnology and Organic fields. 40

41 Potential Strategy Draft the title and the abstract of the patent application such that it falls into one of the good art units. As a result, the patent application might be more likely to avoid or ultimately overcome a 101/Alice rejection. 41

42 New Machine Learning Patent Case Berkheimer v. HP, Inc., , decided February 8, 2018 USPTO Berkheimer Memorandum: memo-berkheimer pdf 42

43 Representative Claim 1. A method of archiving an item comprising in a computer processing system: presenting the item to a parser; parsing the item into a plurality of multi-part object structures wherein portions of the structures have searchable information tags associated therewith; 43

44 Representative Claim evaluating the object structures in accordance with object structures previously stored in an archive; presenting an evaluated object structure for manual reconciliation at least where there is a predetermined variance between the object and at least one of a predetermined standard and a user defined rule. 44

45 Abstract Idea: Yes The Federal Circuit found that these claims are similar to claims we held directed to an abstract idea in prior cases. Specifically, the specification explains that the parser determines and extracts components of the standardized document or item representation and reassembles the components into composite output files. 45

46 Abstract Idea: Yes Even though the parser separates the documents or items into smaller components than the claims determined to be abstract in Content Extraction and TLI, the concept is the same. The parsing and comparing of claims 1-3 and 9 are similar to the collecting and recognizing of Content Extraction, 776 F.3d at 1347, and the classifying in an organized manner of TLI, 823 F.3d at

47 Something More: Yes/No The conventional limitations of claim 1 (including parsers), combined with limitations of analyzing and comparing data and reconciling differences between the data, fail to transform the abstract idea into a patent-eligible invention. Alice, 134 S. Ct. at

48 Dependent Claims While the Federal Circuit found that the independent claim of Berkheimer was patentineligible under 35 U.S.C. 101, the Federal Circuit held that a finding of ineligibility of some of the dependent claims was improper at the summary judgment stage, based on the record available to the trial court (US District Court for the Northern District of Illinois). 48

49 Dependent Claim 4 4. The method as in claim 1 which includes storing a reconciled object structure in the archive without substantial redundancy. 49

50 Dependent Claim 4 Claim 4 contains limitations directed to the arguably unconventional inventive concept described in the specification. Claim 4 recites storing a reconciled object structure in the archive without substantial redundancy.... This claim recites a specific method of archiving that, according to the specification, provides benefits that improve computer functionality. 50

51 Dependent Claim 4 We do not decide today that claims 4-7 are patent eligible under 101. We only decide that on this record summary judgment was improper, given the fact questions created by the specification s disclosure. 51

52 USPTO Berkheimer Memo On April 19, 2018, Robert W. Bahr, Deputy Commissioner for Patent Examination Policy, published a memorandum titled Changes in Examination Procedure Pertaining to Subject Matter Eligibility, Recent Subject Matter Eligibility Decision (Berkheimer v. HP, Inc.). The Berkheimer Memorandum instructs Examiners how to determine whether claim elements correspond to an abstract idea and not something more. 52

53 USPTO Berkheimer Memo An additional element (or combination of elements) is not well-understood, routine or conventional unless the examiner finds, and expressly supports a rejection in writing with, one or more of the following: 53

54 USPTO Berkheimer Memo 1. A citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates the well-understood, routine, conventional nature of the additional element(s) A finding that an element is well-understood, routine, or conventional cannot be based only on the fact that the specification is silent with respect to describing such element. 54

55 USPTO Berkheimer Memo 2. A citation to one or more court decisions noting the well-understood, routine, conventional nature of the additional element(s). 55

56 USPTO Berkheimer Memo 3. A citation to a publication that describes the state of the art and discusses what is well-known and in common use in the relevant industry Merely finding the additional element in a single patent or published application would not be sufficient to demonstrate that the additional element is well-understood, routine, conventional, unless the patent or published application demonstrates that the additional element are widely prevalent or in common use in the relevant field. 56

57 USPTO Berkheimer Memo 4. A statement that the examiner is taking official notice of the well-understood, routine, conventional nature of the additional element(s). This option should be used only when the examiner is certain, based upon his or her personal knowledge, that the additional element(s) represents well-understood, routine, conventional activity engaged in by those in the relevant art. 57

58 Discussion of Berkheimer Memo In order to find a patent claim ineligible for patenting under 35 U.S.C. 101 as abstract and not something more in view of Alice and Berkheimer, the Examiner now must demonstrate that the claim elements were widely prevalent or in common use in the relevant field, on the filing date. 58

59 Discussion of Berkheimer Memo If a claim can overcome the 35 U.S.C. 102 (novelty) and 35 U.S.C. 103 (non-obviousness) rejections, then it is very likely that the claim elements were not widely prevalent or in common use in the relevant field, on the filing date. 59

60 Discussion of Berkheimer Memo The Berkheimer Memo appears to strengthen patents and significantly weaken the Patent Examiner s ability to issue 35 U.S.C. 101 rejections on claims that are otherwise allowable under 35 U.S.C. 102 and 35 U.S.C The impact of the Berkheimer Memo on machine-learning patent applications remains to be seen. 60

61 Invention Disclosure Approval Does the invention provide a technical solution for a technical problem in the field of computer science? Does the invention disclosure identify data structures and describe the use of a computer, rather than focus on mathematical techniques, which can be implemented (more tediously) using a pen and paper? 61

62 Invention Disclosure Approval If the invention might be directed to an abstract idea, what is the something more on which the drafting and prosecuting attorney could rely to overcome a potential 35 U.S.C. 101 rejection? 62

63 Thank you for your participation. Questions? Greg Rabin

64 Protecting Software Inventions Including AI and ML Algorithms Michael D. Stein, BakerHostetler LLP Presented for Strafford Patent Drafting for Machine Learning: Structural Claim Limitations, Avoiding 101 or 112 Rejections Aug. 16, 2018

65 Outline 1. Patenting AI/ML Inventions a. Software patents and functional claim limitations b. Structural claim limitations for software c. Inventorship questions 2. Trade Secret Protection 3. Strategic Use of Patent and Trade Secret Protection 65

66 I. Problem Overview Litigation USPTO Legal Solutions: Use 101 to invalidate overly broad ( abstract ) claims (Alice/Mayo) Use 112(b) to invalidate unclear claims (Aristocrat) Problem: Courts lack guidance/procedure to effectively apply the law. Courts lack context for evaluating claims from perspective of POSITA. Too many patents are being invalidated on incorrect grounds! Alternative Solution: Include structural limitations in claims for software-related inventions. Applicants and USPTO ensure patent file history reflects examiner s consideration of 101 and 112 issues. Provide context for courts. 66

67 Background Critics argue: Patents do less good and cause more harm in the software industry than in other industries. The aggregation of overbroad and unclear claims can result in innovation-stifling patent thickets. Software patents are not necessary to spur innovation because software innovation is less costly than innovation in the life sciences. 67

68 Why functional claim limitations are used in software patents Why functional claiming is so common in software patents: 1. Software structure and function can be separated. A software developer can write new software without knowing details of the hardware. 2. The software industry lacks a commonly accepted vocabulary for defining software elements. In software, a broad claim requires defining the invention at a higher level of abstraction. Software developers coin new terms to define the functional elements of the software, and the meaning and scope of such new terms is often not explicitly defined. 68

69 Functional claim limitations and 35 USC 112(f) 35 U.S.C. 112 Specification.... (f) ELEMENT IN CLAIM FOR A COMBINATION. An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. 69

70 Aristocrat Techs. Australia Pty Ltd, v. Int l Game Tech., 521 F.3d 1328 (Fed. Cir. 2008) Claim Disclosed Structure 70

71 Program structures Data structures Structural claim limitations for software 71

72 Describing computer software in terms of structure Essential feature(not shown): Relationship between the executable software and hardware processor(s). The executable code is not an unconstrained, abstract set of commands written down by the programmer. The executable code must be committed to memory using machine codes selected from the specific machine language instruction set, or native instructions, designed into the hardware processor. The native instruction set is known to, and essentially built into, the hardware processor(s). Each native instruction is a discrete code recognized by the processing architecture and can specify particular registers for arithmetic, addressing, or control functions. Complex operations are built up by combining the simple native instructions. 72

73 Describing computer software in terms of structure Key ideas: Relationship between executable software instructions and hardware processor constrains the claimed system. Problems related to abstractness, indefiniteness, and overbreadth can be addressed by describing inventive computer software elements in the specification at this level of detail, and including appropriate limitations in the claims. I do not advocate inclusion of specific machine code or even source code listings in patent applications. 73

74 Example: Artificial Neural Network 74

75 Example claim to computer-implemented system comprising an ANN 1. A classification system, comprising: a processor configured to execute instructions programmed using a predefined set of machine codes; and an artificial neural network (ANN) comprising: first, second, and third input nodes, wherein each input node includes a memory location for storing an input value; first, second, third, and fourth hidden nodes, wherein each hidden node is connected to each input node and includes computational instructions, implemented in machine codes of the processor, for computing first, second, third, and fourth output numbers, respectively; and first and second output nodes, wherein the first output node includes a memory location for storing a first output signal indicative of a first classification, and the second output node includes a memory location for storing a second output signal indicative of a second classification. 75

76 Example: Linked List Data Structure and Linear Search Algorithm Boolean logic expressions and operators: Logical operands and logical operators are combined together. Operands are statements (that can be proven True or False) and the operators are logical AND, OR and NOT. Boolean expressions involve comparison operators that can be evaluated to determine if they are True or False. Comparison operators include: = < >. Expressions combining Boolean operators and comparison operators are written and evaluated in computer programming languages using control statements that control which sections of code in a program are executed. Decision or Conditional statements are a type of Control statement and are often referred to as IF..THEN..ELSE statements. Iterative constructs, or Loops, allow a section of code to be repeated. There are several variations to iterative or looping constructs but for the most part they fall into two categories: FOR loops and WHILE/DO loops. Pseudocode for a linear search algorithm implemented with Boolean logic and FOR and IF..THEN loops: procedure linear_search (list, value) for each item in the list if match item == value return the item's location end if end for end procedure 76

77 Example claim to computer-implemented search system comprising a linked list data structure 1. A search system, comprising: a processor configured to execute instructions programmed using a predefined set of machine codes; memory coupled to the processor; a linked-list data structure implemented in said memory, comprising a first node, a plurality of intermediate nodes, and a terminator; wherein the first node is connected to a first intermediate node; wherein each of intermediate nodes except for a last intermediate node is connected to a subsequent one of the intermediate nodes; and wherein the last intermediate node is connected to the terminator; and a linear search algorithm implemented in machine codes for the processor. 2. The system of claim 1, wherein the linear search algorithm comprises an iterative construct and a Boolean logic expression. 77

78 78

79 PTAB Example: Ex parte Boucher (Dec. 8, 2017) 1. A method for a computer for assisting the maintenance of a system of an aircraft comprising a diagnostic device that performs computer-readable instructions using a processor for executing the steps of the method comprising: providing an onboard aircraft database in the aircraft, detecting, using the detector, a fault of the system of the aircraft; identifying said each item of the list of said at least one item being liable to be the cause of said detected fault determining said probability of the corresponding cause of the identified fault identifying said at least one item of said equipment of the aircraft actually being the cause of said detected fault; updating an onboard temporary database on a basis of said fault identifier sending a content of said onboard temporary database to said remote ground database erasing said onboard temporary database upon completion of the update determining said probability of the corresponding cause of the identified fault ; and storing both of the onboard aircraft and temporary databases in the system of said aircraft. 79

80 PTAB Example: Ex parte Blackwell (Nov. 2, 2017) 1. A method comprising: providing search results in a first dedicated screen space of a user interface and associated with a first collection based on at least one search term, and which excludes search results associated with any of one or more second collections; providing a separate customizable preview of search results in a separate pane located in a second dedicated screen space of the user interface ; and providing options to a user to customize the second dedicated space and the separate pane in the user interface that is configured to provide customization controls which: allow the user to remove the separate pane in the second dedicated space; order the separate pane with at least one other customizable preview pane in the second dedicated space; and allow the user to add another pane in the second dedicated space, wherein the first collection is a first category of information for the at least one search term and the at least one second collection includes a second category of information for the at least one search term which is different from the first category of information. 80

81 Inventorship Questions Who is the inventor? 35 U.S.C. 100 (definitions) (f) The term "inventor" means the individual or, if a joint invention, the individuals collectively who invented or discovered the subject matter of the invention. (g) The terms "joint inventor" and "coinventor" mean any 1 of the individuals who invented or discovered the subject matter of a joint invention 35 U.S.C. 115 (Inventor s oath or declaration) (a) NAMING THE INVENTOR; each individual who is the inventor or a joint inventor of a claimed invention in an application for patent shall execute an oath or declaration 81

82 Inventorship Questions Improper Naming of Inventors MPEP 2157 Improper Naming of Inventors Although the AIA eliminated pre-aia 35 U.S.C. 102(f), the patent laws still require the naming of the actual inventor The Office presumes that the named inventor or joint inventors in the application are the actual inventor or joint inventors 37 CFR 1.56 Duty to disclose information material to patentability the Office is aware of and evaluates the teachings of all information material to patentability. Each individual associated with the filing and prosecution of a patent application has a duty of candor and good faith in dealing with the Office, which includes a duty to disclose to the Office all information known to that individual to be material to patentability as defined in this section. Duty to Disclose Information on Inventorship MPEP Duty of Disclosure, Candor, and Good Faith In addition to prior art such as patents and publications, 37 CFR 1.56 includes, for example, information on enablement, possible prior public uses, sales, offers to sell, derived knowledge, prior invention by another, inventorship conflicts, and the like. 82

83 Trade Secret Protection What Is a Trade Secret? A trade secret is information ( formula,, pattern, compilation, program, device, method, technique, or process) that: (1) derives economic value from not being generally known or readily ascertainable by proper means; and (2) is the subject of "reasonable efforts" under the circumstances to maintain its secrecy. Trade Secrets Can Be Used As Sword and Shield Defend Trade Secrets Act (2016) DTSA created a private, federal, civil right of action for alleged victims of trade secrets theft, as well as a uniform federal definition of trade secret applicable in federal criminal and civil cases. The DTSA s definition of trade secret appears substantively identical to the UTSA 35 U.S.C. 273 (prior commercial use defense) In the presence of clear and convincing evidence that it had engaged in commercial use of the patented subject matter at least date, the prior user would be allowed free and clear historical and future use of that patented subject matter 83

84 Strategic Use of Patent & Trade Secret Protection Patent Protection Trade Secret Protection 84

85 Thank you! Questions? Michael Stein (206)