DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
In the amendment filed 11/13/2025 the following occurred: Claims 1-4, 7-11, and 14-18 13were amended. Claims 1-20 are presented for examination.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-20 are drawn to a method, system, and non-transitory computer readable storage medium, which is/are statutory categories of invention (Step 1: YES).
Independent claim 1 recites codifying a pre-existing informed consent document into machine actionable rules, wherein the machine actionable rules define what a patient has consented to be done with a specimen and associated data; linking a consent profile to the specimen and the associated data, the consent profile including the machine actionable rules; tracking changes to the machine actionable rules in the consent profile; generating a new consent document based at least in part on global regulations data for a plurality of locations, by using the machine actionable rules with any of the tracked changes and a regulatory intelligence knowledgebase (RIK) configured to learn regulatory data and consent approval behaviors, wherein the RIK includes the global regulations data; and displaying, using analytics of consent approval, regulatory information for at least one of the plurality of locations based on the new consent document and the consent profile.
Independent claim 8 recites codify a pre-existing informed consent document into machine actionable rules, wherein the machine actionable rules define what a patient has consented to be done with a specimen and associated data; link a consent profile to the specimen and the associated data, the consent profile including the machine actionable rules; track changes to the machine actionable rules in the consent profile; generate a new consent document based at least in part on global regulations data for a plurality of locations, by using the machine actionable rules with any of the tracked changes and a regulatory intelligence knowledgebase (RIK) configured to learn regulatory data and consent approval behaviors, wherein the RIK includes the global regulations data; and display, using analytics of consent approval, regulatory information for at least one of the plurality of locations based on the new consent document and the consent profile.
Independent claim 15 recites codifying a pre-existing informed consent document into machine actionable rules, wherein the machine actionable rules define what a patient has consented to be done with a specimen and associated data; link a consent profile to the specimen and the associated data, the consent profile including the machine actionable rules; tracking changes to the machine actionable rules in the consent profile; generating a new consent document based at least in part on global regulations data for a plurality of locations, by using the machine actionable rules with any of the tracked changes and a regulatory intelligence knowledgebase (RIK) configured to learn regulatory data and consent approval behaviors, wherein the RIK includes the global regulations data; and displaying, using analytics of consent approval, regulatory information for at least one of the plurality of locations based on the new consent document and the consent profile.
The respective dependent claims 2-7, 9-14 and 16-20, but for the inclusion of the additional elements specifically addressed below, provide recitations further limiting the invention of the independent claim(s).
The recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity, as reflected in the specification, which states that “present invention relates to consent data for human specimen research, and more specifically, to methods and systems for codification, tracking, and use of informed consent data for human specimen research” (see: specification paragraph 2). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. The present claims cover certain methods of organizing human activity because they address “a need [that] exists for improved methods and systems for managing informed consent data for human specimen research” (see: specification paragraph 5). This problem is addressed “[w]hen a patient provides a specimen, the system may create a consent profile for the patient that links various information to the specimen” (see: specification paragraph 28). Such a profile provides manages the activity between entities by making “it [] easier to track the patient's consent and any changes (e.g., withdrawal of consent) in order to ensure that organizations are managing specimens and their associated data in compliance with the applicable consent rules and regulations. Analytics can provide rapid assessments of risk or other metrics based on various granular searches using an interactive visual portal, such as a webpage or interactive application. Finally, the system may be used to help automatically generate new consent documents based on desired specifications such that the consent documents can be assembled very rapidly and in an automated manner by people without legal training” (see: specification paragraph 28). Accordingly, the claims recite an abstract idea(s) (Step 2A Prong One: YES).
This judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including an “using machine-learning…machine-learning…machine-learning…” (claim 1), “processor; and a non-transitory computer readable storage medium communicatively coupled to the processor, the non-transitory computer readable storage medium further comprising computer-readable instructions that, when executed by the processor, cause the processor to:…using machine-learning…machine-learning…machine-learning…” (claim 8), “the processor…” (claim 9), “the processor” (claim 10), “the processor…” (claim 13), “the processor…” (claim 14), and “non-transitory computer readable storage medium comprising computer-readable instructions executable by a processor to cause the processor to perform operations, the operations comprising:…using machine-learning…machine-learning…machine-learning…” (claim 15), which are additional elements that are recited at a high level of generality (e.g., the “processor; and a non-transitory computer readable storage medium communicatively coupled to the processor, the non-transitory computer readable storage medium further comprising computer-readable instructions” is configured through no more than a statement than that said stored instructions, “when executed by the processor, cause the processor to perform [the] operations” claimed; similarly, the “machine-learning” is merely “us[ed]” to perform functions of the invention, such as codifying, or is otherwise “configured to learn” behaviors; similarly, the “non-transitory computer readable storage medium comprising computer-readable instructions executable by a processor” is configured through no more than a statement than that said stored instructions “cause the processor to perform [the] operations” claimed) such that they amount to no more than mere instruction to apply the exception using generic computer components. See: MPEP 2106.05(f).
The combination of these additional elements is no more than mere instructions to apply the exception using generic computer components. Accordingly, even in combination, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s). Accordingly, the claims are directed to an abstract idea(s) (Step 2A Prong Two: NO).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea(s) into a practical application, using the additional elements to perform the abstract idea(s) amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using generic components cannot provide an inventive concept. See MPEP 2106.05(f).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are configured to perform well-understood, routine, and conventional activities previously known to the industry. See: MPEP 2106.05(d). Said additional elements are recited at a high level of generality and provide conventional functions that do not add meaningful limits to practicing the abstract idea(s). The originally filed specification supports this conclusion at Fig. 4-6, and:
Paragraph 20, where “…The system may further comprise a server with a processor and a memory configured to codify an informed consent document, attach consent rules to a specimen, track the consent rules and any changes to the consent rules, perform allowed use analysis of the specimen and associated data, and automatically generate a consent document using the codified informed consent document and the RIK.”
Paragraph 41, where “…Visual indicators may provide rapid visual assessment of risk. The global landscape analysis may be represented in the form of a map with indicators of risk overlaid on the map, together with filters to allow interactive visualization of different risk categories.”
Paragraph 42, where “…For example, as can be seen from FIG. 4A, a geographical map is displayed with colors-coded indicators of low, medium, and high risk. One or more risk filters may be adjusted by using the risk filter sliders in order to provide a lower and upper risk limit to be displayed on the map.”
Paragraph 45, where “…The RIK 508 may use machine learning 506 and statistical approaches to provide regulatory intelligence to inform decision making and forecast the risk of, for example, trying to collect genomic samples in a region of Germany.”
Paragraph 51, where “As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or computer program product. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a "circuit," "module" or "system." Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon.”
Paragraph 52, where “Any combination of one or more computer readable medium(s) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium (including, but not limited to, non-transitory computer readable storage media). A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CDROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.”
Paragraph 55, where “…The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server…”
Paragraph 56, where “…These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.”
Further, the concepts of receiving or transmitting data over a network, such as using the Internet to gather data, and storing and retrieving information in memory have been identified by the courts as well-understood, routine, and conventional activities. See: MPEP 2106.05(d)(II).
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea(s) with routine, conventional activity specified at a high level of generality in a particular technological environment. Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea(s) (Step 2B: NO).
Dependent claim(s) 2-7, 9-14 and 16-20, when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea(s) without significantly more. These claims fail to remedy the deficiencies of their parent claims above, and are therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4-6, 8, 11-13, 15, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication 2003/0033168 to Califano in view of U.S. Patent Application Publication 2013/0297626 to Levi in view of U.S. Patent Application Publication 2013/0054431 to Forman further in view of U.S. Patent Application Publication 2016/0078196 to Malbon.
As per claim 1, Califano teaches a method comprising (see: Califano, Fig 1; and paragraph 18, 52-54, and 131):
codifying a pre-existing informed consent document into machine actionable rules, wherein the machine actionable rules define what a patient has consented to be done with a specimen and associated data (see: Califano, paragraph 23, 69-70, 76, 91, 118, and 120);
tracking changes to the machine actionable rules in the consent profile (see: Califano, paragraph 21, 41, 45, 58, 67, 121, and 126);
generating a new consent document based at least in part on global regulations data for a plurality of locations, by using the machine actionable rules with any of the tracked changes and a regulatory intelligence knowledgebase (RIK) configured to learn regulatory data and consent approval behaviors, wherein the RIK includes the global regulations data (see: Califano, paragraph 42, 69-70, 75-76, 91-92, and 94); and
regulatory information for at least one of the plurality of locations based on the new consent document and the consent profile (see: Califano, paragraph 42, 69, 70, 76, and 91, is met by, because of local regulatory differences among geographic locales, informed consent forms are to be tailored to the study participant's location (state, country), and informed consent documents for a clinical site location are updated).
Ferguson fails to specifically teach codifying using machine-learning; however, Levi teaches extraction of policy information is performed using a hierarchy of classifiers, each one utilizing a machine learning algorithm and/or natural language processing (see: Levi, Fig. 3, and paragraph 4-7, 10-11, 12-14, 16, 19-21, 31-33, and 35-36).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the assembled and maintained database of templates and information extraction as taught by Califano with extraction of policy information is performed using a hierarchy of classifiers, each one utilizing a machine learning algorithm and/or natural language processing, as taught by Levi with the motivation of providing automatic extraction of policy information to improve transparency and interoperability (see: Levi, paragraph 45).
Ferguson fails to specifically tech that the knowledgebase is machine-learning; however, Forman teaches artificial intelligence software and databases that aid in interpreting regulatory requirements and formulating compliance decisions regarding Federal regulation requirements (see: Forman, paragraph 2).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the knowledgebase as taught by Califano and Levi by using artificial intelligence software and databases that aid in interpreting regulatory requirements and formulating compliance decisions regarding Federal regulation requirements as taught by Forman with the motivation of standardize and simplify transactions between the Federal government and entities that do business with it (see: Forman, paragraph 2).
Though Ferguson teaches an informed consent form manager screen (see: Ferguson, paragraph 125), Ferguson and Forman fail to specifically tech the following limitations met by Malbon as cited: linking a consent profile to the specimen and the associated data, the consent profile including the machine actionable rules (see: Malbon, paragraph 18, 25, 31, and 42 is met by electronic health records data is collected and prefiltered based on patient consent; a list of available specimens and corresponding clinical data in the form of EHR data associated with patients that have provided opt-in consent; consent status can be captured electronically for each patient, and consent status can then be used to filter data);
displaying, using analytics of consent approval (see: Malbon, paragraph 18, 25, 31, and 42, is met by portal may provide relevant information about specific specimens such as information about patient consent).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the system including informed consent forms and associated data and an informed consent form manager screen as taught by Califano, Levi, and Forman to include available specimens and corresponding clinical data in the form of EHR data associated with patients that have provided electronically captured opt-in consent that can then be used to filter data and to provide relevant information about specific specimens such as information about patient consent as taught by Malbon with the motivation of ensuring that only data from patients who have explicitly granted consent and authorization to participate is received (see: Malbon, paragraph 31).
As per claim 4, Califano, Levi, Forman, and Malbon teach the invention as claimed, see discussion of claim 1, and further teach:
further comprising irrevocably linking the data derived from the specimen with the consent profile (see: Malbon, paragraph 18, 25, 27, 31, and 42 is met by encrypted electronic health records data is collected and prefiltered based on patient consent; a list of available specimens and corresponding clinical data in the form of EHR data associated with patients that have provided opt-in consent; consent status can be captured electronically for each patient, and consent status can then be used to filter data).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the system including informed consent forms and associated data as taught by Califano, Levi, and Forman to include available specimens and corresponding clinical data in the form of EHR data associated with patients that have provided electronically captured opt-in consent that can then be encrypted and used to filter data as taught by Malbon with the motivation of ensuring that only data from patients who have explicitly granted consent and authorization to participate is received (see: Malbon, paragraph 31).
As per claim 5, Califano, Levi, Forman, and Malbon teach the invention as claimed, see discussion of claim 1, and further teach:
wherein the machine actionable rules are based at least in part on global, country, regional, and local regulations in force at a time of generating the new consent document (see: Califano, paragraph 69, 70, 76, and 91).
As per claim 6, Califano, Levi, Forman, and Malbon teach the invention as claimed, see discussion of claim 1, and further teach:
receiving an allowed use query for the specimen; and producing, in response to the allowed use query, an allowed use report based on at least some of the machine actionable rules (see: Califano, paragraph 22-23 and 57).
Claims 8, 11-13, 15, and 18-20 repeat the subject matter of claims 1 and 4-6, which have been shown to be fully disclosed by the cited prior art in the rejections above; as such, claims 8, 11-13, 15, and 18-20 are rejected here for the same reasons given in the above rejections of claims 1 and 4-6, which are incorporated herein.
Claims 2-3, 9-10, and 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication 2003/0033168 to Califano in view of U.S. Patent Application Publication 2013/0297626 to Levi in view of U.S. Patent Application Publication 2013/0054431 to Forman in view of U.S. Patent Application Publication 2016/0078196 to Malbon further in view of U.S. Patent Application Publication 2015/0178644 to Oleson.
As per claim 2, Califano, Levi, Forman, and Malbon teach the invention as claimed, see discussion of claim 1, and further teach:
displaying for collection of the specimen in association with the new consent document in the plurality of locations (see: Califano, paragraph 69, 70, 76, and 91).
Califano teaches recontact is triggered in the system when informed consent documents for a clinical site location are updated and upon the occurrence of new information which may alter prognosis or recurrence-risk estimates, but Califano fails to specifically teach associated risk metrics; however, Oleson teaches a particular site compliance metrics for a responsible IRB (see: Oleson, paragraph 46-48, 51, 55, 60, and 103).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the recontact triggering as taught by Califano, Levi, Forman, and Malbon to particular site compliance metrics for a responsible IRB as taught by Oleson with the motivation of identifying as a failure mode when a particular investigation site does not conform to particular site safety and protocol compliance (see: Oleson, paragraph 60).
As per claim 3, Califano, Levi, Forman, Malbon, and Oleson teach the invention as claimed, see discussion of claim 2, and further teach:
wherein the associated data comprises data on the patient, a collection site, a sample type, and data derived from the specimen (see: Califano, paragraph 53, 69, 76, 81, and 88-92).
Claims 9-10 and 16-17 repeat the subject matter of claims 2-3, which have been shown to be fully disclosed by the cited prior art in the rejections above; as such, claims 2-3, 9-10, and 16-17 are rejected here for the same reasons given in the above rejections of claims 2-3, which are incorporated herein.
Claims 7 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication 2003/0033168 to Califano in view of U.S. Patent Application Publication 2013/0297626 to Levi in view of U.S. Patent Application Publication 2013/0054431 to Forman in view of U.S. Patent Application Publication 2016/0078196 to Malbon further in view of U.S. Patent Application Publication 2012/0166229 to Collins.
As per claim 7, Califano, Levi, Forman, and Malbon teach the invention as claimed, see discussion of claim 1, and further teach:
displaying the visual risk indicators in association with a representation of the plurality of locations (see: Collins, Fig. 6; and paragraph 79).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the display of information as taught by Califano, Levi, Forman, and Malbon to include risk zone slider define specific magnitude of risk corresponding to a highlighted color of the risk zone on a map as taught by Collins with the motivation of it being advantageous in the case that the user’s insurance company offers reductions in insurance premiums for clients that conduct their business within certain risk zones and/or risk thresholds (see: Collins, paragraph 79).
Claim 14 repeat the subject matter of claim 7, which have been shown to be fully disclosed by the cited prior art in the rejections above; as such, claim 14 is rejected here for the same reasons given in the above rejection of claim 7, which are incorporated herein.
Response to Arguments
Applicant’s arguments from the response filed on 11/13/2025 have been fully considered and will be addressed below in the order in which they appeared.
In the remarks, Applicant argues in substance that (1) the 35 U.S.C. 101 rejections should be withdrawn in view of the amendments because “these recitations provide an ordered combination of features that weighs toward eligibility. These claim recitations specify a precise, computerized technique for securing a patient's consent information in a consent profile, and tracking changes, as well as linking this consent profile to the specimen, not merely stored data, because the consent profile includes information about what the patient permits and does not permit with regard to the specimen itself. This technique not only facilitates ensuring consent for a specific patient and a specific test, but this data can be used to evaluate how the consent given for the type of specimen involved would be affected by regulations in other locations, so that the laboratory or medical provider can determine how best to proceed if there is a need to use the specimen for a study in a different location. This technique, therefore, is not merely a method of organization human activity... The claims recite coding a consent document into machine-actional rules. Changes to the consent given by a subject are stored in a consent profile that is linked to the specimen sample and data associated with, or about, the specimen sample. Furthermore a machine-learning database determines whether consent is effectuated in different locations with varying laws and regulations about consent given the new consent document. These are specific operations that, as claimed, rely on computer technology. The claims, as amended, integrate the abstract idea into a practical application. Applicant also submits that the additional and amended recitations of the claims amount to significantly more than the abstract idea. The Office suggests that no recitations of the claims amount to significantly more because all of the recitations beyond the abstract idea are well-understood, routine, conventional. Examiners "should continue to consider ... whether an additional element or combination of elements: adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present)."… Applicant submits that, at least, the recitations regarding linking a consent profile to a specimen and its associated data, and displaying location-based regulatory information regarding the use of a new consent document with the subjects consent profile are not well-understood, routine, or conventional. The Office has not pointed to any express statement in the specification, court decision, or publication to suggest any of these disclosed concepts are well-understood, routine, or conventional.”
The Examiner respectfully disagrees. Applicant’s arguments are not persuasive.
The argued “technique” is representative of the abstract idea. That the technique has been “computerized” is considered in the rejection, where for example it is determined that the processor and its storage medium are configured through no more than a statement than that stored instructions, “when executed by the processor, cause the processor to perform [the] operations” claimed. It is argued that the invention is for to “evaluate how the consent given for the type of specimen involved would be affected by regulations in other locations”, which is an abstract determination involving no technology at the core of this concept and where the only technology claimed is applied with it at a high level. The argued goal of “so that the laboratory or medical provider can determine how best to proceed if there is a need to use the specimen for a study in a different location” is not one that addresses a technical problem. The claims here are not directed to a specific improvement to computer functionality that amount to a practical application. Rather, they are directed to the use of conventional or generic technology in a well-known environment, without any claim that the invention reflects an inventive solution to a technical problem presented by combining the two. In the present case, the claims fail to recite any elements that individually or as an ordered combination transform the identified abstract idea(s) in the rejection into a patent-eligible application of that idea.
The machine-actionable rules are broad and generally claimed such that the processing of and based on them is abstract. There are cases classified as certain methods of organizing human activity by the Office which are described by the courts similarly to the present claims, such as generating rule-based tasks for processing an insurance claim (Accenture). Even encoding and decoding image data is abstract (RecogniCorp). Hence, it is reasonable that “codifying” documents into machine actionable rules and “generating” new documents using said rules is abstract. Further, just because a rule is machine actionable, does not prevent it from being human-actionable as well, and the act of tacking rule changes and updating documents addresses an abstract problem, not a technological problem.
As per further arguments concerning establishing what is well-understood, routine, and conventional as per the Berkheimer memo, such establishment is only required in instances in which there is claimed extra-solution activity, which there are no elements of extra-solution activity currently claimed. As currently employed in the rejection above, analysis with regard to MPEP 2016.05(d) is to highlight the well-understood, routine, and conventional nature of the additional elements, though again, this is unnecessary/redundant for the purposes of rejection. The Examiner provided this analysis to be thorough and to assist in understanding the level of technological claiming and its corresponding specification support. To be clear: The combination of the claimed additional elements is no more than mere instructions to apply the exception using generic computer components. This is immediately evidenced by the claiming of the “machine-learning” being merely “us[ed]” to perform functions of the invention, such as codifying, or is otherwise “configured to learn” behaviors, and as such amounts to no more than mere instruction to apply the exception to the data using generic computer elements per MPEP 2106.05(f). No fact finding per Berkheimer is require for this analysis as it is evidenced by the claims in view of the specification itself. Now, per 2016.05(d) and Berkheimer, as argued, facts are required to demonstrate that the claims are well-understood, routine, and conventional, and Berkheimer provides an order of preference for this analysis that begins with a citation to the specification. Per Berkheimer, a “specification demonstrates the well-understood, routine, conventional nature of additional elements when it describes the additional elements as well-understood or routine or conventional (or an equivalent term), as a commercially available product, or in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a).” Again, consider the “machine-learning” additional element and where specification paragraph 45 describes “The RIK 508 may use machine learning 506 and statistical approaches to provide regulatory intelligence to inform decision making and forecast the risk of, for example, trying to collect genomic samples in a region of Germany”. Here it is shown that the machine learning is employed generically to produce the desired output (to provide regulatory intelligence to inform decision making and forecast the risk). Further, it is shown that the machine learning itself is interchangeable with “statistical approaches” without otherwise altering the invention or affecting the core concept of the invention, and the particulars of the machine learning in question need not be described to satisfy the 35 U.S.C. 112(a) requirements. The claims and specification do not describe how the machine learning itself operates to processes the desired input to produce the desired output – it is essentially a black box. Hence, the machine learning is either to be understood to be well-understood, routine, and conventional, or it should be understood to lack written description support under 35 U.S.C. 112(a). The present case represents the former, and the presently claimed additional elements are understood to be well-understood, routine, and conventional.
In the remarks, Applicant argues in substance that (2) the 35 U.S.C. 112 rejections should be withdrawn in view of the amendments.
The rejections are withdrawn.
In the remarks, Applicant argues in substance that (3) the 35 U.S.C. 103 rejections should be withdrawn in view of the amendments because “Califano also does not teach or suggest a consent profile that is linked to a specimen. Califano does not discuss a consent profile. Consent in Califano, by Califano' s own terms, is linked only to stored data. The "systems and methods described herein have a link between study participants, their data and, in some cases, their identity." Califano, paragraph [0044], emphasis added. The "VPI can act as a link between a person's identity data and their medical data." Califano, paragraph [0053], emphasis added. Califano does not discuss how specimens are treated.”
Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument – see application of prior art Malbon.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT A SOREY whose telephone number is (571)270-3606. The examiner can normally be reached Monday through Friday, 8am to 5pm.
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/ROBERT A SOREY/Primary Examiner, Art Unit 3682