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 .
DETAILED ACTION
Response to Amendment
In the amendment dated 02/02/2026, the following occurred: Claims 1 and 20-22 were amended.
Claims 1-22 are currently pending.
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 1-22 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, 21 and 22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claim recites a method, system and one or more non-transitory computer-readable media (CRM) for generating content, which are within a statutory category.
Step 2A1
Regarding claims 1, 20, 21 and 22, the limitation of (claim 1 being representative) accessing […] first data representing a plurality of first clinical trial protocols; accessing […] second data representing a plurality of content generation tools available for use with the one or more […] models; receiving […] a user input instructing […] to generate a second clinical trial protocol using the one or more […] models, wherein the user input comprises an indication of a subject of the second clinical trial protocol; determining […] a plurality of actions to generate the second clinical trial protocol; determining […] one or more content generation tools associated with each of the actions; causing the one or more […] models to perform each of the actions using the one or more content generation tools associated with that action and based on the first data, wherein performance of each of the actions causes the one or more […] models to generate an output using […]; generating […] the second clinical trial protocol based on the output of the one or more […] models; and storing […] a data structure representing the second clinical trial protocol as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for the recitation of generic computer components. That is other than reciting (claim 1 and 22) a method implemented by a computer system (claim 20) a system comprising at least one processing device and a memory and a computer system, or (claim 21) one or more CRM’s, at least one processor and a computer system, the claimed invention amounts to managing personal behavior or interaction between people. For example, but for the recited computer components, the claims encompasses accessing first and second data, receiving a user input, determining actions and associated content generation tools, perform actions to generate an output, generate a second clinical trial protocol and store a data structure representing the second clinical trial protocol in the manner described in the identified abstract idea(s), supra. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People (e.g. social activities, teaching, following rules or instructions)” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Note that the broadest reasonable interpretation of “one or more computerized large language models (LLMs)” and “generative transformer model” and in light of the disclosure, represent the creation of mathematical interrelationships between data. See Spec. Para. [0043] describing LLMs to include models having or more generative pre-trained transformers (GPTs), such as those implemented using or more artificial neural networks and Para. [00135]-[00164] that describe the generative transform as a mathematical concept. Thus given the broadest reasonable interpretation, the Examiner interprets the one or more computerized large language models (LLMs) and generative transformer model to be implemented using existing, known mathematical techniques. As such, the one or more computerized large language models (LLMs) and the generative transformer model are interpreted to be part of the identified abstract idea, supra. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, claims 1 and 22 recite the additional elements of a computer system. Claim 20 recites the additional elements of at least one processing device, memory and a computer system. Claim 21 recites the additional elements of one or more CRM’s, at least one processor and a computer system. These additional elements are not exclusively defined by the applicant and are recited at a high-level of generality (i.e., a generic computers or components thereof) such that they amounts to no more than mere instructions to apply the exception using a generic computer component. As set forth in MPEP 2106.04(d) “merely including instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Claims 1, 20, 21 and 22 further recite the additional elements of one or more hardware storage devices (1), one or more computerized large language models (LLMs) (2), and a generative transformer model having at least one of an encoder or a decoder (3).
Regarding (1), the additional element of one or more hardware storage devices represents a location to which data is accesses and stored. Each of these accessing and storing steps are recited at a high level of generality (i.e. a general means to accessing/storing data) and amount to extra solution activity. MPEP 2106.04(d)(1) indicates that extra solution data gathering activity cannot provide a practical application.
Regarding (2) and (3), the additional elements of one or more computerized large language models (LLMs) and a generative transformer model having at least one of an encoder or a decoder represent a mathematical concept as described in the Specification at Para. [0043] and [00135]-[00164]. This mathematical concept is applied to (“apply it’) the abstract idea. MPEP 2106.04(d)(I) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide a practical application.
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application.
Step 2B
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 into a practical application, the additional elements of a computer system, at least one processing device, memory, one or more CRM’s and at least one processor to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). Moreover, using generic computer components to perform abstract ideas does not provide a necessary inventive concept. See Alice, 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention”). Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
Also as discussed with respect to integration of the abstract idea into a practical application, the additional element of accessing and storing data from (1) was considered extra-solution activity. This has been re-evaluated under “significantly more” analysis and determined to be well-understood, routine and conventional activity in the field. Well-understood, routine and conventional activity cannot provide an inventive concept (“significantly more”). As such the claim is not patent eligible.
As discussed with respect to integration of the abstract idea into a practical application, the additional element of one or more computerized large language models (LLMs) and a generative transformer model having at least one of an encoder or a decoder were determined to be the application of mathematical concept to the identified abstract idea. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP2106.05(1)(A) indicates that merely saying “apply it’ or equivalent to the abstract idea cannot provide an inventive concept (“significantly more’). As such the claim is not patent eligible.
The examiner notes that: A well-known, general-purpose computer has been determined by the courts to be a well-understood, routine and conventional element (see, e.g., Alice Corp. v. CLS Bank; see also MPEP 2106.05(d)); Receiving and/or transmitting data over a network (“a communications network”) has also been recognized by the courts as a well - understood, routine and conventional function (see, e.g., buySAFE v. Google; MPEP 2016(d)(II)); and Performing repetitive calculations is/are also well-understood, routine and conventional computer functions when they are claimed in a merely generic manner (see, e.g., Parker v. Flook; MPEP 2016.05(d)).
Claims 2-12 and 14-20 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim(s) 2 further merely describe(s) presenting a graphical user interface. Claim(s) 3 further merely describe(s) continuously updating contents of the graphical user interface. Claim(s) 4 further merely describe(s) the graphical user interface comprises a plurality of first graphical display elements. Claim(s) 5 further merely describe(s) the graphical user interface comprises a plurality of second graphical display elements. Claim(s) 2, 3, 4, 5 includes the additional element of “a graphical user interface” which is interpreted as a form of extra-solution activity and does not provide practical application or significantly more. Claim(s) 6 further merely describe(s) the subject of the second clinical trial protocol. Claim(s) 7 further merely describe(s) the first data. Claim(s) 8 and 9 further merely describe(s) what accessing the first data comprises. Claim(s) 10 further merely describe(s) Claim(s) 11-15 further merely describe(s) the content generation tools. Claim(s) 11 includes the additional element of “one or more external databases” which is interpreted as a form of extra-solution activity and does not provide practical application or significantly more. Claim(s) 16 further merely describe(s) the at least one of the encoder or the decoder. Claim(s) 17 further merely describe(s) receiving a second user input, determining a plurality of second actions to generate the one or more clinical trial documents, determining one or more second content generation tool, perform each of the second actions to generate a second output, generating the one or more clinical trial documents based on the second output and storing a second data structure. Claim(s) 18 and 19 further merely describe(s) the one or more clinical trial documents.
Subject Matter Free of Prior Art
The cited prior art of record fails to expressly teach or suggest, either alone or in combination, the features found within claim 2. In particular, the cited prior art of record fails to expressly teach or suggest the combination of: each of the following conditions, using the corresponding method: accessing, by a computer system from one or more hardware storage devices, first data representing a plurality of first clinical trial protocols; accessing, by the computer system from the one or more hardware storage devices, second data representing a plurality of content generation tools available for use with the one or more LLMs, wherein the one or more computerized LLMs comprise a generative transformer model having at least one of an encoder or a decoder; receiving, by the computer system, a user input instructing the computer system to generate a second clinical trial protocol using the one or more LLMs, wherein the user input comprises an indication of a subject of the second clinical trial protocol; determining, by the computer system, a plurality of actions to generate the second clinical trial protocol; determining, by the computer system, one or more content generation tools associated with each of the actions; causing, by the computer system, the one or more LLMs to perform each of the actions using the one or more content generation tools associated with that action and based on the first data, wherein performance of each of the actions causes the one or more LLMs to generate an output using at least one of the encoder or the decoder; generating, by the computer system, the second clinical trial protocol based on the output of the one or more LLMs; and storing, by the computer system using the one or more hardware storage devices, a data structure representing the second clinical trial protocol.
Response to Arguments
Claim Objections
Regarding the objection of claim(s) 20 and 21, the Applicant has amended the claims to overcome the basis/bases of objection.
Drawing Objections
Regarding the drawing objection(s), the Applicant has amended Figures 3, 5 and 6A-6D to overcome the basis/bases of objection.
Rejection under 35 U.S.C. § 112(b)
Regarding the indefinite rejection of claims 1, 20 and 21, the Applicant has amended the claims to overcome the bases of rejection.
Rejection under 35 U.S.C. § 101
Regarding the rejection of claims 1-22, the Examiner has considered the Applicant’s arguments, but does not find them persuasive. Applicant argues:
In particular, Applicant's claim 1 is also not directed to a judicial exception, because claim 1 as a whole integrates what would otherwise be a judicial exception instead into a practical application at Step 2A, Prong Two (Step 2A: NO). In Ex Parte Desjardins, the Appeals Review Panel ("ARP") held that claims directed to a machine learning model are patent-eligible if they integrate an abstract idea into a practical application that improves how the model or computer system operates, such as by system complexity. Such is precisely the case here... Similarly, in this case, the claimed, trained machine learning model improves system performance by enabling generation of "data in a more efficient and/or effective manner."… As described above, the Desjardins' specification described a machine learning model that improved system performance. Similarly, in this case, Applicant's specification describes a machine learning that results in improved system performance. Applicant's specification states:… (Para. 006)… (Para. 007)… (Para. 009)…
Regarding 1, The Examiner respectfully disagrees. Applicants claims do not provide improvements to machine learning technology. The claims apply conventional machine learning techniques to generate clinical trial protocols and outputs. In light of Applicant’s disclosure, the of “one or more computerized large language models (LLMs)” and the “generative transformer model” represent the creation of mathematical interrelationships between data. See Spec. Para. [0043] describing LLMs to include models having or more generative pre-trained transformers (GPTs), such as those implemented using or more artificial neural networks and Para. [00135]-[00164] that describe the generative transform as a mathematical concept. Thus given the broadest reasonable interpretation, the Examiner interprets the one or more computerized large language models (LLMs) and generative transformer model to be implemented using existing, known mathematical techniques. As such, the one or more computerized large language models (LLMs) and the generative transformer model are interpreted to be part of the identified abstract idea, supra. Moreover, the one or more computerized large language models (LLMs) and the generative transformer model are analyzed as additional elements and represent a mathematical concept as described above. This mathematical concept is applied to (“apply it’) the abstract idea. MPEP 2106.04(d)(I) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide a practical application. MPEP2106.05(1)(A) indicates that merely saying “apply it’ or equivalent to the abstract idea cannot provide an inventive concept (“significantly more’).
Furthermore, in regard to the improvements the Applicant argues, Specification at para. [006] states generating clinical trial documents without user manual input, retrieving information and presenting a summary. Generating data is an abstract idea and generating more efficient and/or effective data is an improvement to the abstract idea. The technology of machine learning is not being improved upon, yet utilized to generate data more efficiently and/or effectively. This does not qualify for technical improvements. The Specification at para. [007] states “using a generative AI system having one or more LLMs, which is trained to identify statistical relationships from text documents during a computationally intensive self-supervised and semi-supervised training process. Based on the identified statistical relationships, the generative Al system can use the LLMs to generate content that is similar to that that would be produced by a human, without requiring that the computer system have the semantic or syntactic knowledge that a human might possess”. Again generating data is an abstract idea and applying trained machine learning to do so does not render improvements in machine learning technology. Human involvement is not relevant in this case as the claims apply machine learning technology to generate data. Lastly, the Specification at para. [009] states improvements in design and performance of clinical trials, allowing researchers to better assess the effectiveness of medical interventions, improving the safety and/or efficacy of the treatment of patients, automatically generate robust and comprehensive clinical trials protocols to accurately assess the safety and/or efficacy of a particular medial intervention, improve medical intervention safety and/or efficacy, none of these are technical improvement but are improvement in the field of clinical trials and healthcare improvements. As such the claim is ineligible.
Conclusion
Applicant’s amendment necessitated the new grounds of rejection presented in this Office action. 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.
The prior art made of record though not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Gray (US 2023/0298707) teaches systems and methods for clinical trial results endpoint-based analysis and dynamic aggregation. Tran (US 2020/0117690) teaches smart device. Moore (US 2008/0162229) teaches system and method for processing of clinical trial data for multiple clinical trials through associated trial ids.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LIZA TONY KANAAN whose telephone number is (571)272-4664. The examiner can normally be reached on Mon-Thu 9:00am-6:00pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Morgan can be reached on 571-272-6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/L.T.K./Examiner, Art Unit 3683 /ROBERT W MORGAN/Supervisory Patent Examiner, Art Unit 3683