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 .
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.
Step 1
Claims 1, 3-5, 7-8, 14-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1, 3-5, 7-8, 14-15 are directed to a method, and, claims 16 are directed towards an apparatus; thus, each of the pending claims are directed to a statutory category of invention.
Step 2A Prong One
Claim 1, representative of the claimed invention, recites the steps of (a) inputting a plurality of pieces of clinical trial data to a predetermined LLM as training data and training the LLM, wherein process (a) comprises:(a-1) receiving the plurality of pieces of clinical trial data from a user device;(a-2) extracting a query information string including a clinical trial title, a disease name, and a clinical trial phase and a response information string including clinical trial patient recruitment criteria and clinical trial patient group design information from the received clinical trial data, and generating a prompt command by using a pre-stored query text, the query information string, and the response information string;(a-3) matching the prompt command with the query information string and the response information string to create a training dataset and adjusting parameters of the LLM by training the LLM with the training dataset; and(a-4) performing validation by setting clinical trial data for validation as a separate validation dataset among the plurality of pieces of clinical trial data set by a user, training the LLM with the separate validation dataset, and adjusting the parameters of the LLM; (b) receiving, from a user device, basic clinical trial information including the clinical trial title, a drug name, formulation, a target disease, and a phase of a clinical trial to be conducted; (c) combining a plurality of pieces of pre-stored query text with the basic clinical trial information to generate a plurality of pieces of final query texts wherein process (c) further comprises:(c-1) analyzing a meaning of a string constituting the basic clinical trial information from the user device and distinguishing strings corresponding to the clinical trial title, the drug name, the formulation, the target disease, and the clinical trial phase, respectively, from the string constituting the basic clinical trial information, wherein process (c-1) further comprises: identifying a category of essential information constituting the basic clinical trial information from string input from the user device when it is determined that the essential information is missing and requesting input of the essential information from the user device, and wherein process (c-1) further comprises: analyzing the meaning of the string constituting the basic clinical trial information even when the user device inputs a plurality of strings without spaces, distinguishing the strings corresponding to the clinical trial title, the drug name, the formulation, the target disease, and the clinical trial phase, respectively, from the string constituting the basic clinical trial information, and providing the user device with the strings with line spacing to distinguish categories of the clinical trial title, the drug name, the formulation, the target disease, and the clinical trial phase, respectively, to query again whether it has been intended by the user; and (d) inputting the plurality of pieces of final query text to the LLM to generate a clinical trial design draft report in which a plurality of response information strings is output for a plurality of sections, respectively, wherein process (d) further comprises: reconstructing final query text when a number of response strings received is smaller than a predetermined threshold value after the final query text is input, and replacing a word in the final query text with another word or generating a prompt command in which a string constituting the basic clinical trial information is combined with query text into one sentence before inputting it to the LLM, and wherein in process (d),response information strings are sequentially generated for the plurality of sections, respectively, and when any one of the response information strings is generated, the any one of the response information strings as well as the query text and basic clinical trial information corresponding to the any one of the response information strings are stored, and then, a response information string for a next section is generated. The limitations above, as drafted, recite a process that, under its broadest reasonable interpretation, encompass mental processes and also certain methods of organizing human activity. The claimed steps recite several steps that include observations, evaluations, judgments and opinions, and “can be performed in the human mind, or by a human using a pen and paper” which have been considered by the courts to be a mental process. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). The courts do not distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016) (holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer"). The claimed steps also are directed towards managing personal behavior (e.g instructing a user to draft a clinical design report).
Apart from the use of generic technology (discussed further below), each of the limitations recited above describes activities that would encompass actions performed in collecting information regarding user visit history over time and providing activity recommendations.
Based on the broadest reasonable interpretation in light of the specification, these activities describe concepts relating to managing personal behavior and mental processes in that the activities relate to collecting and performing analysis of data to providing a clinical design report. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, commercial interactions, or fundamental economic practices, then it falls within the “Method of Organizing Human Activity” grouping of abstract ideas. The recited steps also are considered to be a mental process as methods that can be performed mentally, or which are the equivalent of human mental work. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application. In particular, claim 1 recites the additional elements of a server and a machine learning model. Claim 2 recites a user device. Claim 16 recites the additional elements of a server, memory, processor, and a machine learning model. The processor, user device, and server are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of receiving information, performing calculations, and providing/transmitting information) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, 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. Likewise, the machine learning model is implemented as a tool to perform an abstract idea. The claim is directed to an abstract idea.
This judicial exception is not integrated into a practical application because the generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. The claim does 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 using a processor to perform the steps of “(a) inputting a plurality of pieces of clinical trial data to a predetermined LLM as training data and training the LLM; (b) receiving, from a user device, basic clinical trial information including a clinical trial title, a drug name, formulation, a target disease, and a phase of a clinical trial to be conducted; (c) combining a plurality of pieces of pre-stored query text with the basic clinical trial information to generate a plurality of pieces of final query text; and (d) inputting the plurality of pieces of final query text to the LLM to generate a clinical trial design draft report in which a plurality of response information strings is output for a plurality of sections, respectively.” 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.
Similarly, use of a computer performing a machine learning model is a tool to perform the abstract idea. See MPEP 2106.05(f): “[u]se of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” An example where the courts have found the additional elements to be mere instruction to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process includes a commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 223 (MPEP 2106.05(f)(2)). The use of a machine learning model emulates what the user does in designing a clinical trial. Thus, even considering the additional elements in combination, the claims do not include elements that are significantly more than the judicial exception.
Step 2B
Limitations that the courts have found to qualify as “significantly more” when recited in a claim with a judicial exception include:
i. Improvements to the functioning of a computer, e.g., a modification of conventional Internet hyperlink protocol to dynamically produce a dual-source hybrid webpage, as discussed in DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258-59, 113 USPQ2d 1097, 1106-07 (Fed. Cir. 2014) (see MPEP § 2106.05(a));
ii. Improvements to any other technology or technical field, e.g., a modification of conventional rubber-molding processes to utilize a thermocouple inside the mold to constantly monitor the temperature and thus reduce under- and over-curing problems common in the art, as discussed in Diamond v. Diehr, 450 U.S. 175, 191-92, 209 USPQ 1, 10 (1981) (see MPEP § 2106.05(a));
iii. Applying the judicial exception with, or by use of, a particular machine, e.g., a Fourdrinier machine (which is understood in the art to have a specific structure comprising a headbox, a paper-making wire, and a series of rolls) that is arranged in a particular way to optimize the speed of the machine while maintaining quality of the formed paper web, as discussed in Eibel Process Co. v. Minn. & Ont. Paper Co., 261 U.S. 45, 64-65 (1923) (see MPEP § 2106.05(b));
iv. Effecting a transformation or reduction of a particular article to a different state or thing, e.g., a process that transforms raw, uncured synthetic rubber into precision-molded synthetic rubber products, as discussed in Diehr, 450 U.S. at 184, 209 USPQ at 21 (see MPEP § 2106.05(c));
v. Adding a specific limitation other than what is well-understood, routine, conventional activity in the field, or adding unconventional steps that confine the claim to a particular useful application, e.g., a non-conventional and non-generic arrangement of various computer components for filtering Internet content, as discussed in BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1350-51, 119 USPQ2d 1236, 1243 (Fed. Cir. 2016) (see MPEP § 2106.05(d)); or
vi. Other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment, e.g., an immunization step that integrates an abstract idea of data comparison into a specific process of immunizing that lowers the risk that immunized patients will later develop chronic immune-mediated diseases, as discussed in Classen Immunotherapies Inc. v. Biogen IDEC, 659 F.3d 1057, 1066-68, 100 USPQ2d 1492, 1499-1502 (Fed. Cir. 2011) (see MPEP § 2106.05(e)).
Claims 1 and 16 are not similar to any of these limitations.
Limitations that the courts have found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include:
i. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or
iv. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook, 437 U.S. 584, 588-90, 198 USPQ 193, 197-98 (1978) (MPEP § 2106.05(h)).
Claims 1 and 16 recite additional elements that are regarded as “apply it” as seen in the Step 2A Prong 2 discussion above. The claims do not set forth a solution to a problem rooted in technology (e.g., technical solution), as collecting and analyzing data to draft a clinical trial design report predate the use of computers or machine learning models.
Looking at the limitations of claims 1 and 16 as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology, effects a transformation of subject matter to a different state or thing, applies the use of a particular machine, integrate the abstract idea into a practical application or provide any meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
Therefore, claims 1 and 16 are not patent eligible.
The dependent claims further describe the abstract idea and do not recite a practical application or significantly more than the judicial exception. None of dependent claims 2-15 recite any further additional elements. Dependent claims 3-5, 7-8, 14-15 further narrow the scope of the abstract idea in claims 1 by providing additional information or considerations used in the analysis.
Thus, claims 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1, 3-5, 7-8, 14-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication 2025/0087315 to Bonageri et al.
As to claims 1 and 16, Bonageri discloses a method for automatically generating a draft of a clinical trial design based on a large language model (LLM), which is performed by a server, comprising:
inputting a plurality of pieces of clinical trial data to a predetermined LLM as training data and training the LLM (Bonageri [0058]) see context documents 63/582,390 at [0009]);
wherein process (a) comprises:
(a-1) receiving the plurality of pieces of clinical trial data from the user device (Bonageri [0062] 63/582,390 at [0010]);
(a-2) extracting a query information string including information string including information from the received clinical trial data, and generating a prompt command by using the pre-stored query text, the query information string, and the response information string (Bonageri [0047] 63/582,390 at [0009]) and drawing page 3); and
(a-3) matching the prompt command with the query information string and the response information string to create a training dataset and adjusting parameters of the LLM by training the LLM with the training dataset (Bonageri [0047] and 63/582,390 at [0009]-[0010] and drawing page 3)
(a-4) performing validation by setting clinical trial data for validation as a separate validation dataset among the plurality of pieces of clinical trial data set by a user, training the LLM with the validation dataset, and adjusting the parameters of the LLM (Bonageri [0018]-[0019] 63/582,390 at [0009]-[0010] and drawing page 3, 5, 6).
(b) receiving, from a user device, basic clinical trial information including a clinical trial title, a drug name, formulation (Bonageri [0046] [0019] 63/582,390 at [0009]-[0010] and drawing page 7);
combining a plurality of pieces of pre-stored query text with the basic clinical trial information to generate a plurality of pieces of final query text (Bonageri [0045]-[0048]; 63/582,390 at [0009]-[0010] and drawing page 7);
wherein process (c) further comprises:
(c-1) analyzing a meaning of a string constituting the basic clinical trial information input from the user device and distinguishing strings corresponding to the sections, respectively, from the string constituting the basic clinical trial information (Bonageri [0038]-[0039]; 63/582,390 at [0009] and drawing page 7);
Wherein process (c-1) further comprises:
identifying a category of missing essential information constituting the basic clinical trial information from the string input from the user device when it is determined that the essential information is missed and requesting input of the essential information from the user device (Bonageri [0081]; 63/582,390 at [0009]-[0010] and drawing page 3, 5, 6, 7);
(d) inputting the plurality of pieces of final query text to the LLM to generate a clinical trial design draft report in which a plurality of response information strings is output for a plurality of sections, respectively (Bonageri [0045]-[0048]; 63/582,390 at [0009]-[0010] and drawing page 3, 5, 6, 7).
wherein the process (d) further comprises:
reconstructing final query text when the number of response strings received is smaller than a predetermined threshold value after the final query text is input, and replacing a word in the final query text with another word or generating a prompt command in which a string constituting the basic clinical trial information is combined with query text into one sentence before inputting it to the LLM (Bonageri [0054]-[0056] ; 63/582,390 at [0009]-[0010] and drawing page 3, 5, 6, 7);
wherein in the process (d), response information strings are sequentially generated for the plurality of sections, respectively, and when any one of the response information strings is generated, the response information string as well as query text and basic clinical trial information corresponding to the response information string are stored, and then, a response information string for a next section is generated (Bonageri [0104] ; 63/582,390 at [0009]-[0010] and drawing page 3, 5, 6, 7).
However, Bonageri does not teach particular prompts. It would have been obvious to one of ordinary skill in the art at the at the time of the effective filing of application by Applicant to input any prompts into a LLM as in Bonageri as a matter of simple substitution of one known element (prompt) for another to obtain predictable result (that the machine learning algorithm would generate a result based on that prompt).
As to claim 3, see the discussion of claim 2, additionally, Bonageri discloses the method wherein when the query text and the extracted query information string are input to the LLM, the LLM is trained to output the response information string for each of the sections (Bonageri [0045]-[0048] ; 63/582,390 at [0009]-[0010] and drawing page 3, 5, 6, 7).
However, Bonageri does not teach the particular section data. It would have been obvious to one of ordinary skill in the art at the at the time of the effective filing of application by Applicant to populate any desired data type by the LLM as in Bonageri as a matter of simple substitution of one known element (prompt) for another to obtain predictable result (that the machine learning algorithm would generate a result based on that prompt).
As to claim 4, see the discussion of claim 3, additionally, Bonageri discloses the method wherein information in each of the sections is extracted from the LLM based on most frequently occurring keywords in the clinical trial report by training the LLM with the plurality of pieces of clinical trial data (Bonageri [0039] ; 63/582,390 at [0009]-[0010] and drawing page 3, 5, 6, 7).
As to claim 5, see the discussion of claim 2, additionally, Bonageri discloses the method wherein the plurality of pieces of clinical trial data includes report files (Bonageri [0019] ; 63/582,390 at [0009]-[0010] and drawing page 3, 5, 6, 7)
As to claim 7, see the discussion of claim 1, additionally, Bonageri discloses the method
wherein the final query text includes query text and a basic clinical trial information string, and the query text is located before the basic clinical trial information string (Bonageri [0104] ; 63/582,390 at [0009]-[0010] and drawing page 3, 5, 6, 7).
However, Bonageri does not teach particular prompts. It would have been obvious to one of ordinary skill in the art at the at the time of the effective filing of application by Applicant to input any prompts into a LLM as in Bonageri as a matter of simple substitution of one known element (prompt) for another to obtain predictable result (that the machine learning algorithm would generate a result based on that prompt.
As to claim 8, see the discussion of claim 7, additionally, Bonageri discloses the method wherein the query text consists of a query or command to make a draft for a specific purpose by using the basic clinical trial information string, which sequentially lists information input by a user (Bonageri [0047]; 63/582,390 at [0009]-[0010] and drawing page 3, 5, 6, 7).
As to claim 14, see the discussion of claim 1, additionally, Bonageri discloses the method further comprising:
(e) providing the clinical trial design draft report in a program format that allows for editing and saving on an application (Bonageri [0105]; 63/582,390 at [0009]-[0010] and drawing page 3, 5, 6, 7).
As to claim 15, see the discussion of claim 13, additionally, Bonageri discloses the method wherein in the process (e), keywords in the clinical trial design draft report are automatically generated in bold or highlighted (Bonageri [0102]). However, Bonageri does not teach the particular keyword types.
It would have been obvious to one of ordinary skill in the art at the at the time of the effective filing of application by Applicant to input any prompts into a LLM as in Bonageri as a matter of simple substitution of one known element (prompt) for another to obtain predictable result (that the machine learning algorithm would generate a result based on that prompt).
Response to Arguments
Applicant's arguments filed 1/13/2026 have been fully considered but they are not persuasive. With respect to the 101 rejection applicant argues that the judicial exception is integrated into a practical application as the claim demonstrates an improvement to the conventional method of generating a clinical trial design report. Generating a clinical trial design report is not a technology or technical field, it is part of the abstract idea. The specification in the cited paragraphs merely recognize the benefits of applying machine learning to automate the manual process, it does not improve a technology. Applicant argues that the invention significantly more than the technical field of generating clinical trial design report. This is not a technical field.
With respect to the 103 rejection applicant asserts that 63/582390 is different from the non-provisional counterpart and fails to teach the features of claim 1. Examiner maintains the rejection as 63/582390 also teaches the elements relied upon (as cited above) and predates the priority date of this application.
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 Eliza Lam whose telephone number is (571)270-7052. The examiner can normally be reached Monday-Friday 8-4:30PST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Choi can be reached at 469-295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ELIZA A LAM/Primary Examiner, Art Unit 3681
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