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
The action is in reply to the application filed 2024 December 23.
Claims 1-22 are currently pending and have been examined.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 119(e) as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 63/366,875 fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. For claims 1 and 13 the prior-filed application does not provide support for “a server”, “a data acquisition module”, and “a data processing engine configured to transform and normalize data from the data acquisition module.” Examiner cannot find disclosure of a server, a data acquisition module, and a data processing engine configured to transform and normalize data from the data acquisition module in the prior filed application. For claim 2 the prior-filed application does not provide support for “the model being trained by the machine learning engine being a multi-tiers model.” Examiner cannot find disclosure of the model being trained by the machine learning engine being a multi-tiers model. For claim 3 the prior-filed application does not provide support for “the multi-tiers model comprising first-tier model configured to calculate a plurality of intermediate predictions of success.” Examiner cannot find disclosure of the multi-tiers model comprising first-tier model configured to calculate a plurality of intermediate predictions of success in the prior filed application. For claim 4 the prior-filed application does not provide support for “the multi-tiers model comprising a plurality of first tier models, each model being configured to calculate an intermediate prediction of success.” Examiner cannot find disclosure of the multi-tiers model comprising a plurality of first tier models, each model being configured to calculate an intermediate prediction of success in the prior filed application. For claim 5 the prior-filed application does not provide support for “the plurality of first tier models comprising at least one of the following models: a model to calculate prediction of target recruitment of the clinical trial; a model to calculate a prediction of protocol deviation of the clinical trial; and a model to calculate other factors relating to the clinical trials.” Examiner cannot find disclosure of the plurality of first tier models comprising at least one of the following models: a model to calculate prediction of target recruitment of the clinical trial; a model to calculate a prediction of protocol deviation of the clinical trial; and a model to calculate other factors relating to the clinical trials in the prior filed application. For claim 6 the prior-filed application does not provide support for “the intermediate prediction of success of each of the first-tier models being inputted in second-tier model to calculate the prediction of the success of the clinical trial.” Examiner cannot find disclosure of the intermediate prediction of success of each of the first-tier models being inputted in second-tier model to calculate the prediction of the success of the clinical trial in the prior filed application. For claim 7 the prior-filed application does not provide support for “a module to interpret and explain the calculated prediction of success of the clinical trial.” Examiner cannot find disclosure of a module to interpret and explain the calculated prediction of success of the clinical trial in the prior filed application. For claim 8 the prior-filed application does not provide support for “the module to interpret and explain the calculated prediction of success of the clinical trial comprising generating logical rules used to calculate the prediction.” Examiner cannot find disclosure of the module to interpret and explain the calculated prediction of success of the clinical trial comprising generating logical rules used to calculate the prediction in the prior filed application. For claim 9 the prior-filed application does not provide support for “the module to interpret and explain the calculated prediction of success of the clinical trial comprising any of the followings: contribution attributes of the clinical trials; studies used to compare to the clinical trial; contrasting explanations; scenarios impacting level of predicted success of the clinical trial.” Examiner cannot find disclosure of the module to interpret and explain the calculated prediction of success of the clinical trial comprising any of the followings: contribution attributes of the clinical trials; studies used to compare to the clinical trial; contrasting explanations; scenarios impacting level of predicted success of the clinical trial in the prior filed application. For claim 14 the prior-filed application does not provide support for “the trained model being a multi-tiers model comprising a plurality of first-tier models and a second-tier model, the method further comprising each of the first-tier model calculating an intermediate prediction of success of a specific aspect of the clinical study.” Examiner cannot find disclosure of the trained model being a multi-tiers model comprising a plurality of first-tier models and a second-tier model, the method further comprising each of the first-tier model calculating an intermediate prediction of success of a specific aspect of the clinical study in the prior filed application. For claim 15 the prior-filed application does not provide support for “each of the plurality of first tier models calculating one of the followings: a prediction of target recruitment of the clinical trial; a prediction of protocol deviation of the clinical trial; and other factors relating to the clinical trials.” Examiner cannot find disclosure of each of the plurality of first tier models calculating one of the followings: a prediction of target recruitment of the clinical trial; a prediction of protocol deviation of the clinical trial; and other factors relating to the clinical trials in the prior filed application. For claim 16 the prior-filed application does not provide support for “the second-tier model using each of the intermediate predications calculated by the first-tier models to calculate the prediction of success of the clinical trial.” Examiner cannot find disclosure of the second-tier model using each of the intermediate predications calculated by the first-tier models to calculate the prediction of success of the clinical trial in the prior filed application. For claim 17 the prior-filed application does not provide support for “using such characteristics to calculate the prediction of success of the clinical trial.” Examiner cannot find disclosure of using such characteristics to calculate the prediction of success of the clinical trial in the prior filed application. For claim 22 the prior-filed application does not provide support for “A computer-readable medium storing instructions.” Examiner cannot find disclosure of a computer-readable medium storing instructions in the prior filed application. Accordingly, claims 1-9, 13-16, 22 are not entitled to the benefit of the prior application.
Claim Objection
Claim 12 is objected to due to a minor informality: unclear what MPP is an acronym for as the specification also does not elaborate. Appropriate correction is required.
Claim Interpretation
The following is a quotation of the first paragraph of 35 U.S.C. 112(f):
The following is a quotation of 35 U.S.C. 112(f):
(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.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
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.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claim 7 recites the following:
“a module … interpret…explain”
Claim 8 recites the following:
“module … interpret…explain”
Claim 9 recites the following:
“module … interpret…explain”
Claim 10 recites the following:
“application module … execute”
which are limitations that invoke 35 U.S.C. § 112(f) or 35 U.S.C. § 112 (pre-AIA ),
sixth paragraph. The limitations create a rebuttable presumption that the claim elements are to be treated under § 112(f) based on the use of the word “means” or generic place holder (underlined) with functional language (in italics). The presumption is not rebutted because the limitations do not recite sufficient structure in the claim to perform the functions. When § 112(f) is invoked the broadest reasonable interpretation of the limitations is restricted to the structure in the disclosure and its equivalents.
The following functional claim limitations:
Of claim 7 recites the following:
“a module … interpret…explain”
Of claim 8 recites the following:
“a module … interpret…explain”
Of claim 9 recites the following:
“a module … interpret…explain”
Of claim 10 recites the following:
“application module … execute”
recite specialized computer functions. A function performed by a programmed
computer requires both the computer and the algorithm that causes the computer to
perform the function. As such, a disclosure of an algorithm to perform these functions and to transform a general purpose computer into a programmed computer is required. Examiner notes that the specification is not clear in providing the specific algorithm and corresponding structure for the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph limitation of the function of the sorting mechanism.
If applicant wishes to provide further explanation or dispute the examiner’s
interpretation of the corresponding structure/algorithm, applicant must identify the
corresponding structure/algorithm with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim limitation(s) treated under 35
U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may amend the
claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination
Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of
Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention 2.
Claims 7-10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim elements “a module” and “an application module” are limitations that invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
Programmed computer functions require a computer programmed with an “algorithm” to perform the function. Because “a module” and “an application module” relate to specific functions that must be performed by a special purpose computer, the supporting specification must specifically identify the structure (including an algorithm for specialized functions) that performs the claimed functions of the above-mentioned claim elements.
Therefore, the claims are indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claims so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the
invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 7-10 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. The claims contain the recitation of “a module” and “an application module.” However, applicant’s specification describes no particular manner in how exactly the module performs the interpretation and explanation for the calculated prediction of success of the clinical trial, and how application module performs the execution of the machine learning engine. MPEP 2161.01 notes, “When examining computer-implemented functional claims, examiners should determine whether the specification discloses the computer and the algorithm (e.g., the necessary steps and/or flowcharts) that perform the claimed function in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed subject matter at the time of filing.” Accordingly, a rejection for lack of written description is necessary.
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.
The claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of the patent eligible subject matter because the broadest interpretation of the computer program product residing on a computer readable medium of claim 22 encompasses signals per se.
Claims 1-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
The claim(s) recite(s) subject matter within a statutory category as a machine (claims 1-12) and a process (claims 13-21).
INDEPENDENT CLAIMS
Step 2A Prong 1
Claim 1 recites steps of
a data source comprising data relating to the clinical trial;
a server comprising:
a data acquisition module in data communication with a plurality of external data sources comprising data relating to clinical trials, regulatory approvals, economic and reimbursement information, pharmacological information, and commercial and corporate information;
a data processing engine configured to transform and normalize data from the data acquisition module using a natural language processor;
a machine learning engine comprising a model trained with the data processed by the data processing engine, the trained machine learning engine being configured to execute an algorithm to analyse the data of the data source relating to the clinical trial and to calculate a prediction of success of the clinical trial based on the said analyzed data.
Claim 13 recites steps of
acquiring data from a plurality of external data sources comprising data relating to clinical trials, regulatory approvals, economic and reimbursement information, pharmacological information, and commercial and corporate information;
processing, transforming and normalizing the acquired data using a natural language processor,
executing a machine learning model trained with the processed, transformed and normalized data to analyse data relating to the clinical trial and to calculate a prediction of success of the clinical trial based on the said analyzed data.
These steps for predicting level of success of a clinical trial, as drafted, under the broadest reasonable interpretation, includes methods of organizing human activity. That is, nothing in the claim element precludes the italicized portions from managing personal behavior or relationships or interactions between people through organizing the activity around analyzing data to predict human behavior or future events (i.e., clinical success). This could be analogized to considering historical usage information while inputting data. The italicized portions containing the recitation of training the machine learning model and executing the machine learning model has been treated as part of the abstract idea, specifically as mathematical calculations which falls within the abstract idea of mathematical concepts, in light of the 2024 USPTO AI Guidance. If a claim limitation, under its broadest reasonable interpretation, covers performance as organizing human activity and mathematical calculations but for the recitation of generic computer components, then it falls within the “Methods of Organizing Human Activity” and “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application. In particular, the additional elements non-italicized portions identified above for claims 1 and 13, does not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception (such as
recitation of a server; a data acquisition module; a data processing engine; from the data acquisition module using a natural language processor; a machine learning engine; and, by the data processing engine, the trained machine learning engine being configured amounts to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea (such as recitation of acquiring data from a plurality of external data sources amounts to mere data gathering since it does not add meaningful limitations to the acquiring action performed, see MPEP 2106.05(g))
Each of the above additional elements therefore only amounts to mere instructions to implement functions within the abstract idea using generic computer components or other machines within their ordinary capacity, and add insignificant extra-solution activity to the abstract idea. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. These elements are therefore not sufficient to integrate the abstract idea into a practical application. Therefore, the above claims, as a whole, are directed to an abstract idea.
Step 2B
The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, and add insignificant extra-solution activity to the abstract idea. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to mere instructions to apply an exception in particular fields such as recitation of a server; a data acquisition module; a data processing engine; from the data acquisition module using a natural language processor; a machine learning engine; and, by the data processing engine, the trained machine learning engine being configured, e.g., a commonplace business method or mathematical algorithm being applied on a general-purpose computer, Alice Corp. v. CLS Bank, MPEP 2106.05(f).
amount to elements that have been recognized as well-understood,
routine, and conventional activity in particular fields such as recitation of
acquiring data from a plurality of external data sources; e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i);
Looking at the limitations 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. Their collective functions merely provide generic computer implementation.
DEPENDENT CLAIMS
Step 2A Prong 1
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2-12 and 14-22 reciting particular aspects for predicting level of success of a clinical trial such as
[Claim 2] the model being trained by the machine learning engine being a multi-tiers model;
[Claim 3] the multi-tiers model comprising first-tier model configured to calculate a plurality of intermediate predictions of success;
[Claim 4] the multi-tiers model comprising a plurality of first tier models, each model being configured to calculate an intermediate prediction of success;
[Claim 5] the plurality of first tier models comprising at least one of the following models:
a model to calculate prediction of target recruitment of the clinical trial;
a model to calculate a prediction of protocol deviation of the clinical trial; and
a model to calculate other factors relating to the clinical trials;
[Claim 6] the intermediate prediction of success of each of the first-tier models being inputted in second-tier model to calculate the prediction of the success of the clinical trial;
[Claim 7] a module to interpret and explain the calculated prediction of success of the clinical trial;
[Claim 8] the module to interpret and explain the calculated prediction of success of the clinical trial comprising generating logical rules used to calculate the prediction;
[Claim 9] the module to interpret and explain the calculated prediction of success of the clinical trial comprising any of the followings:
contribution attributes of the clinical trials;
studies used to compare to the clinical trial;
contrasting explanations;
scenarios impacting level of predicted success of the clinical trial;
[Claim 10] an application module configured to execute the machine learning engine with data relating to the clinical trial;
[Claim 11] the acquired data source being classified in plurality of repositories;
[Claim 12] the repositories comprising any one of the following type of data:
clinical data, regulatory data, economic data, molecule data and MPP;
[Claim 14] the trained model being a multi-tiers model comprising a plurality of first-tier models and a second-tier model, the method further comprising each of the first-tier model calculating an intermediate prediction of success of a specific aspect of the clinical study;
[Claim 15] each of the plurality of first tier models calculating one of the followings:
a prediction of target recruitment of the clinical trial;
a prediction of protocol deviation of the clinical trial; and
other factors relating to the clinical trials;
[Claim 16] the second-tier model using each of the intermediate predications calculated by the first-tier models to calculate the prediction of success of the clinical trial;
[Claim 17] monitoring in real-time progress characteristics of the clinical study using such characteristics to calculate the prediction of success of the clinical trial;
[Claim 18] the characteristics comprising anticipation of recruitment needs and identification of impacting events;
[Claim 19] the execution of the machine learning model further calculating any one of the followings: clinical risk of the clinical trial, regulatory risk of the clinical trial, commercial risk of the clinical trial and pharmacological risk of the clinical trial;
[Claim 20] the execution of the machine learning model further generating prescriptive data for optimizing study conduct;
[Claim 21] developing a plurality of machine learning model for the clinical trial, training the developed models with acquired data and selecting one or more of the developed models based on performance metrics;
[Claim 22] A computer-readable medium storing instructions for executing the method of claim 13;
these italicized portions are methods of organizing human activity since they merely describe types of data and determinations that can be performed by humans. The italicized portions containing the recitation of various models, calculating predictions, and training of the multi-tiers model have been treated as part of the abstract idea, specifically as mathematical calculations which falls within the abstract idea of mathematical concepts, in light of the 2024 USPTO AI Guidance
Step 2A Prong 2
Dependent claims 2, 7-10, and 22 recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (the additional limitations in claim 2 (by the machine learning engine); claims 7, 8, & 9 (module to); claim 10 (an application module configured to execute the machine learning engine with data relating to the clinical trial); and, claim 22 (A computer-readable medium storing instructions) amounts to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f))). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B
Dependent claims 2, 7-10, and 22 recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea, e.g., a commonplace business method or mathematical algorithm being applied on a general-purpose computer, Alice Corp. v. CLS Bank, MPEP 2106.05(f). There is no indication that these additional elements improve the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation.
Therefore, in consideration of all the facts, the present invention is not a patent-eligible invention under USC 101. Additionally, it is evident that the present claims monopolize the judicial exception since it covers any computer-implemented system that uses machine learning to analyze clinical trial data for success prediction, restricting further innovation in this area without offering a specific, technical improvement to how the computer actually operates. Improved predictive accuracy or using advanced AI tools is generally not enough to transform an abstract idea into patent-eligible subject matter if the core of the invention is still a method of calculation; “monopolization of those tools through the grant of a patent might tend to impede innovation more than it would tend to promote it.” Alice Corp., 573 U.S. at 216, 110 USPQ2d at 1980 (quoting Myriad, 569 U.S. at 589, 106 USPQ2d at 1978 and Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012)).
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148
USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-16 and 19-22 are rejected under 35 U.S.C. 103 as being unpatentable over Liebman (US20130197966A1) in view of Fu et al. (Hint: Hierarchical interaction network for clinical-trial-outcome predictions).
Regarding claim 1, Liebman discloses a data source comprising data relating to the clinical trial ([0054] “The system prioritizes available knowledge and pulls relevant data from one or more databases, or other disparate repositories.”)
a server ([0056] “on a central server”)
a data processing engine configured to transform and normalize data from the data acquisition module using a natural language processor ([0048] “In one embodiment, the system includes an ontology based risk analytics engine” [0074] “perform the initial knowledge parsing. This activity will evolve to include an initial assessment using natural language processing, within the system platform, to support preliminary analysis” [0054] “The system […] uses data extraction, transformation” [0062] “All formats may be in standard readable formats, such as XML.”)
Liebman does not explicitly disclose however Fu teaches a machine learning engine comprising a model trained with the data processed by the data processing engine, the trained machine learning engine being configured to execute an algorithm to analyse the data of the data source relating to the clinical trial and to calculate a prediction of success of the clinical trial based on the said analyzed data ([pg. 2] “we utilize the standard medical codes of the diseases and their natural language descriptions […] To provide accurate trial outcome predictions for all trials, we propose the Hierarchical Interaction Network (HINT). The HINT model is trained on a multi-modal dataset, including molecule information of the drugs, the target disease information, the trial eligibility criteria, and biomedical knowledge.” [pg. 3] “The goal of HINT is to learn a deep neural network model f for predicting the actual trial success status y)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Liebman a machine learning engine comprising a model trained with the data processed by the data processing engine, the trained machine learning engine being configured to execute an algorithm to analyse the data of the data source relating to the clinical trial and to calculate a prediction of success of the clinical trial based on the said analyzed data as taught by Fu since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 2, Liebman does not explicitly disclose however Fu teaches the model being trained by the machine learning engine being a multi-tiers model ([pg. 2] “To provide accurate trial outcome predictions for all trials, we propose the Hierarchical Interaction Network (HINT).”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Liebman the model being trained by the machine learning engine being a multi-tiers model as taught by Fu since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 3, Liebman does not explicitly disclose however Fu teaches the multi-tiers model comprising first-tier model configured to calculate a plurality of intermediate predictions of success ([pg. 17] “Utilizing that information, we pretrain on prediction models for Absorption […] absorption score yA and embedding hA”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Liebman the multi-tiers model comprising first-tier model configured to calculate a plurality of intermediate predictions of success as taught by Fu since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 4, Liebman does not explicitly disclose however Fu teaches the multi-tiers model comprising a plurality of first tier models, each model being configured to calculate an intermediate prediction of success ([pg. 19] “Pretrain basic modules: (i) ADMET models (A,D,M,E,T ); (ii) disease risk (DR) model […] Given new data (M,D,C), predict outcome (i.e., success probability ˆy).”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Liebman the multi-tiers model comprising a plurality of first tier models, each model being configured to calculate an intermediate prediction of success as taught by Fu since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 5, Liebman does not explicitly disclose however Fu teaches the plurality of first tier models comprising at least one of the following models: a model to calculate prediction of target recruitment of the clinical trial;
a model to calculate a prediction of protocol deviation of the clinical trial; and
a model to calculate other factors relating to the clinical trials ([pg. 16] “In addition to drug properties, we also consider the knowledge distilled from historical trials of the target diseases. […] The predicted trial risk ˆyR and embedding hR ∈ Rd are generated”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Liebman the plurality of first tier models comprising at least one of the following models: a model to calculate prediction of target recruitment of the clinical trial; a model to calculate a prediction of protocol deviation of the clinical trial; and a model to calculate other factors relating to the clinical trials as taught by Fu since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 6, Liebman does not explicitly disclose however Fu teaches the intermediate prediction of success of each of the first-tier models being inputted in second-tier model to calculate the prediction of the success of the clinical trial ([pg. 2] “After that, we pre sent an interaction graph module to connect all of the embed dings to capture various interaction effects from different trial components. Finally, HINT learns a dynamic attentive graph neural network to predict trial outcomes.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Liebman the intermediate prediction of success of each of the first-tier models being inputted in second-tier model to calculate the prediction of the success of the clinical trial as taught by Fu since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 7, Liebman discloses a module to interpret and explain the calculated prediction of success of the clinical trial ([0059] “the system may operate as application software, which may be managed by a local or remote computing device” [0052] “the system of the present invention further provides statistical analysis and interpretation of trial results in support of drug approval applications and product development for enhanced analysis and interpretation of the trial results.”)
Regarding claim 8, Liebman discloses the module to interpret and explain the calculated prediction of success of the clinical trial comprising generating logical rules used to calculate the prediction ([0059] “the system may operate as application software, which may be managed by a local or remote computing device” [0069] “For example, as depicted in FIG. 23, a natural language question from each of the concepts “target,” “disease” and “treatment (drug)” has been selected and/or prioritized from a provided question set for each concept. This prioritization or selection may be made by a rules based algorithm”)
Regarding claim 9, Liebman discloses the module to interpret and explain the calculated prediction of success of the clinical trial comprising any of the followings:
contribution attributes of the clinical trials;
studies used to compare to the clinical trial;
contrasting explanations;
scenarios impacting level of predicted success of the clinical trial ([0059] “the system may operate as application software, which may be managed by a local or remote computing device” [0053] “In one aspect, the system can assess and optimize a clinical trial by taking into consideration failed trials, patent extension opportunities, and plan scope.”)
Regarding claim 10, Liebman discloses an application module configured to ([0060] “The system may provide software, for example, applications, such as for the assessment of risk, accessible to one or more users to perform one or more functions.”)
Liebman does not explicitly disclose however Fu teaches execute the machine learning engine with data relating to the clinical trial ([pg. 9] “HINT is a graph neural network model that integrates comprehensive data sources to predict trial outcomes.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Liebman execute the machine learning engine with data relating to the clinical trial as taught by Fu since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 11, Liebman discloses the acquired data source being classified in plurality of repositories ([0051] “a generalized linking of the system ontology to commercial, public and client-owned data sets on the data field level”)
Regarding claim 12, Liebman discloses the repositories comprising any one of the following type of data: clinical data, regulatory data, economic data, molecule data and MPP ([0070] “As illustrated in FIG. 25, relevant knowledge repositories, such as public and commercial databases as well as client proprietary data spanning the clinical, molecular and commercial domains”)
Regarding claim 13, Liebman discloses acquiring data from a plurality of external data sources comprising data relating to clinical trials, regulatory approvals, economic and reimbursement information, pharmacological information, and commercial and corporate information ([0054] “The system prioritizes available knowledge and pulls relevant data from one or more databases, or other disparate repositories.” [0065] “the system ontology may include […] cost prognosis […] pharmacokinetics […] developmental status, commercial partners and intellectual property […] clinical trials”)
processing, transforming and normalizing the acquired data using a natural language processor ([0074] “perform the initial knowledge parsing. This activity will evolve to include an initial assessment using natural language processing, within the system platform, to support preliminary analysis” [0054] “The system […] uses data extraction, transformation” [0062] “All formats may be in standard readable formats, such as XML.”)
Liebman does not explicitly disclose however Fu teaches executing a machine learning model trained with the processed, transformed and normalized data to analyse data relating to the clinical trial and to calculate a prediction of success of the clinical trial based on the said analyzed data ([pg. 2] “we utilize the standard medical codes of the diseases and their natural language descriptions […] To provide accurate trial outcome predictions for all trials, we propose the Hierarchical Interaction Network (HINT). The HINT model is trained on a multi-modal dataset, including molecule information of the drugs, the target disease information, the trial eligibility criteria, and biomedical knowledge.” [pg. 3] “The goal of HINT is to learn a deep neural network model f for predicting the actual trial success status y)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Liebman executing a machine learning model trained with the processed, transformed and normalized data to analyse data relating to the clinical trial and to calculate a prediction of success of the clinical trial based on the said analyzed data as taught by Fu since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 14, Liebman does not explicitly disclose however Fu teaches the trained model being a multi-tiers model comprising a plurality of first-tier models and a second-tier model ([pg. 2] “we train a knowledge-embedding module from external knowledge on pharmacokinetic properties for improving drug embedding. […] After that, we present an interaction graph module to connect all of the embed dings to capture various interaction effects from different trial components.” [pg. 15] “we leverage external knowledge on pharmacokinetic properties and trial risk to pretrain some embeddings named knowledge embedding module” [pg. 16] “Utilizing that information, we pretrain on prediction models for Absorption, Distribution, Metabolism, Excretion, Toxicity (ADMET) properties”)
the method further comprising each of the first-tier model calculating an intermediate prediction of success of a specific aspect of the clinical study ([pg. 19] “Pretrain basic modules: (i) ADMET models (A,D,M,E,T ); (ii) disease risk (DR) model […] Given new data (M,D,C), predict outcome (i.e., success probability ˆy).”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Liebman the trained model being a multi-tiers model comprising a plurality of first-tier models and a second-tier model, the method further comprising each of the first-tier model calculating an intermediate prediction of success of a specific aspect of the clinical study as taught by Fu since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 15, Liebman does not explicitly disclose however Fu teaches each of the plurality of first tier models calculating one of the followings:
a prediction of target recruitment of the clinical trial;
a prediction of protocol deviation of the clinical trial; and
other factors relating to the clinical trials ([pg. 16] “In addition to drug properties, we also consider the knowledge distilled from historical trials of the target diseases. […] The predicted trial risk ˆyR and embedding hR ∈ Rd are generated”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Liebman each of the plurality of first tier models calculating one of the followings: a prediction of target recruitment of the clinical trial; a prediction of protocol deviation of the clinical trial; and other factors relating to the clinical trials as taught by Fu since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 16, Liebman does not explicitly disclose however Fu teaches the second-tier model using each of the intermediate predications calculated by the first-tier models to calculate the prediction of success of the clinical trial ([pg. 2] “After that, we pre sent an interaction graph module to connect all of the embed dings to capture various interaction effects from different trial components. Finally, HINT learns a dynamic attentive graph neural network to predict trial outcomes.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Liebman the second-tier model using each of the intermediate predications calculated by the first-tier models to calculate the prediction of success of the clinical trial as taught by Fu since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 19, Liebman does not explicitly disclose however Fu teaches the execution of the machine learning model further calculating any one of the followings: clinical risk of the clinical trial, regulatory risk of the clinical trial, commercial risk of the clinical trial and pharmacological risk of the clinical trial ([pg. 2] “We formally define a model framework for a general clinical-trial-outcome prediction task, which not only models various trial risks, including drug safety, treatment efficiency.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Liebman the execution of the machine learning model further calculating any one of the followings: clinical risk of the clinical trial, regulatory risk of the clinical trial, commercial risk of the clinical trial and pharmacological risk of the clinical trial as taught by Fu since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 20, Liebman does not explicitly disclose however Fu teaches the execution of the machine learning model further generating prescriptive data for optimizing study conduct ([pg. 20] “We also illustrate how HINT provides insights into the drug development process. […] As shown in Figure 5, interaction graph G is constructed, and related trial components (nodes) are connected. […] The A,D,M,E,T and PK components have more weight (darker) than the interaction/augmented interaction components, indicating that the drug’s safety issue is a major concern. This is consistent with the clinical results (fail due to safety issues).”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Liebman the execution of the machine learning model further generating prescriptive data for optimizing study conduct as taught by Fu since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 21, Liebman does not explicitly disclose however Fu teaches developing a plurality of machine learning model for the clinical trial, training the developed models with acquired data and selecting one or more of the developed models based on performance metrics ([pg. 5] “We use the following metrics to measure the performance of all methods. Precision-recall area under the curve (PR-AUC) […] F1: […] Area under the receiver operating characteristic curve (ROC-AUC) [....] We compare the proposed method HINT with several baselines, including conventional machine learning models and deep learning methods. We enhance their feature sets for all classical machine learning baselines to be the same as HINT. […] Logistic regression (LR): LR was used for trial outcome predictions. Random Forest (RF): Similar to LR, RF was used for trial outcome predictions. XGBoost: An implementation of gradient-boosted decision trees designed for speed and performance. k Nearest Neighbor (kNN) + RF10 combines statistical imputation techniques for handling missing data and standard classification methods. In the experiment, we chose the best-performing model reported with kNN as the imputation technique and Random Forest as the classifier.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Liebman developing a plurality of machine learning model for the clinical trial, training the developed models with acquired data and selecting one or more of the developed models based on performance metrics as taught by Fu since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 22, Liebman discloses A computer-readable medium storing instructions for executing the method of claim 13 ([Claim 1] “a computer memory accessible by a software application”)
Claims 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Liebman (US20130197966A1) in view of Fu et al. (Hint: Hierarchical interaction network for clinical-trial-outcome predictions) and further in view of Wu et al. (Machine Learning Prediction of Clinical Trial Operational Efficiency).
Regarding claim 17, Liebman in view of Fu does not explicitly disclose however Wu teaches monitoring in real-time progress characteristics of the clinical study using such characteristics to calculate the prediction of success of the clinical trial ([pg. 57] “We perform a grid search over a set of hyperparameters (number of leaves, minimum data in each leaf, maximum depth, maximum bins, and learning rate) and monitor performance on the validation set before setting the model for each response variable.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Liebman and Fu monitoring in real-time progress characteristics of the clinical study using such characteristics to calculate the prediction of success of the clinical trial as taught by Wu since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 18, Liebman does not explicitly disclose however Fu teaches the characteristics comprising anticipation of recruitment needs and identification of impacting events ([pg. 2] “We formally define a model framework fora general clinical-trial-outcome prediction task, which not only models various trial risks, including […] trial recruitment, but also models a wide range of drugs and indications (e.g., diseases).”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Liebman the characteristics comprising anticipation of recruitment needs and identification of impacting events as taught by Fu since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Prior Art Cited but Not Relied Upon
Halimi, I., Piffo, E., Boudersa, O., Vilmorin, Y. M. C., Ait-ikhlef, M., Kone, K., ... & Dorcelus, G. (2026). A Latent Risk-Aware Machine Learning Approach for Predicting Operational Success in Clinical Trials based on TrialsBank. arXiv preprint arXiv:2603.29041.
This reference is relevant because it discloses the applicant’s invention.
Conclusion
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/WINSTON R FURTADO/Examiner, Art Unit 3687
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