DETAILED ACTION
Remarks
This office action is issued in response to communication filed on 2/22/2024. Claims 1-5 are pending in this Office 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 Objections
Claim 1 objected to because of the following informalities: claim 1 recites limitations that include the term “will” and “to be” which indicate a future tense these limitations are :
“predicting whether the new drug candidate substance will have a pharmacological class corresponding to each of the pharmacological effect prediction models based on an output value obtained by inputting information on the new drug candidate substance into each of the prepared pharmacological effect prediction models”
“wherein: the structural similarity type is further classified according to which calculation method between a Dice similarity calculation method and a Tanimoto similarity calculation method is to be applied, whether a Bemis-Murcko scaffold is applied, and whether a hydrogen atom bond is applied”
Appropriate correction is required.
Claim Interpretation
Claim 1 recites contingent limitations and therefore the prior art only needs to show one condition to meet the claim requirement (this interpretation is only applied to method claim. See MPEP 2111.04 (II)). These limitations are:
“wherein: the structural similarity type is further classified according to which calculation method between a Dice similarity calculation method and a Tanimoto similarity calculation method is to be applied, whether a Bemis-Murcko scaffold is applied, and whether a hydrogen atom bond is applied”;
“the predicting step predicts:
if a first output value obtained by inputting the feature vector into a first pharmacological effect prediction model corresponding to a first pharmacological class is greater than or equal to a reference value, that the new drug candidate substance has the first pharmacological class;
if the first output value is less than the reference value, that the new drug candidate substance does not have the first pharmacological class;
if a second output value obtained by inputting the feature vector into a second pharmacological effect prediction model corresponding to a second pharmacological class is greater than or equal to the reference value, that the new drug candidate substance has the second pharmacological class; and
if the second output value is less than the reference value, that the new drug candidate substance does not have the second pharmacological class”
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.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 :
Step 1: Statutory Category ?: Yes. claim 1 recites a method (i.e., a “process”) which is statutory category.
Step 2A-Prong 1: Judicial Exception Recited ?: Yes.
The 1 recites one or more limitations that can be performed in the human mind using observation, evaluation, judgment and opinion:
“ selecting a structural similarity type, which is a reference for determining the similarity between substances;
preparing pharmacological effect prediction models corresponding to the selected structural similarity type from among a plurality of pharmacological effect prediction models created by structural similarity type and pharmacological class; and
predicting whether the new drug candidate substance will have a pharmacological class corresponding to each of the pharmacological effect prediction models based on an output value obtained by inputting information on the new drug candidate substance into each of the prepared pharmacological effect prediction models” ;
“wherein:
the structural similarity type is further classified according to which calculation method between a Dice similarity calculation method and a Tanimoto similarity calculation method is to be applied, whether a Bemis-Murcko scaffold is applied, and whether a hydrogen atom bond is applied”
“the predicting step predicts:
if a first output value obtained by inputting the feature vector into a first pharmacological effect prediction model corresponding to a first pharmacological class is greater than or equal to a reference value, that the new drug candidate substance has the first pharmacological class;
if the first output value is less than the reference value, that the new drug candidate substance does not have the first pharmacological class;
if a second output value obtained by inputting the feature vector into a second pharmacological effect prediction model corresponding to a second pharmacological class is greater than or equal to the reference value, that the new drug candidate substance has the second pharmacological class; and
if the second output value is less than the reference value, that the new drug candidate substance does not have the second pharmacological class”
Step 2A-Prong 2: Integrated into a practical application? No.
Claim 1 recites additional elements of “receiving information on a new drug candidate substance” which is simply data gathering step and therefore is insignificant extra-solution activities. (See MPEP 2106.05(g)).
The additional limitations: “each of the plurality of pharmacological effect prediction models created by structural similarity type and pharmacological class is created based on machine learning using substances already known whether to have a specific pharmacological class”
in each of the pharmacologic effect prediction models, a binary vector obtained by using a threshold value for binarization is input as a feature vector of the new drug candidate substance to a similarity calculated according to the selected structural similarity type between the new drug candidate substance and the already known substance”
are recited at the very high level of generality such that it amounts no more than mere instructions to implement an abstract idea on a computer or merely uses a computer a tool to perform the abstract idea. See MPEP 2105.05(f)
Step 2B: Recites additional elements that amount to significantly more than the judicial exception? No.
Claim 1 does not include additional elements that are sufficient to amount to significantly more than judicial exception. As indicates above, the additional element of receiving step is data gathering which is well-understood, routine conventional activities previously known to the industry and therefore do not amount to significantly more than the judicial exception. (See MPEP 2106.05(d)) and 2106.07(a)III). The use of machine learning and prediction models are at best equivalent of merely adding the word “apply it “ to the judicial exception. Even when considered in combination, the additional elements do not provide an inventive concept, claim 1 therefore is ineligible.
Claims 2-5 are patent eligible.
Allowable Subject Matter
Claims 2-5 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Although these claims are allowable over prior art, all other rejections and/or objections (if any) such as 101/112/claim objection must be overcome before the claims are allowed.
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 1 is rejected under 35 U.S.C. 103 as being unpatentable over Donner et al.(US Patent Application Publication 2019/0114390 A1, hereinafter “Donner”) and further in view of Turco et al.(US Patent Application Publication 2019/0073570 A1, hereinafter “Turco”)
As to claim 1, Donner teaches a method for predicting pharmacological effects of a new drug candidate substance performed by a computing device, the method comprising:
receiving information on a new drug candidate substance;(Donner par [0030] teaches once trained , the model 220 receives an expression profile for a query perturbagen)
selecting a structural similarity type, which is a reference for determining the similarity between substances;( Donner par [0031] teaches the structure-function relationship 240c may be characterized based on a combination of the similarity scores determined from the embeddings and an existing metric for structural similarity between the two perturbagens, for example Tanimoto coefficients for extended connectivity fingerprints.)
preparing pharmacological effect prediction models corresponding to the selected structural similarity type from among a plurality of pharmacological effect prediction models created by structural similarity type and pharmacological class (the model 220 receives an expression profile for a query perturbagen); and
predicting whether the new drug candidate substance will have a pharmacological class corresponding to each of the pharmacological effect prediction models based on an output value obtained by inputting information on the new drug candidate substance into each of the prepared pharmacological effect prediction models,(Donner par [0031] teaches the functional properties of candidate perturbagens may be propagated and assigned to the query perturbagen)
wherein:
the structural similarity type is further classified according to which calculation method between a Dice similarity calculation method and a Tanimoto similarity calculation method is to be applied, whether a Bemis-Murcko scaffold is applied, and whether a hydrogen atom bond is applied; (Donner par [0031] teaches Tanimoto coefficients)
each of the plurality of pharmacological effect prediction models created by structural similarity type and pharmacological class is created based on machine learning using substances already known whether to have a specific pharmacological class;( Donner par [0030] teaches trained model 220 )
in each of the pharmacologic effect prediction models, a binary vector obtained by using a threshold value for binarization is input as a feature vector of the new drug candidate substance to a similarity calculated according to the selected structural similarity type between the new drug candidate substance and the already known substance; ( Donner par [0036] teaches to extract an embedding, the model 220 implements a deep neural network to generate the embeddings, which may be output in the form of a feature vector. The feature vector of an embedding comprises an array of feature values, each representing a coordinate in a multidimensional vector space, and all together specify a point in said multidimensional vector space corresponding to the expression profile of the query perturbagen)
the predicting step predicts:
if a first output value obtained by inputting the feature vector into a first pharmacological effect prediction model corresponding to a first pharmacological class is greater than or equal to a reference value, that the new drug candidate substance has the first pharmacological class; (Donner par wherein higher similarity scores correspond to greater functional similarity, the similarity scoring module 330 accesses or receives a threshold similarity score stored within computer memory and selects known perturbagens corresponding to similarity scores above the threshold similarity score as candidate perturbagens. In yet another embodiment, wherein lower similarity scores correspond to greater functional similarity, the similarity scoring module 330 accesses or receives a threshold similarity score stored within computer memory and selects known perturbagens corresponding to similarity scores above the threshold similarity score as candidate perturbagens. Donner par [0041] teaches The functional property module 350 generates 460 an aggregate set of functional properties describing the pharmacological effects of each candidate perturbagen and assigns at least one property of the set to the query perturbagen)
if the first output value is less than the reference value, that the new drug candidate substance does not have the first pharmacological class;
if a second output value obtained by inputting the feature vector into a second pharmacological effect prediction model corresponding to a second pharmacological class is greater than or equal to the reference value, that the new drug candidate substance has the second pharmacological class; and if the second output value is less than the reference value, that the new drug candidate substance does not have the second pharmacological class.
Donner only teaches one prediction model and therefore fails to expressly teaches preparing pharmacological effect prediction models.
However, Turco teaches preparing pharmacological effect prediction models. (Turco par [0059] teaches the client may be enabled to select different models in different requests and to perform the same analytical task flexibly based on many different models)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaching of Donner and Lee to achieve the claimed invention. One would have been motivated to make such combination to perform the analytical task flexibly (Turco par [0059])
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HIEN DUONG whose telephone number is (571)270-7335. The examiner can normally be reached Monday-Friday 8:00AM-5:00PM.
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/HIEN L DUONG/Primary Examiner, Art Unit 2147
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