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 § 112
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.
Claim 1-16 and 18-21 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 applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding Claim 1, there is no support in the description for “generating compound information in a computer apparatus,” as the specification teaches to predicting properties of a compound as the purpose of the method is to generate/design compounds. Due to it generality and broadness, the claim leads the examiner to doubt what the claimed invention is. Further, “obtaining information associated with a source molecule to be modified using the learning model,” lacks written description as it encompasses obtaining any property of the source molecule whereas it would appear from the description that the step is only about inputting the source molecule structure. Similar rationale is applied to the following limitation “obtaining information associated with a partial structure set associated with the source molecule, wherein the partial structure set includes a plurality of partial structures of the source molecule” as the specification lends to this step being only about decomposing the source molecule into fragments [00131-00136]. Lastly, the limitation of “obtaining, using the learning model, information associated with a modified partial structure corresponding to the target partial structure”: Considering the learning model as either the encoder and/or decoder, the output could be either mapping or the structure of partial structure. However, there is no support for obtaining any information from an undefined modified partial structure.
Regarding Claim 2, the step of “assigning an index to each binding site” lacks written description in the spec. The spec fails to disclose what the binding site is, what it does and its purpose.
Regarding Claim 3, the spec fails to disclose exactly what the structure of the tree is as the description only supports the tree disclosed with reference to figure 11.
Claim 5, the spec fails to disclose what the target partial structure is and how it is analyzed by the learning model. Further, the term “mapping value” lacks description. The spec discloses that they may be obtained by encoding, that they may be modified and that they may indicate a vector value of the target partial structure, but fail to disclose what the mapping value is and its significance.
Claims 6 through 21 are affected for similar reasons as disclosed above.
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.
Claims 1-16 and 18-21 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claim 1, It is unclear what the learning model is doing. The claim discloses: “selecting, from the plurality of partial structures included in the partial structure set, a target partial structure to be modified using the learning model;”
It is unclear whether the learning model is assisting in selection process of the target partial structure or being used to modify the target partial structure? The applicant can overcome this rejection by clearly indicating the purpose of the learning model.
Claim 2 recites the limitation "each binding site" in the second limitation. There is insufficient antecedent basis for this limitation in the claim. Applicant can overcome this rejection by amending the claim to disclose earlier recitation of the stated entity.
Regarding Claim 3, it is unclear what the structure of the tree is. The applicant can overcome this rejection by clearly annotating the structure of the tree.
Regarding Claim 5, it is not clear what the analysis entails and it does not appear that he analysis is completed by the learning model. The applicant can overcome this rejection by clearly disclosing what the analysis entails and clearly specify what is completed by the learning model.
Similar rationale is applied to Claim 12-16. Rejections can be overcome by the applicant by applying the same solutions as states in the claims above.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-16 and 18-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1 recites:
A method of generating compound information in a computing apparatus, the method comprising:
obtaining a learning model trained based on information associated with one or more partial structures of a plurality of molecules;
obtaining information associated with a source molecule to be modified using the learning model;
obtaining information associated with a partial structure set associated with the source molecule, wherein the partial structure set includes a plurality of partial structures of the source molecule; selecting, from the plurality of partial structures included in the partial structure set, a target partial structure to be modified using the learning model;
obtaining, using the learning model, information associated with a modified partial structurerresponding to the target partial structure; and
outputting result information associated with a modified version of the source molecule in which the target partial structure is replaced by the modified partial structure.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (process).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation, that covers mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) and mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
For example, steps of “obtaining a learning model trained based on information associated with one or more partial structures of a plurality of molecules;
obtaining information associated with a source molecule to be modified using the learning model;
obtaining information associated with a partial structure set associated with the source molecule, wherein the partial structure set includes a plurality of partial structures of the source molecule; selecting, from the plurality of partial structures included in the partial structure set, a target partial structure to be modified using the learning model;
obtaining, using the learning model, information associated with a modified partial structurerresponding to the target partial structure” are treated as belonging to mental process and/or mathematical concept grouping.
Similar limitations comprise the abstract ideas of Claim 12.
Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application.
In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
The above claims comprise the following additional elements:
In Claim 1: computing apparatus;
In Claim 12: computing apparatus; input device, storage device, output device; controller
The additional element in the preamble of “A computing apparatus for generating compound information” is not qualified for a meaningful limitation because it is only generally links the use of the judicial exception to a particular technological environment or field of use. Computing apparatus; input device, storage device, output device and controller are generally recited and are not qualified as particular machines.
Additionally, the limitation of “outputting result information associated with a modified version of the source molecule in which the target partial structure is replaced by the modified partial structure” is defined by MPEP 2106.05 as insignificant extra solution activity, mere data outputting.
In conclusion, the above additional elements, considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis).
The claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 2-11 and 13-21 provide additional features/steps which are part of an expanded algorithm, so these limitations should be considered part of an expanded abstract idea of the independent claims. \
Where there is a great deal of confusion and uncertainty as to the proper interpretation of the limitations of a claim, it would not be proper to reject such a claim on the basis of prior art. As stated in In re Steele, 305 F.2d 859, 134 USPQ 292 (CCPA 1962), a rejection under 35 U.S.C. 103 should not be based on considerable speculation about the meaning of terms employed in a claim or assumptions that must be made as to the scope of the claims. See MPEP § 2173.06.
Conclusion
The prior art made record and not relied upon is considered pertinent to applicant’s disclosure.
Kim et al. (METHOD AND APPARATUS FOR GENERATING CHEMICAL STRUCTURE USING NEURAL NETWORK, 2020-03-04) teaches re a method and apparatus for generating a chemical structure using a neural network device. According to the present invention, the method for generating a chemical structure using a neural network device comprises the steps of: obtaining a descriptor for a reference chemical structure; inputting the descriptor into a trained neural network to calculate a property value of a specific property for the reference chemical structure; determining an expression region expressing the specific property in the descriptor; and creating a new chemical structure by changing a partial structure in the reference chemical structure corresponding to the expression region. According to an embodiment of the present invention, it is possible to determine an expression region for expressing a specific property in an image or a descriptor for a reference chemical structure. In addition, a new chemical structure can be generated by changing a partial structure in a reference chemical structure corresponding to an expression region; Tagade et al. (METHOD AND SYSTEM FOR PERFORMING MOLECULAR DESIGN USING MACHINE LEARNING ALGORITHMS, 2019-10-24) teaches a method and system for designing molecules by using a machine learning algorithm. The method includes representing molecular structures included in a dataset by using a Simplified Molecular Input Line Entry System (SMILES), where the SMILES uses a series of characters, converting a SMILES representation of the molecular structures into a binary representation, pre-training a stack of Restricted Boltzmann Machines (RBMs) by using the binary representation of the molecular structures, constructing a Deep Boltzmann Machine (DBM) by using the stack of the RBMs, determining limited molecular property data for a subset of the molecule structures in the dataset, training the DBM with the limited molecular property data, combining the pre-trained stack of the RBMs and the trained DBM in a Bayesian inference framework, and generating a sample of molecules with target properties by using the Bayesian inference framework;
Kwon et al. (METHOD AND APPARATUS FOR GENERATING A CHEMICAL STRUCTURE USING A NEURAL NETWORK, 2019-07-18) teaches A method of generating a chemical structure performed by a neural network device includes receiving a target property value and a target structure characteristic value; selecting first generation descriptors; generating second generation descriptors; determining, using a first neural network of the neural network device, property values of the second generation descriptors; determining, using a second neural network of the neural network device, structure characteristic values of the second generation descriptors; selecting, from the second generation descriptors, candidate descriptors that satisfy the target property value and the target structure characteristic value; and generating, using the second neural network of the neural network device, chemical structures for the selected candidate descriptors;
Yoo et al. (METHOD AND APPARATUS FOR SEARCHING NEW MATERIAL, 2018-02-01) teaches a structure-generating method for generating a structure candidate of a new material including: by a structure-generating processor: performing machine learning on a machine learning model, wherein the machine learning model is configured to provide a result based on a descriptor of a material, a physical property of the material, and a structure of the material; and generating a structure candidate of the new material based on the result of the machine learning, wherein the new material has a target physical property, and wherein the descriptor of the material, the physical property of the material, and the structure of the material are stored in a database;
Oono et al. (GENERATIVE MACHINE LEARNING SYSTEMS FOR DRUG DESIGN, 2017-06-08) teaches the systems and methods described herein relate to generative models. The generative models may be trained using machine learning approaches, with training sets comprising chemical compounds and biological or chemical information that relate to the chemical compounds. Deep learning architectures may be used. In various embodiments, the generative models are used to generate chemical compounds that have desired characteristics, e.g. activity against a selected target. The generative models may be used to generate chemical compounds that satisfy multiple requirements; and
Yoo et al. (METHOD AND DEVICE FOR SEARCHING NEW MATERIAL, 2017-05-04) teaches a method for searching a new material includes: performing a learning on a material model, which is modeled based on a known material; determining a candidate material by inputting a targeted physical property to a result of the learning; and determining the new material from the candidate material.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL J SINGLETARY whose telephone number is (571)272-4593. The examiner can normally be reached Monday-Friday 8:00am-5:00pm.
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/MICHAEL J SINGLETARY/Examiner, Art Unit 2863