Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Information Disclosure Statement
The IDS filed 5/08/2026 have been considered by the Examiner.
Priority
Acknowledgment is made of applicant's claim for foreign priority under 35 U.S.C. 119(a)-(d) to CN202310403340.8 filed 4/14/2023.
Should applicant desire to obtain the benefit of foreign priority under 35 U.S.C. 119(a)-(d) prior to declaration of an interference, a certified English translation of the foreign application must be submitted in reply to this action. 37 CFR 41.154(b) and 41.202(e).
Failure to provide a certified translation may result in no benefit being accorded for the non-English application.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1: Process, Machine, Manufacture or Composition
Claims 1-9 are drawn to a method, so a process.
Claims 10-18 are drawn to a device, so a machine.
Claims 19-29 are drawn to non-transitory computer readable medium, so a manufacture.
Step 2A Prong One: Identification of an Abstract Idea
The claim(s) recite(s)
1. acquiring a feature representation of state information of a ligand molecule, wherein the state information comprises at least position information and directional information of the ligand molecule, as in claim 1.
This step reads on a mental process of analyzing information about positional orientation of a ligand molecule and determining coordinates of position and orientation of atoms or any structure of the ligand. The step is therefore an abstract idea.
2. determining, by using a trained reinforcement learning model, additional state information and a feedback value of the ligand molecule based on the feature representation of the state information and a feature representation of state information of a receptor molecule corresponding to the ligand molecule, as in claim 1.
This step reads on the abstract idea of determining state information and a value for a ligand molecule from on the feature representation of the state information and a feature representation of state information of a receptor molecule. The step can be performed with math or the human mind by evaluating positional information or energy values related to an equation of state such as a Hamiltonian or binding potential. The recitation of using a trained reinforcement learning model is equivalent to the words just “apply it” as described in MPEP 2106.05(f) and in the USPTO Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, Example 47, claim 2.
3. outputting the additional state information responsive to determining that the feedback value reaches a predetermined threshold, as in claim 1.
This step reads on comparing a feedback value to a threshold which can be achieved by the human mind and is therefore an abstract idea.
4. Training a feature extraction model using training data and self-supervised models, as in claim 3.
This step reads on mathematics because self-supervised models are mathematics performed on a computer. The step is therefore an abstract idea.
5. wherein the reinforcement learning model is a Q learning model and updating the Q learning model based on the Q value, updating the Q learning model based on the Q value, wherein the Q value is determined based on a learning rate, feedback function value and a Q value of best action, as in claim 4.
This limitation reads on math and is therefore and abstract idea. Q-learning is mathematics, i.e. reinforcement learning based on the Markov Decision Process which uses probability and calculus to find the best solution.
6. wherein the state information comprises twist angle information of the ligand molecule, as in claim 5.
This step further characterized the abstract wherein the state information is a mathematical model of the ligand molecule. The recited twist angle encompasses rotational angels that mathematically describe molecular motion. The limitation is drawn to a mathematical concept and is therefore an abstract idea.
7. operation comprises moving a root atom by a predetermined distance, rotating by a predetermined angle, twisting by a predetermined twist angle, as in claim 6.
This limitation is drawn to math. The movement of a molecule is simulated using mathematical expressions describing motion and angular rotation. The step is therefore an abstract idea.
8. determining a compressed version of the state information of the ligand molecule using an encoder, determine a decompressed version of the compressed version by using an decoder, and acquiring a feature representation from the decompressed version, as in claim 7.
This step reads on a mathematical process of compressing and decompressing information on a computer using mathematic. The encoder and decoder read on mathematical processes. Converting one form of information to another corresponds to concepts identified as abstract ideas by the Courts, such as in Gottschalk v. Benson, 409 U.S. 63, 175 U.S.P.Q. 673 (1972) and Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014). Like the processes claimed in Gottschalk v. Benson, the claimed process “can be carried out in existing computers long in use, no new machinery being necessary.” 409 U.S. at 67. Digitech Image Technologies., LLC v. Electronics for Imaging, Inc., states that “[w]ithout additional limitations, a process that employs mathematical algorithms to manipulate existing information to generate additional information is not patent eligible.” Furthermore, the court in Bancorp Servs., 687 F.3d at 1278, stated ,“As we have explained, ‘the fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter.’” The step is therefore drawn to an abstract idea.
9. determining additional compressed version using an encoder, determine a decompressed version of the additional compressed version using a decoder, as in claim 8.
This step is further drawn to math and is therefore an abstract idea.
10. docking the ligand molecule to the receptor molecule based on the additional sate information, as in claim 9.
This step reads on mathematical calculations describing interactions between modeled atoms and surfaces of a molecule. The recited docking is a mathematical calculation describing energies of interactions of the geometries of the atoms. The step is therefore an abstract idea.
Claims 10-20 recite substantially the same process steps as claims 1-9 and are therefore also drawn to the abstract idea.
Step 2A Prong Two: Consideration of Practical Application
The claims result in a step of outputting state information which is an extra solution activity as described in MPEP 2106.05(g). The claims do not recite any additional elements that integrate the judicial exception into a practical application.
This judicial exception is not integrated into a practical application because the claims do not meet any of the following criteria:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than
a drafting effort designed to monopolize the exception.
Step 2B: Consideration of Additional Elements and Significantly More
The claimed method also recites "additional elements" that are not limitations drawn to an abstract idea. The recited additional elements are drawn to:
1. outputting the additional state information, as in claims 1, 10 and 19.
2. inputting the state information to a feature extraction model, as in claims 2, 11 and 20.
4. inputting state information and action information to a Q learning model, as in claims 4 and 13.
5. sending compressed and additional compressed version of the state information to the server side, as in claims 7-8 and 16-17.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because inputting, outputting and sending information to and from computers is deemed to be extra solution activity as described in MPEP 2106.05(g).
Other elements of the method include the processor, memory (claim 10) and non-transitory computer readable medium (claim 19) which is a recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea recited in the instantly presented claims into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims under 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of 35 U.S.C. 103(c) and potential 35 U.S.C. 102(e), (f) or (g) prior art under 35 U.S.C. 103(a).
Claims 1-20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Wang et al. (US 2024/0386993) in view of Sun et al.
Wang et al. teach (Abstract) obtaining a first feature representation based on a structure of a ligand (i.e. feature representation of state information of a ligand molecule) and a second feature representation based on a structure of a protein (i.e. feature representation of state information of a receptor molecule), as in claims 1, 10 and 19.
Wang et al. teach (Abstract) determining a third feature representation of a complex structure based on the ligand molecule and protein molecule to generate aggregate feature representation to determine evaluation (i.e. feedback value) on the binding of the ligand to the protein (i.e. determining additional state information and feedback value of the ligand molecule based on feature representation of state information of the ligand and receptor molecule), as in claims 1, 10 and 19.
Wang et al. teach that the feature representation may include a graph of nodes and edges representing an atom and bond to another atom (par. 0032) of the feature representation may include bond distance information (par. 0034)(i.e. position), chirality information (par. 0033) and whether the bond is in a ring and connection type (par. 0034)(i.e. directional information), as in claims 1, 10, and 19.
Wang et al. teach (Abstract) that the evaluation information may indicate the effectiveness of binding or indicate affinity of a binding pose; wherein the binding analysis screens the binding pose as effective if the affinity score is larger than a threshold (par. 0070)(i.e. outputting additional state information responsive to determining that the feedback value reaches a predetermined value), as in claims 1, 10, and 19.
Wang et al. do not teach using a trained reinforcement learning model to determine the additional state information, as in claims 1, 10 and 19.
Sun et al. teach reinforcement learning with a Q-learning algorithm to predict drug-target interactions (Abstract and page 4 section “Q-Learning Algorithm). Sun et al. teach Q-learning to optimize drug-drug and target-target similarity matrices to obtain new drug-drug and target-target similarity matrices, as in claims 1, 10 and 19.
Sun et al. teach (page 2, col. 1, par. 2) that similarity is used to determine features of a compound including how a compound will bind or behave based on the features of already known compounds
It would have been obvious to one of ordinary skill in the art at the time the invention was made to have combined the teachings of Wang et al. for evaluating binding of ligands and target proteins using feature representations of the ligand, target, and complex with the teaching of Sun et al. that use Q-learning as reinforcement learning to determine drug target interactions by building similarity matrices. Sun et al. provide motivation (page 3, col. 1, par. 1) by teaching that assigning weights by Q-learning improves the amount of heterogenous data that can be used. One of skill in the art would have had a reasonable expectation of success at combining Wang et al. and Sun et al. because both are directed to evaluating ligand-target binding interactions by using features of the ligand and target with machine learning techniques.
Regarding dependent claims 2-9, 11-18 and 20
Wang et al. teach (Abstract) that the feature representation is determined based on a structure of the ligand (i.e. extract feature representation from state information), as in claim 2.
Wang et al. teach an attention module that generates feature representation based on inputs corresponding to the ligand molecule (par. 0048) wherein the feature representation is a graph model (par. 0058)(i.e. suitable for reinforcement learning) wherein it is know that attention heads are primarily associated with self-supervised learning, as in claims 3.
Sun et al. teach the Q algorithm where the agent receives the state of the environment and choses to perform the corresponding action (page 4, col. 2, par. 1)(i.e. inputting state information and action information in a training data, and determining a Q value, and updating the formula for Q value (page 4, col. 2, par. 2-3)(i.e. so as to determine a corresponding Q value, and updating the Q learning model based on the Q value); Sun et al. teach the Q value is based on the learning rate (page 4, col. 2, par. 3) and discount rate, feedback (page 4, col. 2, par. 2) and action (page 4, col. 2, par. 1)(i.e. Q value is based on learning rate, feedback function value and Q value of best action), as in claim 4.
Wang et al. teach evaluation information as including the binding pose (i.e. twist)(par. 0003 and 0016) wherein it is known to one of ordinary skill that the ligand’s pose refers to the 3D orientation, spatial arrangement and internal shape of the molecule when it binds to a target, as in claim 5.
Regarding claim 6, Wang et al. teach comparing at least two different binding poses (par. 0074) to determine the better binder. Wang et al. do not specifically teach moving a root atom, rotating and twisting the ligand, as in claim 6. However since pose is associated with position and orientation of a molecule when binding, it would be obvious to one of ordinary skill to move, rotate and twist the ligand to achieve the better binding pose, as taught in Wang et al.
Sun et al. teach (page 4, col. 2, par. 1) that in a Q-learning algorithm, for each time step, the agent receives the state of the environment and chooses to perform the corresponding action, and then in the next time step, the agent obtains a reward and a new state based on the feedback of the environment. In other words, reinforcement learning refers to continuously.
Wang et al. teach encoding and decoding modules for encoding/decoding functions (par. 0022) wherein it is well known in the art to compress state functions for transmission of data that is more easily transmitted because it is in a compressed form, and then later decompressed by a decoder, as in claims 7-8.
Regarding claim 9, Sun et al. teach (page 2, col. 1, par 2) target based docking simulation which used docking techniques to predict interactions. It would be obvious to one of ordinary skill to combing determining potential ligand-target interactions (Figure 2) as taught by Sun et al. and perform a docking simulation as taught by Sun et al.
Regarding claims 10-20, the claims are drawn to a device comprising a processor, memory and computer readable medium for carrying out the same process as that of claims 1-9. Wang et al. teach that their methods can be implemented on a computer (par. 0020). Sun et al. also suggest that their methods are computational (page 2, col. 1, par. 1-2).
E-mail communication Authorization
Per updated USPTO Internet usage policies, Applicant and/or applicant’s representative is encouraged to authorize the USPTO examiner to discuss any subject matter concerning the above application via Internet e-mail communications. See MPEP 502.03. To approve such communications, Applicant must provide written authorization for e-mail communication by submitting the following statement via EFS Web (using PTO/SB/439) or Central Fax (571-273-8300):
Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.
Written authorizations submitted to the Examiner via e-mail are NOT proper. Written authorizations must be submitted via EFS-Web (using PTO/SB/439) or Central Fax (571-273-8300). A paper copy of e-mail correspondence will be placed in the patent application when appropriate. E-mails from the USPTO are for the sole use of the intended recipient, and may contain information subject to the confidentiality requirement set forth in 35 USC § 122. See also MPEP 502.03.
Conclusion
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/Anna Skibinsky/
Primary Examiner, AU 1635
Prosecution Insights