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 the Claims
Claims 1-19 are pending and examined on the merits.
Claims 1-19 are rejected.
Priority
This application claims priority to U.S. Provisional Application No. 63/285,647 filed on December 3, 2021, and U.S. Provisional Application No. 63/285,783, filed on December 3, 2021. The effective file date is December 3, 2021.
Information Disclosure Statement
The information disclosure statement (IDS) filed on Aug 06, 2024 is acknowledged.
Claim Rejections - 35 USC § 112
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.
Claims 1, 8, 10 and 19 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.
The metes and bounds of claims 1, 8 and 10 are unclear with respect to” biomolecular data” acquired. The claims fail to particularly point out and distinctly claim, whether ALL biomolecular data comprise “sequence” information, such that all the data can be transformed into and out of a sequence space? Does the data gathered have any non-sequence information such that any “improvement” could be recognized?
The claims 1 and 10 fail to particularly point out and distinctly claim, what is the difference between a “coarse representation” and a “low dimensional representation”? The specification does not describe sufficiently to differentiate between “coarse representation” and a “low dimensional representation”.
The claims 1 and 10 fail to particularly point out and distinctly claim “ fitness factor”. The claim / or specification does not recite how is the fitness factor calculated, and then used to organize the data?
The claims 1, 10 and 19 fail to particularly point out and distinctly claim “improved candidate sequence”. The claims or/and specification does not describe what aspect sequence is improved.
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 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Subject Matter Eligibility Analysis
Step 1: Statutory Category?
Claim 1 recites a system and therefore, is a machine. (Step 1, Yes).
Claim 10 recites a series of steps and therefore, is a process. (Step 1, Yes).
Step 2A - Prong One: Judicial Exception (JE) Recited?
Independent claim 1 is directed to a system for identifying biomolecules “with a desired property, and performs the following….” method steps recited in independent claim 10.
“Collecting a quantity of biomolecular data”, which is an additional element of data gathering.
“Transforming the biomolecular data from a sequence space”, which is a mathematical concept because the step of data transformation from a higher level of complexity to a lower level of complexity is a mathematic calculation, or a mathematic relationship.” MPEP 2106.04(a).
“Compressing the latent space representation of the biomolecular data”, which is a mathematical concept because the step of data transformation to a smaller size is a mathematic calculation or a mathematic relationship. MPEP 2106.04(a).
“Compressing the coarse representation of the biomolecular data”, which is a mathematical concept because the step of data transformation to a smaller size is a mathematic calculation or a mathematic relationship. MPEP 2106.04(a).
“Organizing the data in the low-dimensional representation”, which is a mental step of observing the data, and making a judgement as to how it should be structured. MPEP 2106.04(a). The concept can be performed in human mind or with pen and paper, therefore is a mental process (including an observation, evaluation, judgment, opinion).
“Choosing a first point from within the low-dimensional representation”, which is choosing a data value, is the mental step of observing the data, evaluating which value meets a condition, and judging whether to select that value. MPEP 2106.04(a).
“Calculating a gradient of the fitness factor at the first point in the low-dimensional representation”, which is a mathematical step of calculation.
“Selecting a second point in the low-dimensional representation of the biomolecular data”, which is choosing a data value, is the mental step of observing the data, evaluating which value meets a condition, and judging whether to select that value. MPEP 2106.04(a).
“Transforming the selected point from within the low-dimensional representation of the biomolecular data”, is a mathematical concept because the step is performed via mathematical equation/algorithm.
Dependent claims 8, 17 are related to the data gathered, and thus are additional elements. MPEP 2106.05(g).
Claims 2-7, 9, 11-16, 18 recite further mathematic concepts summarized below as ; “pooling mechanism”, “informational bottleneck”, “adding negative samples to the latent space representation”, “fitness value less than or equal to the minimum fitness value calculated in the latent space”, “transforming the biomolecular data to a latent space representation”.
Dependent claim 19 is a step of producing a protein, which is the physical production of a physical polypeptide, an extra-solution activity. MPEP 2106.05(g). (Step 2A, Prong One, Yes).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception.
With respect to Step 2A, Prong Two, NO, the claims do not integrate the JE into a practical application (MPEP 2106.04(d)).
Claims 1, 8, 10 and 17 recite “collecting a quantity of biomolecular data”, which is additional non-abstract element(s) of data gathering or a description of the data gathered. Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data necessary to carry out the judicial exception (JE). MPEP 2106.05(g).
The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. MPEP 2106.05(g).
The data gathering steps constitute a general link to a technological environment: the trait prediction methods are intended to be applied to plant populations. (MPEP 2106.05(h), citing Mayo, Bilski, electric Power Group, Genetic Techs Ltd v Merial LLC.)
The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception to provide integration into a practical application. (MPEP 2106.05(g) citing Mayo, PerkinElmer, Inc. v. Interna Ltd, Intellectual Ventures LLC v. Erie Indem. Co., Electric Power Group LLC v. Alstom S.A.).
Claim 1 recites the additional non-abstract element “computer-readable medium with instructions stored thereon” and “a processor perform steps”. The claims provide structures of the computer elements with high level of generality are used to implement the JE. MPEP 2106.05(a), contrasting decisions identifying how the computer implements an abstract idea, such as in McRo to decisions which found no specific interaction with the computer, such as in Affinity Labs of Tex v. DirecTV, LLC.
The computer elements of the claims do not provide improvements to the functioning of the computer itself. MPEP 2106.05(a) I, contrasting decisions indicating an improvement to the computer, such as DDR Holdings, LLC v. Hotels.com LP, with decisions that did not identify an improvement to the computer, such as Fair Warning IP, LLC v. Iatrix Sys.
The computer elements of the claims do not utilize a particular machine. MPEP 2106.05(b): contrasting decisions wherein a particular machine was identified, such as MacKay Radio & Tel. Co. v. Radio Corp. of America, Eibel Process Co. v. Minn. & Ont. Paper Co., with decisions where a general-purpose computer does not qualify as a particular machine, such as Ultramercial, Inc. v. Hulu, LLC, TLI communications, or Eon Corp. IP holdings LLC v. AT&T Mobility LLC.
Hence, these are mere instructions to apply the JE using a computer, and therefore the claim does not recite integrate that JE into a practical application.
Dependent claim(s) 2-9, 11-18 recite(s) an abstract limitation to the JE reciting additional mathematic concepts, or mental processes. Additional abstract limitations cannot provide a practical application of the JE as they are a part of that JE.
Dependent claim 19 is a step of producing a protein, which is the physical production of a physical polypeptide, an extra-solution activity. MPEP 2106.05(g).
The additional element taken individually, and also taken as a combination, do not result in the claims as a whole amounting to significantly more than the judicial exception.
The federal court found claims directed to selecting certain information, analyzing
it using mathematical techniques, and reporting or displaying the results of the analysis
to be a patent-ineligible concept, SAP America, Inc. v. InvestPic, LLC. (2106.04(a)(2)).
(Step 2A, Prong Two, No).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole
amount to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim
(MPEP 2106.05).
With respect to step 2B: NO, the claims do not recite a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05).
“… an "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim, as a whole, amounts to significantly more than the judicial exception itself. Alice Corp…”
With respect to claim(s) 1, 8, 10 and 17: The limitation(s) identified above as non-abstract elements (EIA) related to data gathering do not rise to the level of significantly more than the judicial exception.
Camacho et al. in review article, Cell 173, June 14, 2018 (title: Next-Generation Machine Learning for Biological Networks) and Kimber et al. J. Mol. Sci. 2021, 22, 4435 (title: Deep Learning in Virtual Screening: Recent Applications and Developments) discloses data receiving, data analysis in computer environment.
These elements meet the BRI of the identified data gathering limitations. As such, the prior art recognizes that this data gathering element is routine, well understood and conventional in the art. MPEP 2106.05(d): “If, however, the additional element (or combination of elements) is no more than well-understood, routine, conventional activities previously known to the industry, which is recited at a high level of generality, then this consideration does not favor eligibility.”
With respect to claim(s) 1-9, 12 the limitations identified above as non-abstract elements (EIA) related to general-purpose computer systems do not rise to the level of significantly more than the judicial exception.
Each of disclose computer systems or computing elements which meet the BRI of the claimed computer system or computer system elements, comprising input, output/ display, a processor, and memory.
As such, the prior art (Sebastian et al. review article, Nature Nanotechnology, 2020, 15, 529-544, Mar30, 2020) recognizes that these computing elements are routine, well understood and conventional in the art.
The claims provide structures of the computer elements with high level of generality are used to implement the JE. MPEP 2106.05(a), contrasting decisions identifying how the computer implements an abstract idea, such as in McRo to decisions which found no specific interaction with the computer, such as in Affinity Labs of Tex v. DirecTV, LLC.
The computer elements of the claims do not provide improvements to the functioning of the computer itself. MPEP 2106.05(a) I, contrasting decisions indicating an improvement to the computer, such as DDR Holdings, LLC v. Hotels.com LP, with decisions that did not identify an improvement to the computer, such as FairWarning IP, LLC v. Iatrix Sys.
The computer elements of the claims do not provide improvements to any other technology or technical field. MPEP 2106.05(a) II: contrasting decisions indicating an improvement to the technology, such as Diamond v. Diehr, Trading Techs. Int' l v. CQG Inc, or Intellectual Ventures I v. Symantec Corp, with decisions that did not identify an improvement to the technology, such as Alice Corp, Versata Dev. Group, Inc. v. SAP AM. Inc, or TLI Communications.
The computer elements of the claims do not utilize a particular machine. MPEP 2106.05(b): contrasting decisions wherein a particular machine was identified, such as MacKay Radio & Tel. Co. v. Radio Corp. of America, Eibel Process Co. v. Minn. & Ont. Paper Co., with decisions where a general-purpose computer does not qualify as a particular machine, such as Ultramercial, Inc. v. Hulu, LLC, TLI communications, or Eon Corp. IP holdings LLC v. AT&T Mobility LLC.
Hence, these are mere instructions to apply the JE using a computer, and therefore the claim does not provide significantly more.
Dependent claim(s) 2-9, 11-18 each recite a limitation requiring additional mathematic concepts or mental processes. Additional abstract limitations cannot provide significantly more than the JE as they are a part of that JE (MPEP 2106.05).
In combination, the data gathering steps providing the information required to be acted upon by the JE, performed in a high level general computing environment fail to rise to the level of significantly more than that JE. The data gathering steps provide the data for the JE, which is carried out by the general-purpose computers. No non-routine step or element has clearly been identified.
Dependent claim 19 is a step of producing a protein, which is the physical production of a physical polypeptide, an extra-solution activity. MPEP 2106.05(g).
Production of polypeptides is well-understood or conventional. As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional. Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978).
The claims have all been examined to identify the presence of one or more judicial exceptions. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether the additional limitations integrate the judicial exception into a practical application. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether those additional limitations provide an inventive concept which provides significantly more than those exceptions. For these reasons, the claims, when the limitations are considered individually and as a whole, are rejected under 35 USC § 101 as being directed to non-statutory subject matter.The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. These additional elements as stated above in Step-2A, Prong Two, only amount to necessary data gathering and analysis steps viewed as insignificant extra solution activities that do not add a meaningful limitation to the claims (see MPEP 2106.05(g)). (Step 2B, NO).
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 (i.e., changing from AIA to pre-AIA ) 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 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, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Ranganathan et al. [WO 2021/050923 A1 published date: Mar18, 2021](referred as “Ranganathan”) in view of Wang et al. [NPJ Computational Materials (2019) 5:125, published date: Dec18, 2019] (referred as “Wang”).
Regarding independent claims 1 and 10, Ranganathan teaches systems and methods of designing proteins having a desired functionality [0062] as in claims 1 and 10 teach a system for identifying biomolecules with a desired property.
Regarding claims 1 and 10, Ranganathan teaches a CPU in the processor can execute a computer program including a set of computer-readable instructions that perform various steps of method, and the program being stored in any of the above-described non-transitory electronic memories and/or a hard disk drive, CD, DVD, FLASH drive or any other known storage media [0267] as in claims 1 and 10 teach a non-transitory computer-readable medium with instructions stored thereon, which when executed by a processor perform method steps.
Regarding independent claims 1 and 10, Ranganathan teaches obtaining protein data set for training [0141, 0272, 0289-0297] as in claims 1 and 10 teaches collecting a quantity of biomolecular data.
Regarding independent claims 1 and 10, Ranganathan teaches an encoder, trained on the MSA of a protein family, can be used to convert proteins represented as sequences of amino acids into a continuous vector representation [0146] as in claim 1 teaches transforming the biomolecular sequence data to a latent space representation of the biomolecular data.
Regarding independent claims 1 and 10, Ranganathan teaches use of fitness function as one of the component for multi-dimensional optimization to identify which amino acid sequences are good candidates when designing for the desired functionality [0068]. Ranganathan’s teaching includes selection of candidate points within the latent space to identify a region/neighborhood within the latent space that is likely to correspond to proteins having the desired functionality/attributes [0182-0183][Fig 3A][0311-0318]. Ranganathan further teaches gradient based optimization method, where during the backward pass, the model computes the gradient of the loss function with respect to the current parameters after which the parameters are updated by taking a step of size of a predefined size in the direction of minimized loss. In accelerated methods the step size can be selected to more quickly converge to optimize the loss function [0276-0278][0192][0083] as in claim 1 and 10 teaches organizing the data in the low-dimensional representation of the biomolecular data according to a fitness factor; choosing a first point from within the low-dimensional representation of the biomolecular data; calculating a gradient of the fitness factor at the first point in the low-dimensional representation of the biomolecular data; selecting a second point in the low-dimensional representation of the biomolecular data in a direction indicated by the gradient to have a higher fitness factor than the first point, setting the second point as the first point, then repeating the gradient calculating step until the fitness factor reaches a convergence point or threshold value.
Regarding independent claims 1 and 10, Ranganathan teaches an iterative process is performed to select ever better candidate amino acid sequence, which includes producing proteins for the candidate amino acid sequences and assaying them to measure their functionality. Using a fitness function based on the functionality landscape together with the machine-learning new candidate sequence can be determined having better functionality than the first. The iterative process is then repeated with each iteration yielding new candidate sequence that are better than the previous iteration, until stopping criteria are reached and the final, optimized amino acid sequences are output as the designed proteins. This iterative process is illustrated in Figure 1A, for example as in claims 1 and 10 teaches transforming the selected point from within the low-dimensional representation of the biomolecular data back to the sequence space to identify an improved candidate sequence.
Ranganathan does not teach explicitly compressing the coarse representation of the biomolecular data using an informational bottleneck [0085]. However, Wang teaches an unsupervised learning technique, variational auto-encoders (VAEs) compress data through an information bottleneck that continuously maps an otherwise complex data set into a low-dimensional space and can probabilistically infer the real data distribution via a generating process. Wang’s teaching includes the coarse-grained representation [page 1-3, introduction, and fig.3][page 6-7, section: methods] as in claim 1 teaches compressing the latent space representation of the biomolecular data to a coarse representation using a pooling mechanism; compressing the coarse representation of the biomolecular data to a low- dimensional representation of the biomolecular data using an informational bottleneck.
It would have been obvious to a person of ordinary skill in the art of bioinformatics at the time of the invention to modify the teachings of Ranganathan by incorporating teachings of Wang to arrive at the claimed invention because the teachings of Ranganathan and Wang are well known in the art. A person of ordinary skill in the art of bioinformatics would have motivated to combine well known methods from the arts to arrive at the claimed invention with reasonable success.
Claims 2-9, 11-19 are rejected under 35 U.S.C. 103 as being unpatentable over Ding et al. [Nature Communications, (2019) 10:5644, published online date: Dec10, 2019](referred as “Ding”) in view of Weaver et al. [US 2013/0252280 A1, published date: Sep23, 2013] (referred as “Weaver”).
Regarding dependent claims 2-4, 11-13, Ding teaches a generic doc2vec embedding method, which is learned by pooling sequences from many protein families together and viewing all protein sequences equally [page 8-9][page 8 of supplementary information] as claims 2-4, 11-13 teach the pooling mechanism is an attention-based pooling mechanism …….is a mean or max pooling mechanism……is a recurrent pooling mechanism.
Regarding dependent claims 5, 14, Ding teaches variational auto-encoders (VAEs) compress data through an information bottleneck [page 1, Introduction] as in claims 5, 14 teach informational bottleneck is an autoencoder-type bottleneck.
Regarding dependent claims 6-7, 15-16, Ding teaches fitness concept used in evolution biology, where a protein’s fitness landscape can also be viewed as a fitness function in a high-dimensional discrete space of sequences. Ding’s teaching includes protein sequence data and experimental fitness data to learn protein fitness landscapes [page 7-8, section: Navigating protein fitness landscapes in latent space] as in claims 6-7, 15-16 teach adding negative samples to the latent space representation of the biomolecular data. negative samples have a fitness value have a fitness value less than or equal to the minimum fitness value calculated in the latent space.
Regarding dependent claims 8, 17, Ding teaches using protein sequence , which is a biomolecule as in claims 8, 17 teach biomolecular data comprises sequencing data of at least one lead biomolecule.
Regarding dependent claims 8, 17 and 18, Ding teaches using auto-encoders that converts biomolecule sequence data to latent space. Ding discloses hidden layers in in the encoder/decoder model. Ding is silent on biomolecule is lead compound [page 12, section: Data availability]. However, Ranganathan teaching includes biomolecules as lead drug compounds [0008, 0011-0012].
It would have been obvious to a person of ordinary skill in the art at the time of the invention to modify the teaching of Ding to include a biomolecule, which is a lead compound, as taught by Ranganathan to arrive at the claimed invention with reasonable success because this modification involves a simple substitution of biomolecule with a lead biomolecule molecule yielding predictable results.
Regarding claim 19, Ranganathan teaches the method can further include compiling, at the processor, a new or improved predicted biomolecule list with associated sequence features [0043] as in claim 19 teaches producing a protein with the improved candidate sequence.
The prior art made of record and not relied upon is considered pertinent to applicant's claims 1 and 10.
Ranganathan et al., US 2022/0348903 A1, title: Method and apparatus using machine learning for evolutionary data-driven design of proteins and other sequence defined biomolecules.
Fox et al. , US 2008/0220990 A1, title: Methods, systems, and software for
identifying functional bo-molecules.
Castro et al., online publication: arXiv:2201.09948v2, title: ReLSO: A Transformer-based Model for Latent Space Optimization and Generation of Proteins
Conclusion
Claims 1-19 are not allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PRAMOD KUMAR MOHANTA whose telephone number is (571)272-8775. The examiner can normally be reached Mon-Fri 9:00am-5:00pm EST.
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/P.K.M./Examiner, Art Unit 1686
/MARY K ZEMAN/ Primary Examiner, Art Unit 1686
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