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 Status
Claims 1-17 are currently pending and under exam herein.
Priority
The Effective Filing Date of the instant invention is the actual filing date of 13 January 2022. No priority is claimed.
Information Disclosure Statement
The Information Disclosure Statement filed 18 October 2022 is in compliance with the provisions of 37 CFR 1.97 and has therefore been considered. A signed copy of the IDS is included with this Office Action.
Drawings
The Drawings filed 13 January 2022 have been accepted.
Specification
Note: All references to the Specification herein pertain to the PG publication: US20230222176A1.
Claim Objections
Claims 10 and 15 objected to because of the following informalities: Claims 10 and 15 recite, “receiving, by the one or more processors, a classification label data set comprising a plurality of classification label constructed from biological sample data labels”, which should be amended to recite “labels”, as in “comprising a plurality of classification labels constructed…” Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)-Indefiniteness
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 1-17 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1, 13, and 17 recite, “receiving, by the one or more processors, a first biological data set comprising a first plurality of biological sample data collected from a set of patients…to generate a first trained VAE comprising a first latent space vector of the first biological data set…receiving, by the one or more processors, a second biological data set comprising a second plurality of biological sample data collected from a patient, different from the set of patients” wherein the reference to “the set of patients” in the 3rd claim step is indefinite as it is unclear if this is intended to refer back to “the first set of patients” or some other patient set. For examination purposes, the claim is interpreted as “different from the first set of patients”. Clarification is requested.
Claim 6 recites, “further comprising corrupting the received data set using a corruption function” wherein the claim is indefinite with respect to “data set” as claim 1, from which claim 6 depends, refers to more than one type of “data set” (e.g., a first biological data set; a second biological dataset). For examination purposes, the claim is interpreted as “further comprising corrupting the received first biological data set…” Clarification is requested.
Claims 10 and 15 recite, “receiving, by the one or more processors, a classification label data set comprising a plurality of classification label constructed from biological sample data labels”, wherein the claim is indefinite with respect to the biological sample data labels from which the classification label[s] are constructed because there are no steps directed to “data labels” in claims 1 and 13, from which claims 10 and 15, respectively depend. Clarification is requested.
Claims 10 and 15 recite, “classifying, by the one or more processors, the latent space representation of the second biological data set based on a first latent space vector”, wherein the claim is not clear with respect to what classification is intended. The claim includes recitation only of biological sample data sets without any indication for what the data are labeled, e.g. types of analytes, patient demographic data, disease classes, such as cancer/non-cancer etc... Further, the claim is indefinite with respect to classification of the second set. The parameters of the first latent space that would lend to “classification” of the second space are not clear. Claim 16, reciting “the set of classifications” is also unclear for the same reasons. Clarification through clearer claim language is requested.
Claim 11 recites, “wherein the classifying comprises generating a disease prediction based on the second biological data set”. However, the steps by which “disease prediction” are generated by classification are not clear, as no conditions by which this occurs are claimed. Clarification is requested through clearer claim language.
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 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The instant rejection reflects the framework as outlined in the MPEP at 2106.04:
Framework with which to Evaluate Subject Matter Eligibility:
(1) Are the claims directed to a process, machine, manufacture or composition of matter;
(2A) Prong One: Do the claims recite a judicially recognized exception, i.e. a law of nature, a natural phenomenon, or an abstract idea;
Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and
(2B) If the claims do not integrate the judicial exception, do the claims provide an inventive concept.
Framework Analysis as Pertains to the Instant Claims:
Step 1 Analysis: Are claims directed to process, machine, manufacture/composition of matter
With respect to step (1): yes, the claims are directed to a method; a system; and a non-transitory computer-readable storage medium.
Step 2A, Prong 1 Analysis: Do claims recite abstract idea
With respect to step (2A)(1), yes, the claims recite abstract ideas. The MPEP at 2106.04(a)(2) further explains that abstract ideas are defined as:
mathematical concepts, (mathematical formulas or equations, mathematical relationships and mathematical calculations);
certain methods of organizing human activity (fundamental economic practices or principles, managing personal behavior or relationships or interactions between people); and/or
mental processes (procedures for observing, evaluating, analyzing/ judging and organizing information).
With respect to the instant claims, under the (2A)(1) evaluation, the claims are found herein to recite abstract ideas that fall into the grouping of mental processes (in particular procedures for observing, analyzing and organizing information) and in conjunction with mathematical concepts (in particular mathematical relationships and formulas).
The claim steps to abstract ideas are as follows:
Claims 1, 13, and 17: processing, by the one or more processors, the first biological data set using a first variational autoencoder (VAE) to generate a first trained VAE comprising a first latent space vector of the first biological data set comprising a plurality of values corresponding to each latent space dimension of the latent space vector, the latent space vector having lower dimensionality than the biological sample data set; generating, by the one or more processors, a latent space representation of the second biological data set based on a first latent space vector, wherein a variational autoencoder, under the BRI of the instant claims, is a step directed to mathematical concepts whereby said operations of an autoencoder perform data transformation (high to low dimension) and generate latent space vectors as representation. Latent space representation is graph mathematics. The Specification at [0027] provides that data dimensionality is reduced to eigenvalues, for example.
Claim 2: wherein the first VAE is a βVAE, which is a further limitation to the above autoencoder. It is further noted with respect to mathematical concepts herein that there is no structure associated with the VAE nor are there particular layers, generators, discriminators, or other elements. There are no limits to said VAE, other than including the VAE serves a functional description, “to generate a fist trained VAE comprising latent space vectors…”. No particular type of “VAE” algorithm is set forth in this limitation nor are there any recitations of training protocols (algorithms).
Claim 6: corrupting the received data set using a corruption function, wherein said operation is directed to mathematical concepts that include corruption functions. Corruption functions (claim 7), as disclosed in the Specification and understood in the art are those functions that include salt and pepper function; Gaussian function; masking functions [0006] (claim 8). “Functions” are math, e.g. y = f(x). Claim 9 further is limited to loss function that is a forward or reverse KL divergence.
Claim 10 and 15: processing, by the one or more processors, the classification label data set using a second VAE to generate a second trained VAE comprising a second latent space vector of the classification label data set, the second latent space vector comprising a plurality of values corresponding to each latent space dimension of the classification label data, the latent representation having lower dimensionality than the classification label data set; communicating, by the one or more processors, the latent space representation of the second biological data set to the second VAE; and classifying, by the one or more processors, the latent space representation of the second biological data set based on a first latent space vector, wherein processing using the VAE is addressed above and is also directed to mathematical concepts. Further the steps directed to classifying the latent space is a step that is both mathematical concept and a mental process, wherein one can merely use the generated data and place said data into groups (mental) or use mathematical operations of “classification” to perform said task. For example, classification can include the use of linear algebra for data representations in vector form, for example. The Specification includes that classification is performed using an autoencoder.
Claim 11: classifying comprises generating a disease prediction based on the second biological data set, which is a further limitation to the step of classifying using prediction techniques. Prediction techniques under the BRI are fairly interpreted as including the use of math.
Claims 12 and 16: processing, by the one or more processors, the set of classifications using the second VAE to generate a classification representation of the set of classifications, the second latent representation having lower dimensionality than the set of classifications; communicating, by the one or more processors, the classification representation to the first VAE; and generating, by the one or more processors, a predicted biological data set based on the classification representation, wherein processing using the VAE is addressed above and is also directed to mathematical concepts. Further the steps directed to classifying the latent space is a step that is both mathematical concept and a mental process, wherein one can merely use the generated data and place said data into groups (mental) or use mathematical operations of “classification” to perform said task. For example, classification can include the use of linear algebra for data representations in vector form, for example. The Specification includes that classification is performed using an autoencoder.
As such, the recitations are mathematic concepts which encompasses mathematic relationships, calculations, and formulae or algorithms. (MPEP 2106.04(a)(2) subsection) and thus claims explicitly recite numerous elements that, individually and in combination, constitute abstract ideas.
Further with respect to the Broadest Reasonable Interpretation (BRI) it is noted that there are no specifics as to the methodology involved in “processing” or in “generating” and thus, under the BRI, one could simply, for example, perform said operation with pen and paper, or, alternatively with the aid of a generic computer as a tool to perform said calculations. These recitations are similar to the concepts of collecting information, analyzing it and providing certain results from the collection and analysis (Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations (Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in (Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind with pen and paper, and can include mathematical concepts.
Further, see MPEP § 2106.04(a)(2), subsection III. The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation (see, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674: noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016): holding that claims to a mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper"). Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind" (see Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016): holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer").
Step 2A, Prong 2 Analysis: Integration to a Practical Application
Because the claims do recite judicial exceptions, direction under (2A)(2) provides that the claims must be examined further to determine whether they integrate the abstract ideas into a practical application (MPEP 2106.04(d). A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception. This is performed by analyzing the additional elements of the claim to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim is said to fail to integrate the abstract idea into a practical application (MPEP 2106.04(d).III).
With respect to the instant recitations, the claims recite the following additional elements:
Claims 1, 13, and 17: receiving, by the one or more processors, a first biological data set comprising a first plurality of biological sample data collected from a set of patients; receiving, by the one or more processors, a second biological data set comprising a second plurality of biological sample data collected from a patient, different from the set of patients, wherein said operations are directed to “receiving” in the form of “data”. Those steps directed to data gathering perform functions of collecting the data needed to carry out the abstract idea. Data gathering does not impose any meaningful limitation on the abstract idea, or on how the abstract idea is performed. Data gathering steps are not sufficient to integrate an abstract idea into a practical application. (MPEP 2106.05(g). Claims 3-5 include further limitations to the types of data and Claims 10, 12, 15 and 16 also include steps to “receiving…data” and, as such, are analyzed as above.
Claims 1, 13, and 17: computer-implemented; system; processor; storage device; storage media; instructions, which are recited at a high level of generality and not directed to any specific machine. As such, said limitations do not describe any specific computational steps by which the “computer parts” perform or carry out the abstract idea, nor do they provide any details of how specific structures of the computer, such as the computer-readable recording media, are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the abstract idea. Hence, these are mere instructions to apply the abstract idea using a computer, and therefore the claim does not integrate that abstract idea into a practical application. The courts have weighed in and consistently maintained that when, for example, a memory, display, processor, machine, etc… are recited so generically (i.e., no details are provided) that they represent no more than mere instructions to apply the judicial exception on a computer, and these limitations may be viewed as nothing more than generally linking the use of the judicial exception to the technological environment of a computer. (see MPEP 2106.05(f)).
Step 2B Analysis: Do Claims Provide an Inventive Concept
The claims are lastly evaluated using the (2B) analysis, wherein it is determined that because the claims recite abstract ideas, and do not integrate that abstract ideas into a practical application, the claims also lack a specific inventive concept. Applicant is reminded that the judicial exception alone cannot provide the inventive concept or the practical application and that the identification of whether the additional elements amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they provide significantly more than the judicial exception. (MPEP 2106.05.A i-vi).
With respect to the instant claims, the additional elements of data gathering described above do not rise to the level of significantly more than the judicial exception. As directed in the Berkheimer memorandum of 19 April 2018 and set forth in the MPEP, determinations of whether or not additional elements (or a combination of additional elements) may provide significantly more and/or an inventive concept rests in whether or not the additional elements (or combination of elements) represents well-understood, routine, conventional activity. Said assessment is made by a factual determination stemming from a conclusion that an element (or combination of elements) is widely prevalent or in common use in the relevant industry, which is determined by either a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to the instant claims, the prior art to Kingma and Welling (Foundations and Trends in Machine Learning (2019) Vol. 12:89 pages) disclose the principals of VAEs wherein at the foundation of said operations is “data”. The instant claims include the additional elements of “receiving…data set” wherein steps of receiving data are routinely done by the prior art autoencoders. VAEs include processes to “disentangle[d], semantically meaningful, statistically independent and causal factors of variation in data” (p.4-Kigma and Welling). The “data” (in this instance, biological data) do not change the operation of the computer-implementation, as any computer serves to perform said functions. Further, the prior art to Wei et al. (IEEE Access (2021) Vol. 9:4939-4956) disclose biomedical datasets that are operational in VAEs (abstract). As such, operations of data gathering also encompass steps that are routine, well-understood and conventional in the art.
With respect to claims 1-17, the computer-related elements or the general purpose computer do not rise to the level of significantly more than the judicial exception. For example, the instant Specification discloses that computer processors and systems, as example, are commercially available or widely used at least at [0078] (computing device intended to represent various forms of digital computers, such as laptops, desktops, workstations etc.). The additional elements are set forth at such a high level of generality that they can be met by any general purpose computer. Therefore, the computer components constitute no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than an abstract idea (see MPEP 2106.05(b)I-III).
The dependent claims have been analyzed with respect to step 2B and none of these claims provide a specific inventive concept, as they all fail to rise to the level of significantly more than the identified judicial exception.
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.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
1. Claims 1, 2, 6-9, 13 and 17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kingma and Welling (Foundations and Trends in Machine Learning (2019) Vol. 12:89 pages).
Claim 1 is directed to:
A computer-implemented biological data classification method executed by one or more processors and comprising:
receiving, by the one or more processors, a first biological data set comprising a first plurality of biological sample data collected from a set of patients (Kingma and Welling disclose the “dataset D” at Figure 2.1; p.17);
processing, by the one or more processors, the first biological data set using a first variational autoencoder (VAE) to generate a first trained VAE comprising a first latent space vector of the first biological data set comprising a plurality of values corresponding to each latent space dimension of the latent space vector, the latent space vector having lower dimensionality than the biological sample data set (Kingma and Welling disclose processing by an encoder to generate a latent space vector with lower dimensionality at Figure 2.1. p. 17);
receiving, by the one or more processors, a second biological data set comprising a second plurality of biological sample data collected from a patient, different from the set of patients (Kingma and Welling disclose reparameterization whereby variables are differential (p. 20); and
generating, by the one or more processors, a latent space representation of the second biological data set based on a first latent space vector (Kingma and Welling at p. 17, Figure 2.1).
Insofar as VAEs are applied to any data, biological “data” for input into the VAE would be an inherent teaching by Kingma and Welling disclose the principals of VAEs and that they are operational in a variety of fields, including for data applications in chemistry, for example (p. 6: The framework of variational autoencoders (VAEs) (Kingma and Welling, 2014; Rezende et al., 2014) provides a principled method for jointly learning deep latent-variable models and corresponding inference models using stochastic gradient descent. The framework has a wide array of applications from generative modeling, semi-supervised learning to representation learning; p. 56, Figure 4.2).
Claims 13 is directed to the system for implementing said method above and claim 17 is directed to the computer media with instructions to perform said method. The prior art of Kingma and Welling disclose a system and processors and media, as “machine learning” takes place in the environment of a computer.
With respect to claim 2, Kingma and Welling disclose βVAEs (p. 61 In (Higgins et al., 2017) (β-VAE) it was proposed to strengthen the contribution of DKL(qθ(z|x)||pθ(z)), thus restricting the information flow through the latent space, which was shown to improve disentanglement of latent factors, further studied in (Chen et al., 2018).
With respect to claims 6 and 8, Kingma and Welling disclose corruption functions that are Gaussian function, for example at pp. 24-26.
With respect to claim 9, Kingma and Welling disclose KL divergence models beginning at p. 28 (see section 2.7).
2. Claims 1, 2, 6-9, 13 and 17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wei et al. (IEEE Access (2021) Vol. 9:4939-4956).
Claim 1 is directed to:
A computer-implemented biological data classification method executed by one or more processors and comprising (Wei et al. disclose biological data classification using VAEs-abstract):
receiving, by the one or more processors, a first biological data set comprising a first plurality of biological sample data collected from a set of patients (Wei et al. disclose data directed to biomedical data at the abstract; p. 4941 Table 1);
processing, by the one or more processors, the first biological data set using a first variational autoencoder (VAE) to generate a first trained VAE comprising a first latent space vector of the first biological data set comprising a plurality of values corresponding to each latent space dimension of the latent space vector, the latent space vector having lower dimensionality than the biological sample data set (Wei et al. disclose architecture of the variational autoencoder at p. 4940, Figure 1 with data inputs and sets μ, σ and latent space vector Z);
receiving, by the one or more processors, a second biological data set comprising a second plurality of biological sample data collected from a patient, different from the set of patients (Wei et al. disclose architecture of the variational autoencoder at p. 4940, Figure 1 with data inputs and sets μ, σ and latent space vector Z). ; and
generating, by the one or more processors, a latent space representation of the second biological data set based on a first latent space vector (Wei et al. disclose architecture of the variational autoencoder at p. 4940, Figure 1 with data inputs and sets μ, σ and latent space vector Z with output).
Claims 13 is directed to the system for implementing said method above and claim 17 is directed to the computer media with instructions to perform said method. The prior art of Wei et al. disclose a system and processors and media, as “machine learning” takes place in the environment of a computer.
With respect to claim 2, Wei et al. disclose βVAEs at p. 4940 wherein image quality and diversity are improved using βVAEs, giving the ability to classify.
With respect to claim 6-8, Wei et al. disclose corrupting the received data using Gaussian function at p. 4941, col. 2.
With respect to claim 9, Wei et al. disclose KL divergence at p. 4941, col. 1.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
1. Claim 1-2, 6-9, 13, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Kingma and Welling (Foundations and Trends in Machine Learning (2019) Vol. 12:89 pages) in view of Wei et al. (IEEE Access (2021) Vol. 9:4939-4956).
Claim 1 is directed to:
A computer-implemented biological data classification method executed by one or more processors and comprising:
receiving, by the one or more processors, a first biological data set comprising a first plurality of biological sample data collected from a set of patients (Kingma and Welling disclose the “dataset D” at Figure 2.1; p.17);
processing, by the one or more processors, the first biological data set using a first variational autoencoder (VAE) to generate a first trained VAE comprising a first latent space vector of the first biological data set comprising a plurality of values corresponding to each latent space dimension of the latent space vector, the latent space vector having lower dimensionality than the biological sample data set (Kingma and Welling disclose processing by an encoder to generate a latent space vector with lower dimensionality at Figure 2.1. p. 17);
receiving, by the one or more processors, a second biological data set comprising a second plurality of biological sample data collected from a patient, different from the set of patients (Kingma and Welling disclose reparameterization whereby variables are differential (p. 20). ; and
generating, by the one or more processors, a latent space representation of the second biological data set based on a first latent space vector (Kingma and Welling at p. 17, Figure 2.1).
Insofar as VAEs are applied to any data, biological “data” for input into the VAE would be an inherent teaching by Kingma and Welling disclose the principals of VAEs and that they are operational in a variety of fields, including for data applications in chemistry, for example (p. 6: The framework of variational autoencoders (VAEs) (Kingma and Welling, 2014; Rezende et al., 2014) provides a principled method for jointly learning deep latent-variable models and corresponding inference models using stochastic gradient descent. The framework has a wide array of applications from generative modeling, semi-supervised learning to representation learning; p. 56, Figure 4.2).
Claims 13 is directed to the system for implementing said method above and claim 17 is directed to the computer media with instructions to perform said method. The prior art of Kingma and Welling disclose a system and processors and media, as “machine learning” takes place in the environment of a computer.
In the event that Kingma and Welling is interpreted as not specifically disclosing “biomedical” data as used in a VAE as in the above rejection under 102, the prior art to Wei et al. disclose VAEs for extraction of relevant biomedical information from learned features of biological datasets (abstract; p. 4939).
As such, it would alternatively have been prima facie obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to have included biomedical data as the “data” for a VAE, as is disclosed in the art to, for example, Wei et al. As both references are in the same field of endeavor and Kingma and Welling disclose VAEs operational for any data task, one would have been motivated to include the biomedical data as disclosed in Wei et al. for generative modeling and low-dimensional data representation (Wei et al. at p. 4939, col. 2).
Claims 13 is directed to the system for implementing said method above and claim 17 is directed to the computer media with instructions to perform said method. The prior art of Kingma and Welling disclose a system and processors and media, as “machine learning” takes place in the environment of a computer, as does the prior art to Wei et al.
With respect to claim 2, Kingma and Welling disclose βVAEs (p. 61 In (Higgins et al., 2017) (β-VAE) it was proposed to strengthen the contribution of DKL(qθ(z|x)||pθ(z)), thus restricting the information flow through the latent space, which was shown to improve disentanglement of latent factors, further studied in (Chen et al., 2018).
With respect to claims 6 and 8, Kingma and Welling disclose corruption functions that are Gaussian function, for example at pp. 24-26.
With respect to claim 9, Kingma and Welling disclose KL divergence models beginning at p. 28 (see section 2.7)
2. Claim 3-5, 10-12, and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Kingma and Welling (Foundations and Trends in Machine Learning (2019) Vol. 12:89 pages) in view of Wei et al. (IEEE Access (2021) Vol. 9:4939-4956), as applied to claims 1 and 13 above and in further view of Sadati et al. ("Representation learning with autoencoders for electronic health records: a comparative study." arXiv preprint arXiv:1801.02961 (2018):10 pages).
Claim 1 is directed to:
A computer-implemented biological data classification method executed by one or more processors and comprising:
receiving, by the one or more processors, a first biological data set comprising a first plurality of biological sample data collected from a set of patients (Kingma and Welling disclose the “dataset D” at Figure 2.1; p.17);
processing, by the one or more processors, the first biological data set using a first variational autoencoder (VAE) to generate a first trained VAE comprising a first latent space vector of the first biological data set comprising a plurality of values corresponding to each latent space dimension of the latent space vector, the latent space vector having lower dimensionality than the biological sample data set (Kingma and Welling disclose processing by an encoder to generate a latent space vector with lower dimensionality at Figure 2.1. p. 17);
receiving, by the one or more processors, a second biological data set comprising a second plurality of biological sample data collected from a patient, different from the set of patients (Kingma and Welling disclose reparameterization whereby variables are differential (p. 20). ; and
generating, by the one or more processors, a latent space representation of the second biological data set based on a first latent space vector (Kingma and Welling at p. 17, Figure 2.1).
Insofar as VAEs are applied to any data, biological “data” for input into the VAE would be an inherent teaching by Kingma and Welling disclose the principals of VAEs and that they are operational in a variety of fields, including for data applications in chemistry, for example (p. 6: The framework of variational autoencoders (VAEs) (Kingma and Welling, 2014; Rezende et al., 2014) provides a principled method for jointly learning deep latent-variable models and corresponding inference models using stochastic gradient descent. The framework has a wide array of applications from generative modeling, semi-supervised learning to representation learning; p. 56, Figure 4.2).
Claims 13 is directed to the system for implementing said method above. The prior art of Kingma and Welling disclose a system and processors as “machine learning” that takes place in the environment of a computer.
In the event that Kingma and Welling is interpreted as not specifically disclosing “biomedical” data as used in a VAE as in the above rejection under 102, the prior art to Wei et al. disclose VAEs for extraction of relevant biomedical information from learned features of biological datasets (abstract; p. 4939).
As such, it would alternatively have been prima facie obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to have included biomedical data as the “data” for a VAE, as is disclosed in the art to, for example, Wei et al. As both references are in the same field of endeavor and Kingma and Welling disclose VAEs operational for any data task, one would have been motivated to include the biomedical data as disclosed in Wei et al. for generative modeling and low-dimensional data representation (Wei et al. at p. 4939, col. 2).
With respect to specific sample types of claims 3-5 or classification label datasets for predictions as in claims 10-12 and 15-16, neither the prior art to Kingma and Welling nor Wei et al. specifically disclose VAE classifications. However, the prior art to Sadati et al. disclose classification of mixed data types for biomedical health records data that include data types such as lab test data, history data, imaging data etc. (p. 3, Figure 1). Sadati et al. disclose deep learning approaches that include deep autoencoders for diagnostic prediction purposes (p. 3, col. 2). With respect to classification labeling, Sadati et al. disclose preprocessing such that categorical features are represented (classification) (p. 4, col. 1). Sadati et al. further disclose that the performance of multiple networks as depicted in Figure 2 can be various in various cases and therefore it would be obvious, given said teaching to include two types of VAEs for purposes of assessing biomedical data for prognosis purposes.
In combination with the VAEs as disclosed by Kingman and Welling and those of Wei et al. for biomedical data, the groundwork for applying VAEs for biomedical data in the context of classification of mixed data as in Sadati et al. would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, as each of said references is in the field of VAEs, in particular. The art to Kingma and Welling provide the details of the mathematical components for VAEs. The prior art to Wei et al. establish that said VAEs can be utilized for various types of biomedical data, and the prior art to Sadati et al. provide for specifics of electronic heath record data that can include patient laboratory results and multiple encoders.
Prior Art Made of Record
The following prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
1. 11,636,283 to Poole et al. disclosing VAE neural network systems.
2. 12,223,432 to Chakraborty et al. disclosing deep learning models for classification/regression.
3. 2022/0414451 to Gurev et al. disclosing VAEs employed with mechanistic models for biological data.
Conclusion
No claims are allowed.
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