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 .
Information Disclosure Statement
The IDS filed 11/30/2022 and 12/27/2022 have been considered by the Examiner.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged. Priority of US application 63/034140 filed 6/03/2020 is acknowledged.
Status of Claims
Claims 38-57 are under examination.
Claims 1-37 are cancelled.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 38 and 41-57 are provisionally rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-5, 18, 19, and 24-26 of US 11,424,007. Although the conflicting claims are not identical, they are not patentably distinct from each other because the patented claims are either species of the instant claims or have only minor differences encompassed by the instant generic claims. Instantly, independent claim 38 is broader than patented claims 1 and 3 which read on the instant independent claim 38.
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 38-57 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 38-49 are drawn to a method, so a process.
Claims 50-56 are drawn to a system comprising a processor, so a machine.
Claim 57 is drawn to a non-transitory computer readable medium, so a manufacture.
Step 2A Prong One: Identification of an Abstract Idea
The claim(s) recite(s):
1. collecting a first training data set, the first training data set comprising first experimental data from a series of libraries, the first experimental data evaluating a compatibility of xenotransplantation of a non-human sample from a non-human donor into a human recipient.
This step reads on organizing available information which is a step that can be performed by the human mind and is therefore an abstract idea.
2. training an artificial intelligence model with the first training data set to identify one or more compatible nucleotide sequences for introduction into a genome of a non-human donor, wherein the artificial intelligence model is selected from a set of: a neural network model, a Bayesian network model, a support vector machine model, a k-nearest neighbors model, or a combination thereof.
This step reads on training a mathematical model which is math. Training a neural network model, a Bayesian network model, a support vector machine model or a k-nearest neighbors model is entirely mathematical. Each of the recited models is math and therefore the step is drawn to an abstract idea. The limitation of “to identify one or more compatible nucleotide sequences” for introduction into donor is an intended use.
3. obtaining first human nucleotide sequencing data relating to a first human patient or patient population.
This step can be performed by the human mind and is therefore an abstract idea.
4. identifying, using the trained artificial intelligence model and the first human nucleotide sequencing data, a first set of one or more compatible nucleotide sequences for introduction into a genome of a first non-human donor.
This step is drawn to “identifying” a first set of compatible nucleotide sequences which is drawn to observing nucleotide data and performing a selection. Such mental observations or evaluations fall within the “mental processes” groupings of abstract ideas. The limitation of “using the trained artificial intelligence model” is drawn to mere instructions of implement an abstract idea on a generic computer. The artificial intelligence model is used to generally apply the abstract idea without limiting how the trained AI model functions. The AI model is described at a high level such that it amounts to using a computer to calculate math (i.e. a neural network, a Bayesian network model, a support vector machine model or a k-nearest neighbors model).
See 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, Example 47 (claim 2).
5. designing a first candidate non-human sample from the first non-human donor using the identified first set of one or more compatible nucleotide sequences.
This step reads on a mental process of contemplating designs for a non-human sample such as cells, tissue or organs, from a nucleic acids. The step reads on an analysis that can be performed by the human mind and is therefore an abstract idea.
6. obtaining a second training data set, the second training data set comprising second experimental data evaluating a compatibility of xenotransplantation of the first candidate non- human sample in the first human patient or patient population.
This step reads on selecting experimental information which can be performed by the human mind and is therefore an abstract idea.
7. refining the artificial intelligence model with the second training data set.
This step reads on performing math with additional information which is the second training data set. The artificial intelligence model reads on mathematics performed on a computer. Inputting additional experimental data to a mathematical model or calibrating a mathematical model with additional information amounts to math and is therefore an abstract idea.
Dependent claims 40-49 and 51-56 are further dawn to describing the data being analyzed by the abstract idea and further abstract idea steps and are therefore also judicial exceptions.
Step 2A Prong Two: Consideration of Practical Application
The claims result in a step of refining the artificial intelligence model with a second training data set. The step is drawn to an abstract idea. The claims do not recite any additional elements that integrate the abstract idea 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. a computer processor and memory, as in claim 50.
2. computer readable storage medium, as in claim 57.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of the drawn to the computer system with process and memory and computer readable medium, are 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 38-57 are rejected under 35 U.S.C. 103(a) as being unpatentable over Wohlgemuth et al. (US2010/0092989) in view of Pineda et al. (Frontiers in Immunology 8 (2017) pages 1-17).
Wohlgemuth et al. teach designing diagnostic oligonucleotides from public databases (par. 0061); Wohlgemuth et al. teach (par. 0129) diagnostic oligonucleotides for use in treatment of xenograft recipients of transplanted organs from a non-human animal to a human (i.e. collecting a first training set comprising first experimental data from a series of libraries evaluating compatibility of xenotransplantation of a non-human sample into a human donor), as in claims 38, 50 and 57.
Wohlgemuth et al. do not specifically teach training an artificial intelligence model with the first training data to identify one or more compatible nucleotide sequences for introduction into a genome of a non-human donor, as in claims 38, 50 and 57.
However, Wohlgemuth teach (par. 0122-0123) identifying individual diagnostic oligonucleotides or oligonucleotide sets for monitoring organ transplant success, rejection and treatment that can be used as diagnostic tests for transplant rejection and success. Wohlgemuth et al. teach that the diagnostic oligonucleotides and diagnostic oligonucleotide sets are applicable to any organ transplant population.
Furthermore, Wohlgemuth et al. also teach a diagnostic classifier such as a mathematical function that assigns samples to diagnostic categories based on expression data that is applied to unknown sample expression levels (par. 0098); neural networks are taught (par. 0101) for classifying samples into diagnostic groups (par. 0101); and using genes as classifiers (par. 0109-0110) where genes are correlated with stage of rejection in organ biopsies (par. 0124).
It would have been obvious to one of ordinary skill to combine the teachings of Wohlgemuth et al. for diagnostic oligonucleotides that distinguish transplant rejection or success including in treatment of xenograft recipients of transplanted organs from a non-human animal to a human (par. 0129) with the teaching of Wohlgemuth et al. for neural network classifiers that classify diagnostic samples of genes based on rejection in organ biopsies. At the time of the invention a practitioner could have combined the diagnostic oligonucleotides with a neural network classifier to arrive at a trained neural network classifier that identifies which oligonucleotides would result in successful transplantation (i.e. compatible nucleotide sequences for introduction into a genome of a non-human donor). Wohlgemuth et al. teach that diagnostic oligonucleotide sets are applicable to any organ transplant population (par. 0122-0123) and can be use in treatment of xenograft recipients of transplanted organs from a non-human animal to a human (par. 0129). Such a combination is merely a "predictable use of prior art elements according to their established functions." KSR Int’l 7, 127 S. Ct. at 1740.
Wohlgemuth et al. teach expression analysis for patient samples and used for classification analysis (par. 0110)(i.e. obtaining first nucleotide sequencing data relating to first human patient or patient population), as in claim 38.
Wohlgemuth et al. teach that once discrimination genes sets are identified the diagnostic classifier is applied to unknown sample expression levels (par. 0098)(i.e. identifying using the trained artificial intelligence model and first nucleotide sequence data, a first set of compatible nucleotide sequence for introduction into a genome), as in claims 38, 50 and 57.
Wohlgemuth et al. teach a learning component where expression profiles and relevant subject data are compiled and monitored and correlation are refined (par. 0217)(i.e. refining the artificial intelligence model with a second training set), as in claim 38, 50 and 57.
Wohlgemuth et al. do not specifically teach designing a first candidate non-human sample from the first non-human donor using identified first set of one or more compatible nucleotide sequences, as in claims 38, 50 and 57.
Wohlgemuth et al. do not specifically teach obtaining a second training data set comprising second experimental data evaluating compatibility of xenotransplantation.
Pineda et al. teach (Abstract) a computational approach using donor and recipient exome sequencing and gene expression to predict clinical post-transplant outcome; statistical analysis of donor-recipient kidney transplant pairs with biopsy-confirmed clinical outcomes of rejection was performed (i.e. designing a first candidate sample from the non-human donor using identified compatible nucleotide sequences), as in claims 38, 50 and 57.
Pineda et al. teach (Abstract) machine learning is used to predict variants with post transplant antibody mediated rejection; variants that are associated with increased AMR (rejection) are found (page 4, col. 2, par. 2)(i.e. obtaining a second training data set comprising second experimental data evaluating a compatibility of transplantation of the first non-human sample in the first patient), as in claim 38, 50 and 57.
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 Wohlgemuth et al. of using diagnostic oligonucleotides to classify nucleotides for successful xenotransplantation with the teachings of Pineda et al. for using in silico statistical analysis and machine learning to determine if sequences from a donor would result in donor-recipient match. It would be obvious to one of ordinary skill to use the machine learning and computational methods wherein the donor is a non-human. Pineda et al. provide motivation by teaching that their machine learning techniques provide a robust prediction of post-transplant rejection and (page 11, par. 2). One of skill in the art would have been motivated to combine Wohlgemuth et al. and Pineda et al. because both teach genetic sequence analysis with machine learning techniques to determine sequences that can be used for successful transplantation.
Regarding dependent claims 2-49, and 51-56
Pineda et al. teach that HLA matching between donor and acceptor is an important criteria and any cell displaying another HLA type (page 2, col. 1, par. 2); using donor-recipient sequence data for matching is taught (page 2, col. 1, par. 4) which would make obvious sequence data from MHC (for the non-human donor) and HLA for a human wherein MHC is the non-human generic name and equivalent of HLA in humans, as in claim 39.
Pineda et al. teach donor-recipient HLA matching (page 2, col. 1, par. 2) including nHLA antigen matching (page 3, col. 1, par. 2) which suggests a mixed lymphocyte reaction assay, as in claim 40.
Pineda et al. teach (Abstract) machine learning is used to predict variants with post transplant antibody mediated rejection; variants that are associated with increased AMR (rejection) are found (page 4, col. 2, par. 2) wherein it would be obvious to apply the machine learning to sequences for non-human donors. The substitution of human to non-human donors would be a simple substitution of one known equivalent element for another to obtain predictable results (i.e. modeling in silico outcome of xenotransplantation), as in claim 41.
Wohlgemuth et al. teach diagnostic oligonucleotides related to diagnostic genes with a correlation to transplant rejection (par. 0061)(i.e. genomic sequences, nucleotide sequences), as in claim 42.
Wohlgemuth et al. teach detecting transplant rejection by measuring proteins expressed by genes (par. 0022-0025)(i.e. protein data, genomic, proteomic and research data specific to humans), as in claim 43.
Wohlgemuth et al. teach detecting transplant rejection by measuring proteins expressed by genes (par. 0022-0024) including by measuring patient serum (par. 0025); Wohlgemuth et al. also teach xenotransplant rejection (par. 0019) the combination of which would make obvious measuring in vitro patient outcome of xenotransplantation, as in claim 44.
Wohlgemuth et al. teach that the diagnostic oligonucleotide sets can include mutations of polymorphisms that are amenable to genotyping (par. 0043) and gene a gene product may include deletions, additions or substitutions (par. 0176), as in claims 45-46.
Pineda et al. teach sequencing variants mismatches in HLA genes (page 7, col. 1, par. 4) which impact the development of cell mediated rejection (CMR), as in claim 47.
Regarding claims 48 and 49 drawn to skin grafting and non-human donors which are porcine, porcine skin xenotransplantation is well known to those of ordinary skill in the art.
Claims 50-56 drawn to a system comprising a processor are made obvious by the teachings of a computer system in both Wohlgemuth et al. (par. 0209) and Pineda et al. (Abstract).
Claim 57 drawn to a computer readable medium is made obvious by a computer with software including instructions taught in Wohlgemuth et al. (par. 0209).
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