DETAILED ACTIONS
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07 November 2025 has been entered.
Status of the Claims
Claims 1-4, 7-9, 11-15, and 18-25 are pending and examined herein.
Claims 5-6, 10, and 16-17 are canceled.
Priority
As detailed on the 16 February 2022 filing receipt, the application claims priority as early as 28 March 2019. At this point in examination, all claims have been interpreted as being accorded this priority date as the effective filing date.
Information Disclosure Statement
An information disclosure statement (IDS) was submitted on 07 November 2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the references are being considered by the examiner.
Claim Objections
Claim 2 is objected to because of the following informality: an “and” is required between “device” and “based.”
Claim 8 is objected to because of the following informality: “being calculated” can be replaced with “are” similar to claim 9 to avoid implications of product-by-process. Claims 18 and 23 are similarly objected to.
Appropriate correction is required.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-4, 7-9, 11-15, and 18-25 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The instant rejection is newly stated and is necessitated by claim amendment.
Independent claim 1 recites “identifying, via the computing device executing a first machine learning model, cancerous CDR3s and non-cancerous CDR3s among the plurality of CDR3s” in its fourth element. The specification discloses the TRUST algorithm for identifying cancer-associated CDR3s (Fig. 3, Refs 302, 306, and 308) and adaptive boosting to use the amino acid indices from the cancer and non-cancer CDR3s to generate the classifiers (Fig. 3, Refs. 312, 314, and 316). There does not appear to be written description support for a machine learning algorithm identifying cancer- and non-cancer-associated CDR3s from the sequencing data and the TCR data. Rather, non-cancer CDR3 data is disclosed as experimentally determined (pg. 18, paragraph [94]). The adaptive boosting algorithm, which may be considered a machine learning algorithm, is clearly disclosed as used for classifying the CDR3 data (Fig. 3, Refs. 314 and 316) and is recited in the sixth element of the independent claim. Independent claims 12 and 19 are rejected on similar grounds, as are dependent claims 2-4, 7-9, 11, 13-15, 18, and 20-25.
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 1-4, 7-9, 11-15, and 18-25 are rejected under 35 USC § 101 because the claimed inventions are directed to an abstract idea without significantly more. "Claims directed to nothing more than abstract ideas (such as a mathematical formula or equation), natural phenomena, and laws of nature are not eligible for patent protection" (MPEP 2106.04 § I). Abstract ideas include mathematical concepts, and procedures for evaluating, analyzing or organizing information, which are a type of mental process (MPEP 2106.04(a)(2)). The claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than the abstract idea of diagnosing cancer based on peripheral blood T-cell receptor repertoire.
MPEP 2106 organizes JE analysis into Steps 1, 2A (Prong One & Prong Two), and 2B as analyzed below.
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter (MPEP 2106.03)?
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of
nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
Step 1: Are the claims directed to a 101 process, machine, manufacture, or composition of matter (MPEP 2106.03)?
The claims are directed to a method (claims 1-4, 7-9, 11) a non-transitory computer-readable medium (claims 12-15, 18, and 25), and a computing device (claims 19-24), and each of which falls within one of the categories of statutory subject matter. [Step 1: Yes]
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. MPEP § 2106.04(a)(2) further explains that abstract ideas are defined as:
• mathematical concepts (mathematical formulas or equations, mathematical relationships
and mathematical calculations) (MPEP 2106.04(a)(2)(I));
• certain methods of organizing human activity (fundamental economic principles or practices, managing personal behavior or relationships or interactions between people) (MPEP 2106.04(a)(2)(II)); and/or
• mental processes (concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions) (MPEP 2106.04(a)(2)(III)).
The independent claims recite the following steps:
identifying a set of sequencing data,
identifying T cell receptor data associated with a set of antigen specific TCRs,
identifying TCR CDR3 regions within the two data sets,
identifying, using a first machine learning model, cancerous and non-cancerous CDR3s,
defining features of the cancerous and non-cancerous CDR3s as amino acid indices,
training a second machine learning model using said indices,
grouping a set of TCR-seq sample data into clusters based on sequence similarity, and
determining, by the second machine learning model, a score based on cluster analysis.
Mathematical concepts recited in the independent claims include a first and second machine learning algorithm and determining a score based on the clustering. The terms in the claims are given their broadest reasonable interpretation in light of the specification. The specification discloses "the disclosed framework can utilize any known or to be known machine learning or artificial intelligence (AI) technique, algorithm or mechanism without departing from the scope of the initial disclosure" (pg. 12, paragraph [61]), "any known or to be known deep learning architecture or algorithm is applicable to the disclosed systems and methods discussed herein" (pg. 21, paragraph [108]), and executing a classifier or multiple classifiers by AdaBoost (pg. 13, paragraph [68]). The classifiers underlying AdaBoost are classification and regression trees (pg. 23, paragraph [119]). These classifiers are interpreted as mathematical constructs and thus, under a broadest reasonable interpretation, the machine learning models can be mathematical concepts. Furthermore, the cancer score is disclosed as a numerical determination of the score with confidence intervals (Fig. 5A), which indicates the score is not solely qualitative determination but rather based on mathematical calculations.
Mental processes or steps which the human mind is practically equipped to perform found in the independent claims are identifying RNA-seq data and data associated with a set of antigen-specific T cell receptors and determine a set of amino acid indices. These steps are interpreted as selecting data, identifying patterns (by comparing to amino acid index tables), and interpreting the output of a model, all of which the human mind is practically equipped to do.
Dependent claims 2-4, 7-10, 13-15, 18, and 20-23 recite further details of the mental processes and mathematical concepts found in the independent claims. Claims 2, 13, and 20 recite collecting data, identifying genomic information, analyzing genomic information, and extracting CDR3 sequences, which are interpreted as data analysis and thus mental processes. Claims 3, 14, and 21 recite performing alignment, which is interpreted as data analysis and thus a mental process. Claims 4, 15, and 22 recite generating a connectivity matrix, which is disclosed as grouping alignments with high scores (pg. 19, paragraph [94]), which is data evaluation and thus a mental process. Claims 7, 18, and 23 recite at least minimizing cross-validation errors, which is interpreted as a mathematical concept. Claim 8 recites calculating cross-validation errors, which is interpreted as a mathematical concept. Claim 9 recites further information about calculating the cross-validation errors.
Hence, the claims explicitly recite numerous elements that, individually and in combination,
constitute abstract ideas. The claims must therefore be examined further to determine whether they
integrate that abstract idea into a practical application (MPEP 2106.04(d)). [Step 2A: Yes]
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Multiple elements in addition the abstract ideas are recited in the instant claims. Claims 1-4 and 19-24 recite a "computing device." Claim 12-15 and 18 recite a non-transitory computer readable medium. Claims 12-15 and 19-22 recite a processor. Claims 2 and 13 recites a network. Claims 11 and 24-25 recite a deep neural network.
The claims comprising computer components do not describe any specific computational steps by which the computer performs or carries out the abstract idea, nor do they provide any details of how specific structures of the computer are used to implement these functions. The claims state nothing more than that a generic computer performs the functions that constitute the abstract idea. In particular, using the neural network as recited in claims 11 and 24-25 is describing a neural network at a high degree of generality, and so amounts to mere instructions to implement the abstract idea using a computer. MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Therefore, the limitations merely serve to link the judicial exception of determining a cancer score to the technological environment of a neural network.
None of the dependent claims recite any additional non-abstract elements; they are all directed
to further aspects of the information being analyzed, the manner in which that analysis is performed, or
the mathematical operations performed on the information. [Step 2A Prong Two: No]
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself. Step 2B of 101 analysis determines whether the claims contain additional elements that amount to an inventive concept, and an inventive concept cannot be furnished by an abstract idea itself (MPEP 2106.05).
Claims 1-4 and 19-24 recite a "computing device." Claim 12-15 and 18 recite a non-transitory computer readable medium. Claims 12-15 and 19-22 recite a processor. Claims 2 and 13 recites a network. Claims 11 and 24-25 recite a deep neural network.
The claims recite a general purpose computer, interpreted as instructions to apply the abstract idea using a computer including a broadly recited neural network, where the computer does not impose meaningful limitations on the judicial exceptions, which can be performed without the use of a computer (MPEP 2106.04(d) § I; and MPEP 2106.05(f)). The additional elements recites use of neural networks are interpreted as mere instruction to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f). Storing and retrieving data on a computer is a conventional computer activity (Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; MPEP 2106.05(d)(II)). Transmitting data over a network (Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; MPEP 2106.05(d)(II)) is a conventional computer activity. Repetitive calculations is a conventional computer activity (Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) (MPEP 2106.05(d)(II)).
Therefore, the recited additional elements, alone or in combination with the judicial exceptions, do not appear to provide an inventive concept. [Step 2B: No]
Conclusion: Claims are Directed to Non-statutory Subject Matter
For these reasons, the claims, when the limitations are considered individually and as a whole,
are directed to an abstract idea and lack an inventive concept. Hence, the claimed invention does not
constitute significantly more than the abstract idea, so the claims are rejected under 35 USC § 101 as
being directed to non-statutory subject matter.
Response to the 07 November 2025 Applicant Remarks
Applicant remarks assert the rejection under 35 USC 101 is improper and should be withdrawn. The stated arguments are unpersuasive.
The advisory action filed 31 October 2025 was objected to. The proposed amendments lacked support and were questionable under 35 USC 112(a) as described above. The following response to remarks is based on the remarks filed 07 November 2025, some of which is reiterative from said advisory action.
Applicant remarks state, regarding Step 2A Prong One of the 101 analysis, that the independent claims do not recite abstract ideas. As amended, independent claim 1, for instance, recites:
identifying a set of sequencing data,
identifying T cell receptor data associated with a set of antigen specific TCRs,
identifying TCR CDR3 regions within the two data sets,
identifying, using a first machine learning model, cancerous and non-cancerous CDR3s,
defining features of the cancerous and non-cancerous CDR3s as amino acid indices,
training a second machine learning model using said indices,
grouping a set of TCR-seq sample data into clusters based on sequence similarity, and
determining, by the second machine learning model, a score based on cluster analysis.
Applicant remarks state the amended claim 1 does not recite mathematical concepts (pg. 12), pointing to Example 39. The claims recite executing a first and second machine learning model. Under a broadest reasonable interpretation in light of the specification (MPEP 2111), these can include mathematical concepts. A model and algorithm are both interpreted as verbal descriptions of a mathematical concept; a mathematical relationship may be expressed in words and there is no particular word or set of words that indicates a claim recites a mathematical calculation (MPEP 2106.04(a)(2)). The specification discloses any form of machine learning or AI can be utilized to analyzed T cell, blood, and tumor sample/types (pg. 13, paragraph [69]). The machine learning algorithm is disclosed as executing a classifier or multiple classifiers by AdaBoost (pg. 13, paragraph [68]). The classifiers underlying AdaBoost are classification and regression trees (pg. 23, paragraph [119]). These classifiers are interpreted as mathematical constructs and thus, under a broadest reasonable interpretation, the machine learning models can be mathematical concepts. Determining the cancer score, while no longer reciting determining a probability, is interpreted as possibly encompassing a mathematical concept in light of the specification because the specification discloses a numerical determination of the score with confidence intervals (Fig. 5A), which indicates the score is not solely qualitative determination but rather based on mathematical calculations.
Mental processes, or steps which the human mind is practically equipped to perform, found in this claim include at least identification of the datasets, identifying TCR CDR3 regions, defining features, and making determinations based on model output. These steps are interpreted as selecting data, identifying patterns, and interpreting the output of a model, all of which the human mind is practically equipped to do. Applicant remarks state “the identifying and defining steps identified by the office encompass a first machine learning model (i.e., AI) in a way that cannot be practically performed in the human mind and therefore do not recite any mental processes” (pg. 12, first paragraph). The first three identifying steps are performed prior to the introduction of the machine learning model(s), so this argument cannot apply to these steps. The identifying cancerous and non-cancerous CDR3s and defining features is interpreted as using the mathematical concepts as discussed above. The models speed up the process but it is not clear that the mere incorporation of a machine learning model transforms the step from a mental process. Further regarding mental processes, applicant remarks state that the human mind cannot identify cancerous and non-cancerous sequences or determine a score for each cluster in a plurality of clusters (pg. 10, first paragraph). Making thousands of comparisons, while time consuming and repetitive, is not interpreted as beyond what is performable by the human mind. Similarly, determining a score is interpreted as data evaluation based on repetitive data analysis.
It is noted that the applicant remarks draw analogy of Example 39. Applicant remarks state that the previous analysis was improperly narrow (pg. 8, last paragraph) as a means of arguing the instant claims do not recite abstract ideas and similarity between adaptive boosting and a neural network. For the reasons explained above, the broadest reasonable interpretation in light of the specification for the use of the recited adaptive boosting is combining multiple classifier models which can be understood as mathematical concepts, which are distinct from the structure of a neural network. Furthermore, the data input in Example 39 is image data, which is different from the data recited in the instant claims. These fact patterns differ and so the fact specific situations are not similar.
It is also noted that even if the machine learning models were to be treated as elements in addition to the abstract ideas and not math, the claims do not recite structure of the models that would differentiate them from a general purpose computer. For instance, dependent claims 11 and 24-25 recite a neural network at a high level of generality and so, while elements in addition to the abstract ideas, they are general purpose computers which cannot provide an inventive concept.
At Step 2A Prong Two of the 101 analysis, applicant remarks state the analysis improperly isolates the additional elements (pg. 13, fourth paragraph). At Step 2A Prong Two, the elements in addition to the abstract ideas are evaluated to determine if they integrate the abstract ideas into a practical application. Thus, the additional elements are not evaluated in a vacuum but rather they are evaluated to determine if they provide a nexus to the real world with which a technical improvement or particular treatment, for instance, are realized. Here, at least the independent claims are interpreted as reciting abstract steps with or without a general purpose computer. Because the steps are interpreted as abstract, or abstract steps implemented by a general purpose computer, the additional elements do not integrate the abstract ideas into a practical application (MPEP 2016.05(f)).
Applicant remarks state the “normal” volume of blood of 3-10 ml enables bioinformatic detection of TCRs independent of tumor antigens (pg. 14, first paragraph). Collection of a blood sample of any volume is not a required limitation of the claims. The claims begin with identify sequencing data, where the collection of said data from a wet/physical sample is outside the metes and bounds of the claims. Therefore, Applicant’s remarks are not commensurate with the scope of the claims. Additionally, given the improvement is realized by an additional element, it is not clear how the improvement is realized. The claim begins with data and ends with data in the form of a cancer score which predicts a cancer status. An alleged improvement may be improvement to cancer diagnosis, but cancer diagnosis or prediction is neither a technical field nor is it a particular treatment.
At Step 2B, the elements in addition to the abstract ideas are evaluated for conventionality alone and in combination. MPEP 2106.05(d) sets forth that, at Step 2B, it is the additional elements which are examined to determine whether they are well-understood, routine, conventional activities previously known to the industry. The analysis at Step 2A, Prong 2, considers the claims as a whole, i.e., the additional elements in combination with the judicial exceptions (see MPEP 2106.05(a)), although the integration or improvement provided in the claim must flow from the additional elements and not the judicial exceptions to be considered persuasive. However, Step 2B requires examining only the additional elements, either alone or in combination with one another, for conventionality. The limitations pointed to be Applicant are considered to recite a judicial exception as described above and are therefore not considered at Step 2B. Applicant remarks state use of a machine learning algorithm to predict cancer status based on a patient’s peripheral blood TCR repertoire is not conventional (pg. 16, second paragraph). The using of a machine learning algorithm, interpreted as a mathematical concept, to determine a prediction, which is abstract, is not interpreted as having additional elements besides the computer it is implemented on, where using a computer to perform calculations is conventional. Storing and retrieving data on a computer is a conventional computer activity (Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; MPEP 2106.05(d)(II)). Transmitting data over a network (Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; MPEP 2106.05(d)(II)) is a conventional computer activity. Repetitive calculations is a conventional computer activity (Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) (MPEP 2106.05(d)(II)). Therefore, use a computer to perform the calculations does not provide significantly more than the abstract idea.
Thus, the rejection under 35 USC 101 is maintained.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Robert J Kallal whose telephone number is (571)272-6252. The examiner can normally be reached Monday through Friday 8 AM - 4 PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Olivia M. Wise can be reached at (571) 272-2249. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/R.J.K./Examiner, Art Unit 1685
/JANNA NICOLE SCHULTZHAUS/Examiner, Art Unit 1685