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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 11/14/2025 has been entered.
Claims 31-38, 42-54 are pending and under examination. Claims 39-41 have been canceled.
Claim Interpretation
The claims in this application are given their broadest reasonable interpretation (BRI) using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art.
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 31-38, 42-54 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of mental steps, mathematic concepts, organizing human activity, or a natural law without significantly more.
The claims have been significantly amended, and those amendments are reflected in the analysis below. Applicant’s arguments will be addressed at the end.
Applicant is directed to MPEP 2106 and the Federal Register notice (FR89, no 137 (7/17/2024) p 58128-58138) for the most current and complete guidelines in the analysis of patent- eligible subject matter. The current MPEP is the primary source for the USPTO’s patent eligibility guidance.
With respect to step (1): YES, the claims are drawn to statutory categories: computer systems, and computer-implemented methods.
With respect to step (2A) (1): YES, the claims recite an abstract idea, law of nature and/or natural phenomenon. The claims explicitly recite elements that, individually and in combination, constitute one or more judicial exceptions (JE).
Mathematic concepts, Mental Processes or Elements in Addition (EIA) in the claim(s) include:
31. A computer-implemented method for determining a molecular subtype classification of a test cancer specimen, the method comprising:
(EIA- preamble, setting forth a computer-implemented method, using a generically-described computer, and the goal of the method.)
training, via one or more processors, a machine learning model using training RNA gene expression data and molecular subtype labels for a plurality of pre-determined molecular subtypes from a training cohort of cancer specimens, the training comprising:
calculating, from the training RNA gene expression data, enrichment scores for a plurality of biological pathways,
identifying biological pathways of the plurality of biological pathways having a mean negative enrichment score in training specimens with positive molecular subtype labels for the plurality of pre-determined molecular subtypes and a mean positive enrichment score in training specimens with negative molecular subtype labels for the plurality of pre-determined molecular subtypes,
adjusting parameters of the machine learning model based on statistical correlations between the enrichment scores and the molecular subtype labels in the training cohort;
(Mathematic concept of applying training data to an unspecified machine learning model, based on statistical correlations. The enrichment score is a mathematic calculation (z-score): [0032] enrichment scores are determined by UMAP analysis. [0112] “the reference gene expression for a pathway is represented by one or more enrichment scores for the pathway, indicating an upregulation or downregulation of gene expression for the pathway. In this way, enrichment scores are associated with pre-determined molecular subtypes…” The structure of the ML model is not specified [0134]. Definitions of neural networks and other models for classification. The pathway scoring uses the accumulated z-scores that are positive or negative, a mathematic concept [0017-0022]. (MPEP 2106.04(a) section I))
receiving, via the one or more processors, RNA gene expression data of a plurality of nucleic acids associated with the test cancer specimen, wherein the RNA gene expression data of the test cancer specimen is normalized to one or both of read depth and gene length to provide a normalized expression profile for each gene;
(EIA- data gathering step of receiving test RNA gene expression data, that has previously been subjected to a mathematic normalization process.)
determining, via the one or more processors, a plurality of biological pathways from the normalized RNA gene expression data;
(Mental process, in a computing environment, of observation of the presence of data subsets correlating to pathway categories. MPEP 2106.04(a) section III)
filtering, via the one or more processors, the plurality of biological pathways to generate a reduced set of biological pathways by removing one or more of the biological pathways based on at least one heuristic-based filtering criterion;
(Mathematic concept of applying a filter criterion to a list as a cutoff or threshold, to select or remove subsets of data. [0010-0015, 0111] MPEP 2106.04(a) section I)
for each biological pathway in the reduced set of biological pathways:
identifying a gene set within the normalized RNA gene expression data, the gene set corresponding to the biological pathway, and
(Mental process, in a computing environment, of observing data meeting a pathway condition; MPEP 2106.04(a) section III).
calculating, via the one or more processors, an enrichment score for the gene set, wherein the enrichment score reflects expression of the gene set in comparison to a reference expression profile associated with the biological pathway;
(Mathematic concept of calculating a score, as above; MPEP 2106.04(a) section I)
for each biological pathway in the reduced set of biological pathways that was identified during training, flipping a sign of the enrichment score for that biological pathway;
(Mathematic concept of mathematically changing a score from positive to negative, or vice versa. [0020-0022, 0112] MPEP 2106.04(a) section I.)
determining, via one or more processors, a summary score for the reduced set of biological pathways by aggregating the enrichment scores of the identified gene sets;
(Mathematic concept of aggregating, adding, or combining score values; MPEP 2106.04(a) section I)
applying, via the one or more processors, the summary score to the trained machine learning model to output a probability distribution across the plurality of pre-determined molecular subtypes;
(Mathematic concept of applying the aggregated values to the trained CNN, to provide a result.)
determining, via the one or more processors from the probability distribution, the molecular subtype classification of the test cancer specimen;
(Mathematic concept, and mental process; of determining a classification, using the probability distribution, and making a judgement as to the appropriate classification. [0070-0080, 0112-0113, 0119] MPEP 2016.04(a) sections I and III.)
determining, via the one or more processors, at least one treatment therapy corresponding to the molecular subtype classification; and
(Mental process of observing the subtype classification, and making a judgement as to what an appropriate treatment therapy would be for that classification. [0071, 0086-0087, 0098, 0116, 0124] MPEP 2106.04(a) section III.)
generating, via the one or more processors, an electronic report indicating the molecular subtype classification and the at least one treatment therapy.
(EIA: routine output of an electronic report in any format, and the intended contents of the report.)
32. The computer-implemented method of claim 31, wherein the at least one heuristic-based filtering criterion is a pathway overlap filter.
(Mathematic concept modification, modifying aspects of the filter, or the criteria.)
33. The computer-implemented method of claim 32, wherein filtering the plurality of biological pathways comprises: filtering, via the one or more processors, the plurality of biological pathways to generate the reduced set of biological pathways by removing one or more of the biological pathways based on a percentage of genes in common.
(Mathematic concept modification, modifying aspects of the filter, or the criteria.)
34. The computer-implemented method of claim 31, wherein the at least one heuristic-based filtering criterion is a gene expression data filter.
(Mathematic concept modification, modifying aspects of the filter, or the criteria.)
35. The computer-implemented method of claim 31, wherein the at least one heuristic-based filtering criterion is a molecular subtype filter.
(Mathematic concept modification, modifying aspects of the filter, or the criteria.)
36. The computer-implemented method of claim 31, wherein the enrichment score for the gene set is a z-score.
(Mathematic concept modification, modifying the calculation.)
37. The computer-implemented method of claim 31, wherein the summary score is an average of the z-scores of the identified gene sets.
(Mathematic concept modification, modifying the calculation.)
38. The computer-implemented method of claim 37, further comprising: before determining the summary score, scaling one or more of the z-scores.
(Mathematic concept modification, modifying the calculation.)
42. The computer-implemented method of claim 31, further comprising: scaling the enrichment score of each identified gene set before determining the summary score.
(Mathematic concept modification, modifying the calculation.)
43. The computer-implemented method of claim 31, wherein the RNA gene expression data are RNA-seq gene expression data.
(EIA- defining the data gathered)
44. The computer-implemented method of claim 31, wherein the plurality of biological pathways are one or more of the Hallmark pathways.
(EIA/ mental process modification, specifying a type of pathway recognized in the prior art.)
45. The computer-implemented method of claim 31, wherein the plurality of biological pathways are one or more pathways related to estrogen signaling.
(EIA/ mental process modification, specifying a type of pathway recognized in the prior art.)
46. The computer-implemented method of claim 31, wherein the plurality of biological pathways are one or more of pathways downstream of human epidermal growth factor receptor 2 (HER2), downstream of RAS, or downstream of mTOR.
(EIA/ mental process modification, specifying a type of pathway recognized in the prior art.)
47. The computer-implemented method of claim 31, wherein the plurality of biological pathways are one or more immune-related pathways.
(EIA/ mental process modification, specifying a type of pathway recognized in the prior art.)
48. The computer-implemented method of claim 31, wherein the plurality of biological pathways are one or more immune-related Hallmark pathways.
(EIA/ mental process modification, specifying a type of pathway recognized in the prior art.)
49. The computer-implemented method of claim 31, wherein calculating the enrichment score for the gene set comprises: calculating, via the one or more processors using an UMAP analysis, the enrichment score for the gene set.
(Mathematic concept of calculating an enrichment score, and the data required.)
50. The computer-implemented method of claim 31, wherein the molecular subtype of the test cancer specimen is a HR+ subtype, a HR+/HER2+ subtype, a HR- /HER2+ subtype, or a HER2- subtype.
(EIA- data gathering, specifying the type of test sample for which the RNA gene expression data is obtained and provided.)
51. The computer-implemented method of claim 31, wherein the molecular subtype of the test cancer specimen is a triple negative subtype.
(EIA- data gathering, specifying the type of test sample for which the RNA gene expression data is obtained and provided.)
52. The computer-implemented method of claim 31, wherein the test cancer specimen is from a patient diagnosed with breast cancer.
(EIA- data gathering, specifying the source of the test sample for which the RNA gene expression data is obtained and provided.)
53. The computer-implemented method of claim 31, wherein the test cancer specimen is a breast cancer specimen.
(EIA- data gathering, specifying the type of test sample for which the RNA gene expression data is obtained and provided.)
54. A computing system comprising:
one or more processors; and
a memory, the memory having stored thereon computer-executable instructions, that when executed, cause the one or more processors to:
(EIA- general purpose computer, [0130-0135])
The remainer of the steps have the same analysis as for claim 31.
Natural law embraced by the claim(s):
The claims embrace the naturally occurring correlations between differences in naturally occurring genetic data (gene expression data) and naturally occurring phenotypes, such as cancer subtypes.
The gene expression information from the samples represents natural phenomena, and the relationship of that information to phenotypic traits (naturally occurring cancer subtypes) is simply put, a natural law. Nothing more than observation of this law is required. (See MPEP 2106.04(b), citing Genetic Techs, Mayo, Cleveland Clinic, Funk Bros, and Vanda.)
With respect to step 2A (2): NO, the claims do not integrate the JE into a practical application (MPEP 2106.04(d)). The claimed additional elements (EIA) are analyzed alone, or in combination to determine if the JE is integrated into a practical application (MPEP 2106.05(a-c, e, f and h)).
Claim(s) 31, 43-48, 50-53 recite the additional non-abstract element(s) (EIA) of data gathering.:
Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data needed to carry out the JE. The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception. (MPEP 2106.04/.05, citing Intellectual Ventures LLC v. Symantec Corp, McRO, TLI communications, OIP Techs. Inc. v. Amason.com Inc., Electric Power Group LLC v. Alstrom S.A.).
Claim(s) 31, 54 recite the additional non-abstract element (EIA) of a general-purpose computer system or parts thereof.
The EIA do not provide any details of how specific structures of the computer elements are used to implement the JE. The claims require nothing more than a general-purpose computer to perform the functions that constitute the judicial exceptions. The computer elements of the claims do not provide improvements to the functioning of the computer itself (as in DDR Holdings, LLC v. Hotels.com LP); they do not provide improvements to any other technology or technical field (as in Diamond v. Diehr); nor do they utilize a particular machine (as in Eibel Process Co. v. Minn. & Ont. Paper Co.). 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) 31-38, 42, 44-48, 49, 54 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.
In combination, the limitations of data gathering, for the purpose of carrying out the JE, using a general-purpose computer merely provide extra-solution activity, and fail to integrate the JE into a practical application.
With respect to step 2B: NO, the claims do not provide a specific inventive concept providing significantly more than the identified JE. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). The additional elements were considered individually and in combination to determine if they provide significantly more than the judicial exception. (MPEP 2106.05.A i-vi).
With respect to claim(s) 31, 54, 43-48, 50-53: 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.
Filippova, D. et al. (WO2019/232435 A1) provided abundance levels of target nucleic acids, and molecular labels for types and subtypes of cancer, as discussed at [0057]. The abundance levels of targeted genes in sets of samples each with a molecular label as to a type of cancer, with enrichment information [0065], are scored into a vector (B-score, M-score, k-dimensional score, et al.).
Arraya, C. et al. (US 2018/0365372 A1) provided [0052-0053] gene expression data, from RNA, from samples with molecular labels, are provided at [0055-0056, 0063]. The gene expression data is scored, according to [0059, 0073-0074] for biological pathways [Fig 1, see also 0106-0107].
Chittenden et al. (US 2020/0327962 A1) provided RNA gene sequence data [0031, 0039, 0056], as well as molecular labels from cohorts with different kinds of cancers [Fig 2, 0037, 0054].
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 (as in Alice Corp., CyberSource v. Retail Decisions, Parker v. Flook).
In the specification at [0089-0090] it is disclosed that the steps identified as data gathering can be met by obtaining RNA-seq datasets from [0089] “a multitude of different sources through the network… a network-accessible RNA-SEQ database (or dataset) …” [0090] “may include gene expression data and other data collected in clinical settings….” [0091] provides TCGA and NCI genomic Data Commons as additional publicly-available sources for RNA gene expression data.
These underscore the assertion that the gathering of the gene expression data and the molecular label data were routine, well understood and conventional in the art of bioinformatics and oncology, whether from test or reference samples.
Activities such as data gathering do not improve the functioning of the computer itself, or comprise an improvement to any other technical field. The limitations do not require or set forth a particular machine, they do not effect a transformation of matter, nor do they provide an unconventional step (citing McRO and Trading Technologies Int’l v. IBG). Data gathering steps constitute a general link to a technological environment. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp.,).
With respect to claims 31 and 54: 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 Filippova, Arraya, and Chittenden disclosed 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 recognizes that these computing elements are routine, well understood and conventional in the art.
The specification, at [0130-0135] disclosed the use of routine general-purpose computers for carrying out the invention, and/or the use of commercially available computer system elements.
This underscores the assertion that the computer-related elements were routine, well understood and conventional in bioinformatics and oncology.
These elements do not improve the functioning of the computer itself, or comprise an improvement to any other technical field (Trading Technologies Int’l v IBG, TLI Communications). They do not require or set forth a particular machine (Ultramercial v. Hulu, LLC., Alice Corp. Pty. Ltd v. CLS Bank Int’l), they do not effect a transformation of matter, nor do they provide an unconventional step. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp., CyberSource v. Retail Decisions, Parker v. Flook, Versata Development Group v. SAP America).
Dependent claim(s) 31-38, 42, 44-49, 54 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 generic computer or generic 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.
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.
Applicant’s arguments:
Applicant’s arguments have been carefully considered, but are not completely persuasive.
The examiner recognizes the attempt to model the claims to liken them to certain recent decisions provided relevant to 35 USC 101. The inclusion of more details re: the biological pathways in the training of the ML is noted. The changes in the positive or negative value after the initial training is noted. The new steps related to selecting treatment, and an electronic report are noted. However, the claims lack the particular features used in Desjardins, which led to the finding of patent eligibility. Desjardins had claims drawn to the use of two separate neural networks, which interact with one another in a particular way, with a particular data structure. In Desjardins, the retraining of the particular neural networks changed the structure of that network in a way that provided an improvement.
The previous iteration of the claims indicated a convolutional neural network, however this has been broadened to “a machine learning model.” This change eliminated any possible structure inherent in a convolutional neural network, and replaced it with ML of any form or structure: essentially a recitation without any structure. It is not clearly set forth in the claim how the “retraining” or flipping the signs leads to an improved determination of a molecular subtype of a test cancer specimen by the ML. The newly added steps (after applying the summary score to the ML) of determining the subtype, the treatment therapy, and the report are all generically described, and fail to set forth how each element is to be determined from the data at hand from the preceding steps.
As amended, the claims fail to integrate the JE into a practical application, and fail to provide an inventive concept. The alleged improvement achieved by the claimed method is provided by the calculations carried out by the generic ML. According to the guidance set forth in MPEP 2106, this is an improvement to the judicial exception itself, and is not reflected back into a specific technological environment or practically applied process.
An improvement in the judicial exception itself is not an improvement in the technology. For example, in In re Board of Trustees of Leland Stanford Junior University, 989 F.3d 1367, 1370, 1373 (Fed. Cir. 2021) (Stanford I), Applicant argued that the claimed process was an improvement over prior processes because it ‘‘yields a greater number of haplotype phase predictions,’’ but the Court found it was not ‘‘an improved technological process’’ and instead was an improved ‘‘mathematical process.’’ The court explained that such claims were directed to an abstract idea because they describe ‘‘mathematically calculating alleles’ haplotype phase,’’ like the ‘‘mathematical algorithms for performing calculations’’ in prior cases. Notably, the Federal Circuit found that the claims did not reflect an improvement to a technological process, which would render the claims eligible (FR89 no.137, p58137, 7/17/2024).
Here, Applicant has provided an improved mathematical process of calculation of certain enrichment scores, summary scores and their ML directed and calculated associations with subtypes of cancer.
The improvement in identification of a molecular subtype of cancer (carried out by the judicial exception) does not provide an improvement in the technology of receiving exogenous data. The collection of exogenous data is carried out, unchanged, whether or not the judicial exception is applied. (Cleveland Clinic Foundation: using well-known or standard laboratory techniques is not sufficient to show an improvement (MPEP2106.05(a)).
The improvement in the identification of a molecular subtype of cancer (achieved by the judicial exception) does not require a non-conventional interaction with a specific element of a computer as was required in Enfish. The disputed claims in Enfish were patent-eligible because they were "directed to a specific improvement to the way computers operate, embodied in [a] self-referential table." Enfish, 822 F.3d at 1336. The court found that the "plain focus of the claims" there was on an improvement to computer functionality itself-a self-referential table for a computer database, designed to improve the way a computer carries out its basic functions of storing and retrieving data- not on a task for which a computer is used in its ordinary capacity. Id. at 1335-36. The court noted that the specification identified additional benefits conferred by the self-referential table (e.g., increased flexibility, faster search times, and smaller memory requirements), which further supported the court's conclusion that the claims were directed to an improvement of an existing technology. Id. at 1337 (citation omitted).
The improvement in the identification of a molecular subtype of cancer (carried out by the judicial exception) does not improve the functionality of the computer itself as in Finjan, Visual Memory, or SRI Int’l. In Finjan, claims to virus scanning were found to be an improvement in computer technology. In Visual Memory, claims to an enhanced computer memory system were found to be directed to an improvement in computer capabilities. In SRI Int'l, claims to detecting suspicious activity by using network monitors and analyzing network packets were found to be an improvement in computer network technology.
The process of identifying a molecular subtype of cancer using mathematical scoring, filtering and ML is not a technological process; it is information evaluation.
As set forth previously, the claims can be distinguished from eligibility decisions provided in examples 47-49.
These arguments, including references to Examples 47-48, have been carefully considered.
In Example 47, the difference between Claim 2 not being patent-eligible, and claim 3 being patent-eligible is related to the analysis under Step 2A-2, whether a JE is integrated into a practical application, by taking action with the results of the neural network analysis.
In claim 2 of example 47, the claim does not provide details about how the trained ANN operates, or the detection, or the analysis of the data in later elements is performed. In the example the “trained ANN” provides nothing more than mere instructions to implement an abstract idea on a generic computer (MPEP 2106.05(f)).
The limitations “to detect” and “to analyze” describe desired outcomes without setting forth how to actually achieve those outcomes.
Claim 3 of example 47, takes the trained ANN, (trained in a prior step) and the EIA provide the integration into a practical application, as they “provide specific computer solutions that use the output from the ANN to provide security solutions to the detected anomalies, and take action to mitigate the anomalies using specific steps (i.e. dropping network packets and blocking future traffic).”
These steps take the form of positive active method steps which reflect the improvement to technology.
In Example 48, similar distinctions can be made.
In claim 1 of example 48, there are no details about a particular DNN or how the DNN operates to derive the embedding vectors. The DNN is used to generally apply the JE without placing any limitation on how the DNN operates to derive the embedding vectors as a function of the input signal. The claim omits any details as to how the DNN solves a technical problem, and instead recites only the idea of a solution or outcome. Also, the claim invokes a generic DNN merely as a tool for making the recited mathematical calculation rather than purporting to improve the technology or a
computer. See MPEP 2106.05(f). Therefore, the limitation represents no more than mere instructions to apply the judicial exception on a computer.
In claim 2 of example 48, the analysis of the DNN in step a) remains the same as it was for claim 1: equivalent to the words “apply it” using a computer. The difference comes in the analysis of the other limitations in addition to the abstract ideas. The EIA were analyzed, and the EIA reflect the improvement by reciting details of how the DNN aids in the cluster assignments to correspond to the sources identified in the mixed speech signal, then take actions to synthesize and convert mixed audio signals. Converting clusters into separate speech waveforms and generating a mixed speech signal from the separate speech waveforms in steps (f) and (g) of claim 2 are not an idea of an outcome.
Claim 3 of example 48, “using a DNN” trained on source separation to carry out certain actions was found not to provide details about a particular DNN, it is used generally, covering every mode of implementing the JE using DNN. It does not provide details about how the DNN operates to derive the embedding vectors. Again, the analysis changes with the consideration of the additional elements. The DNN alone isn’t enough to integrate the JE into a practical application, but considering the EIA in step f) of claim 3, the claim reflects the improvement by reciting details of how the DNN trained on source separation aids in the cluster assignments to correspond to the sources identified in the mixed speech signal (etc.); then takes action to convert the mixed signal to separate signals, to generate a sequence of words, making individual transcription of each separated signal possible.
In pending claims 31/ 54, in contrast, the ML does not have structural information as to its architecture, and how that architecture is affected by the training, or the application of the test data. No action that reflects the improvement is taken with the output of the ML (the molecular subtype of the test cancer specimen).
MPEP 2106.04: “A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception.” The terms “apply, rely on or use” refers to taking a specific positive action.
With respect to the Examples and step 2B, and the search for an inventive concept, the elements in addition to the JE are considered individually, in combination, and as a whole.
Example 47, claim 2 discusses step 2B and found “that the recitations of “(a) receiving continuous training data” and “(g) outputting the anomaly data from the trained ANN” are recited at a high level of generality. These elements amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. Additionally, the recitation of a computer to perform limitations (a), (b), and (c) amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.”
Example 48, claim 1 discusses step 2B and found “Here, the step of receiving a mixed speech signal is mere data gathering that is recited at a high level of generality, and as discussed in the disclosure, is well-understood (e.g., the first paragraph of the background explains that smartphones and other devices have long been equipped to receive a mixed speech signal via the microphones integrated into the devices). Therefore, this limitation remains insignificant extra-solution activity even upon reconsideration and does not amount to significantly more. Even when considered in combination, these additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, and therefore do not provide an inventive concept.”
Example 49, claim 1 discusses step 2B and found “The data gathering activities in limitation (a) are recited at a high level of generality and have been recognized by the courts as being routine laboratory techniques. See Genetic Techs. v. Merial LLC, 818 F.3d 1369, 1377 (Fed. Cir. 2016) (analyzing DNA to provide sequence information or to detect allelic variants is conventional in the art); MPEP 2106.05(d), subsection II. The specification otherwise only describes carrying out sample collection and genotyping by conventional methods. See paragraph 4 of the Background. Consequently, for the reasons discussed above, the additional elements individually or in combination with the judicial exception do not provide an inventive concept; so, the claim as a whole does not amount to significantly more than a generic instruction to “apply” the judicial exception.”
Here, in claims 31 and 54, the data gathering elements are recited at a high level of generality, and are also routine laboratory techniques. In these claims the computing elements are described at a high level of generality, and the system works in the way it was designed: to receive, analyze, and store data. Additionally, prior art references were provided that obtained the same data for the same general purposes, and used generally-described computer systems.
Further, with respect to the arguments regarding the alleged improvement, it is unclear that the independent claims recite all the necessary and sufficient steps required to achieve that improvement. MPEP 2106.05(a): “An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102- 03; DDR Holdings, 773F.3d at 1259, 113 USPQ2d at 1107.”
The MPEP sets forth that “if the examiner concludes the disclosed invention does not improve technology, the burden shifts to applicant to provide persuasive arguments supported by any necessary evidence to demonstrate that one of ordinary skill in the art would understand that the disclosed invention improves technology. Any such evidence submitted under 37 CFR 1.132 must establish what the specification would convey to one of ordinary skill in the art and cannot be used to supplement the specification.” Applicant’s arguments cannot take the place of evidence.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARY K ZEMAN whose telephone number is 5712720723. The examiner can normally be reached on 8am-2pm M-F. Email may be sent to mary.zeman@uspto.gov if the appropriate permissions have been filed.
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/MARY K ZEMAN/ Primary Examiner, Art Unit 1686