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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 Ju has been entered.
Response to Amendment
The amendment filed July 21, 2026 has been entered. Claims 29, 32, 33, 36, and 71-78 are pending, all previous and intervening claims cancelled. Examiner acknowledges Applicant’s cancellation of claims 81-85 in the latest set of amended claims.
Under further search and consideration, new grounds of rejection under 35 U.S.C. 112(b) are presented.
Applicant’s arguments are not persuasive to overcome the rejection of claims under 35 U.S.C. 101.
Response to Arguments
Applicant's arguments filed July 21, 2026 have been fully considered but they are not persuasive.
Regarding Applicant’s argument:
“Applicant respectfully submits that claim 29 is directed to patent-eligible subject matter at least because when the claim is taken as a whole, the specific steps of the algorithm update the training model and provides technical improvements over previous methods of prediction and detection of deep tissue injuries (Step 2A Prong 2). Furthermore, the Examiner has not provided evidence showing that the claimed subject matter is well-understood, routine and conventional (Step 2B).”
The improvement which Applicant alleges (e.g., updating a training model”) is merely a step of re-evaluation (i.e., carrying out further implicit evaluation steps of a “training model”), which entails repeating abstract processes based on previous analysis steps. See MPEP 2106.04:
“Because a judicial exception is not eligible subject matter, Bilski, 561 U.S. at 601, 95 USPQ2d at 1005-06 (quoting Chakrabarty, 447 U.S. at 309, 206 USPQ at 197 (1980)), if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application… (eligibility “cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself.”). For a claim reciting a judicial exception to be eligible, the additional elements (if any) in the claim must “transform the nature of the claim” into a patent-eligible application of the judicial exception, Alice Corp., 573 U.S. at 217, 110 USPQ2d at 1981, either at Prong Two or in Step 2B. If there are no additional elements in the claim, then it cannot be eligible.”
Applicant’s present Remarks do not clearly demonstrate an improvement beyond what is claimed in the judicial exception.
Additionally, Applicant addresses that the evaluation steps of the claimed abstract process are not well-understood, routine, or conventional; however, the conventionality of abstract evaluation does not alter the patent eligibility of the judicial exception. See Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 714-15, 112 USPQ2d 1750, 1753-54 (Fed. Cir. 2014). Cf. Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) (“a new abstract idea is still an abstract idea”) (emphasis in original).
Regarding Applicant’s argument:
“Similar to Desjardins and Enfish, the present claims are directed to methods that improve detection and prediction of deep tissue injuries. Claim 29 recites a method of utilizing a trained model, wherein the trained model is automatically updated with a plurality of optimal weight values that are calculated based on a calculated true positive rate (TPR) and a false positive rate (FPR). This is an improvement over the state of the art that relies on visual assessments to detect and predict pressure injuries… Further, Applicant respectfully submits that the Examiner evaluates the claims at an impermissibly high level of generality… In Desjardins… The ARP disagreed with the PTAB's new ground of rejection under 35 USC § 101, finding that many adequately described and nonobvious Al innovations are rejected as being potentially patent ineligible only "because the panel essentially equated any machine learning with an unpatentable 'algorithm' and the remaining additional elements as 'generic computer components,' without adequate explanation." Desjardins at p. 9… " Like the PTAB in Desjardins, the Examiner here ignores the specific methodology and steps in the algorithm recited in the claims, and impermissibly reduced the specific steps of the optimization algorithm to a generic mental process.”
Regarding Applicant’s arguments with respect to Desjardins, Desjardins directly addresses a known technical problem in a subset of machine learning algorithms, i.e., “catastrophic forgetting” in continual learning systems; thus, Desjardins alters the performance for a class of algorithms. In contrast, Applicant alleges improvement of “detection and prediction of deep tissue injuries,” whereby such an alleged improvement is furnished primarily by abstract evaluation steps. Applicant fails to identify a known technical problem of an algorithmic class which the claimed invention intends to solve; rather, Applicant’s invention appears to be directed to a series of evaluation steps using variations on known techniques of machine learning model evaluation without further explanation as to how such variations improve the performance of the model beyond techniques which are commonplace. Accordingly, for at least these reasons, Desjardins has little to no applicability to the claimed invention.
Regarding Applicant’s arguments with respect to Enfish, Examiner notes that in Enfish, the court analyzed whether the focus of the claims was on the specific asserted improvement in computer capabilities, or instead, on a process that qualifies as an “abstract idea” for which computers are invoked merely as a tool. The claims analyzed in Enfish focused on a specific type of data structure designed to improve the way a computer stores and retrieves data in memory. On the other hand, the plain focus of Applicant’s claimed invention is on executing evaluation steps for which a computer is used in its ordinary capacity in order to execute evaluation of specific medical data. In contrast, the claims analyzed in Enfish recited a specific type of data structure designed to improve the way a computer stores and retrieves data in memory. Thus, the claims of the present application are not like Enfish because they do not recite a specific type of data structure or improve the computer functionality itself. Accordingly, for at least these reasons, Enfish has little to no applicability to the claimed invention.
Examiner further notes that the concept of utilizing true and false positive rates to evaluate the performance of a machine learning model is a common technique, known from at least:
Gandenberger, Greg. Blog - Evaluating Classification Models, Part 1: Weighing False Positives against False Negatives, gandenberger.org/posts/2019-11-14_evaluating-classifier-pt1/evaluating_classifiers_pt_1.html. Accessed 27 July 2026.
Katz, Erez. Is Your Machine Learning Model That Good?, https://www.linkedin.com/pulse/your-machine-learning-model-good-erez-katz?tl=en . Accessed 27 July 2026.
Examiner further notes that the above-recited concept is also generally known from machine learning concepts such as threshold tuning involving ROC curve analysis, and, relatedly, Youden’s J statistic.
Should Applicant argue that the claimed process steps provide a substantive improvement to the evaluation method/algorithm itself, Examiner requests clarification as to how particular claimed evaluation steps provide the alleged improvement over known techniques [emphasis added]. For instance, Applicant’s step of a set of randomly ascending numbers between 0 and 2 is not provided with a prior art rejection; however, the concept of weight randomization is a generally known technique (i.e., weight initialization) used to break symmetry among neurons of a neural network to learn the different features during training. Applicant is invited to provide a technical explanation as to how such a step may provide a novel benefit over known concepts from training machine learning models.
Regarding Applicant’s argument:
“The Examiner asserts that the "Applicant's Specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims." Office Action at p. 7. Applicant respectfully disagrees with the Examiner. A person of ordinary skill in the art would understand that at the time of the priority date of this application, skin and tissue assessment (STA), the standard technique used in clinical practice for detecting any pressure injuries, achieved only a sensitivity of 50.6% and a specificity of 60.13%. See Pancorbo-Hidalgo PL et al., J. Adv Nurs., 54(1):94:110 (2006). On the other hand, a person of ordinary skill in the art reading the instant application would have recognized that the unconventional methods for improving the detection of deep tissue injuries with an accuracy of 79% (true positive rate = 90% and false positive rate = 21%) represent an improvement over the prior art. See Specification at Examples 2, 3, 5, and FIG. 8. A person of ordinary skill in the art would find these results to be impressive considering that deep tissue injuries are a subset of pressure injuries that are more difficult to detect than others.”
Applicant is reminded that abstract ideas cannot provide a practical application or significantly more (e.g., an improvement). Both Step 2A Prong 2 and Step 2B require an additional element, not an abstract idea, to provide a practical application or significantly more (e.g., an improvement). The only additional element appears to be a machine learning model, i.e., evaluation steps carried out via a generic processor (which, according to Applicant’s Specification, is well understood routine conventional activity); see rejection below.
Additionally, Examiner notes that the results which Applicant presents “an accuracy of 79% (true positive rate = 90% and false positive rate = 21%)” results from a narrower implementation of a machine learning model than what is claimed. The most detail in Applicant’s Specification regarding a trained model is in Paragraphs 0061, 0071, which describe minor details regarding the basic architecture of neural networks or types of learning algorithms:
“In an aspect, the neural network is a single-layer neural network. In an aspect, the neural network is a multi-layer neural network. In an aspect, the neural network comprises at least one hidden layer, at least two hidden layers, at least three hidden layers, at least four hidden layers, or at least five hidden layers. In an aspect, the neural network uses a supervised learning algorithm. In an aspect, the neural network uses an unsupervised learning algorithm.”
Thus, Applicant’s Specification is absent of a specific configuration or choice of a trained model which yields “an accuracy of 79% (true positive rate = 90% and false positive rate = 21%).” For instance, one of ordinary skill in the art would recognize that 1) changing the number of layers in a neural network would alter its performance, and, similarly, that 2) supervised and unsupervised learning algorithms also differ in performance. Thus, Applicant’s Specification supports a wide scope of possible configurations for a machine learning model, but insufficient support for a model which particularly provides “an accuracy of 79% (true positive rate = 90% and false positive rate = 21%).”
Regarding Applicant’s argument:
“As to Step 2B of the Alice/Mayo analysis, the Examiner alleges that none of the claims
include additional elements that, when viewed as a whole, are sufficient to amount to significantly more than an abstract idea. For example, the Examiner construes a "processor," a "neural network," or a "trained model" as generic computing devices or known algorithms. Id. at pg. 9. The Examiner further alleges that "it is clear from the claims themselves and the specification that these limitations require no improved computer resources and merely utilize already available computers with their already available basic functions to use as tools in executing the claimed process (intake of specific data to evaluate via a trained model, and further evaluations involving training the model." Id. at p. 10. The Examiner also notes that "an improvement to a technology cannot come from elements determined to be part of the abstract idea, and must instead come from identified additional elements to the abstract idea." Id. at p. 10. Applicant respectfully disagrees.
The Examiner is required to "expressly support[]" an assertion that a combination of elements is well-understood, routine, or conventional with one or more evidence outlined in M.P.E.P. § 2106.07(a)(III)(A)-(D). Applicant respectfully submits that the Examiner has not identified any art, admission, or evidence showing that the specific ordered combination of steps, e.g., bounded random weight generation, iterative input of training data and weights into a trained
model, comparison of predicted-versus-known status, TPR/FPR computation from that comparison, and iterative repetition to identify an optimal weight set, was well-understood, routine, or conventional as an ordered combination at the time of filing. Without providing the requisite evidence, Applicant respectfully submits that the Examiner's rejection under Step 2B cannot be sustained..”
Applicant appears to address a series of abstract process steps as a combination of elements which are not well-understood, routine, or conventional. See previous arguments regarding the novelty of an abstract idea. See Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 714-15, 112 USPQ2d 1750, 1753-54 (Fed. Cir. 2014). Cf. Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) (“a new abstract idea is still an abstract idea”) (emphasis in original).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 29, 33, 72, 74, and respective dependent claims thereof are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Re. Claim 29: Claim 29 recites “outputting a prediction of the likelihood of the patient developing DTI based on the probability value.” The limitation “the likelihood” is not provided with clear antecedent basis.
Re. Claim 33: Claim 33 possesses multiple issues of indefiniteness.
Claim 33 recites “the constraint of TPR>>FPR.” It is unclear if the “>>” character is meant to signify that a value of TPR is “much greater than” FPR, or if it is a typographical error, whereby “>” would be used instead. In the case that “>>” is used to signify “much greater than,” it is unclear what the relative degree would be for “much greater than” as opposed to a value being “greater than” another.
Claim 33 recites “the constraint,” which is not provided with clear antecedent basis.
Claim 33 recites “the objective function,” which is not provided with clear antecedent basis.
Claim 33 is dependent upon cancelled claim 31.
Re. Claim 72: Claim 72 possesses multiple issues of indefiniteness.
Claim 72 recites “up to the formation of the DTI.” The limitation “the formation” is not provided with clear antecedent basis.
Claim 72 recites “performing the steps.” The limitation “the steps” is not provided with clear antecedent basis.
Re. Claim 74: Claim 72 recites “the N least-recent weighted SEM delta values,” which is not provided with clear antecedent basis.
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 29, 32, 33, 36, and 71-78 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. Each claim has been analyzed to determine whether it is directed to any judicial exceptions.
Step 2A, Prong 1
Each of the claims recites steps or instructions for ascertaining and processing data to measure a blood pressure of a mammal subject, which is grouped as a mental process. Accordingly, each of the claims recites an abstract idea.
Independent claim 29 recites:
receiving a plurality of sub-epidermal moisture (SEM) delta values associated with the patient (data gathering, additional element);
automatically inputting, via a processor, the plurality of SEM delta values into a trained model configured to calculate a probability value of the patient developing DTI, wherein the trained model is trained by performing the steps comprising:
receiving a set of training data comprising:
a plurality of SEM delta values associated with a set of patients, wherein each patient in the set of patients has a known DTI status, and a threshold value, wherein the threshold value is a number between 0 and 1;
automatically inputting the training data into an optimization algorithm to receive a plurality of optimal weight values; and automatically updating the trained model with the plurality of optimal weight values, wherein the optimization algorithm is configured to:
(a) generate a plurality of ascending random numbers between 0 and 2 as a plurality of weight values;
(b) input the training data and the plurality of weight values into the trained model to receive a set of predicted DTI statuses associated with the set of patients;
(c) compare the predicted DTI statuses with the known DTI statuses associated with the set of patients;
(d) calculate a true positive rate (TPR) and a false positive rate (FPR) based on the comparison, wherein the TPR is calculated as percentage of patients in the set of patients whose predicted DTI status matches their known DTI status, and the FPR is calculated as percentage of patients in the set of patients whose predicted DTI status does not match their known DTI status;
repeat steps (a) to (d) for a predetermined number of iterations to obtain a plurality of TPRs and FPRs;
identify an optimal TPR and FPR from the iterations; and output the optimal plurality of weight values associated with the optimal TPR and FPR; (data gathering or extra-solution activity, additional element and/or evaluation and/or mathematical concept),
outputting a prediction of the likelihood of the patient developing DTI based on the probability value (extra-solution activity).
Independent claim 36 recites analogous limitations in a broader method claim; thus, the analysis of claim 29 applies mutatis mutandis to claim 36.
As indicated above, the independent claim recites at least one step or instruction grouped as a mental process. Therefore, each of the independent claims recites an abstract idea. Each limitation, aside from language reciting a generic computer components, can be grouped as a mental process (see italicized portions above), and is addressed as follows:
Thus, the limitation of the trained model is configured to… [perform claimed evaluation steps] encompasses an individual performing evaluation on gathered input data (SEM delta values) to arrive at an output (a probability value/likelihood), whereby such action is performable mentally or by pen-and-paper practice. The trained model being trained merely entails performing evaluations of the model on a gathered data set to arrive at a model with adjusted parameters. No limitations are provided that would force the complexity of any of the identified evaluation steps to be precluded from being performed by at least pen-and-paper practice.
Alternatively or additionally, these steps describe the concept of using implicit mathematical formula(s) (i.e., evaluation and training of a model) to derive a conclusion based on input of medical data, which corresponds to concepts identified as abstract ideas by the courts, such as in Diamond v. Diehr. 450 U.S. 175, 209 U.S.P.Q. 1 (1981), Parker v. Flook. 437 U.S. 584, 19 U.S.P.Q. 193 (1978), and In re Grams. 888 F.2d 835, 12 U.S.P.Q.2d 1824 (Fed. Cir. 1989). The concept of the recited steps above is not meaningfully different than those mathematical concepts found by the courts to be abstract ideas.
Thus, these concepts are similar to court decisions of abstract ideas of itself: collecting, displaying, and manipulating data (Int. Ventures v. Cap One Financial), collecting information, analyzing it, and displaying certain results of the collection and analysis (Electric Power Group), collection, storage, and recognition of data (Smart Systems Innovations).
The dependent claims merely include limitations that either further define the abstract idea (e.g. limitations relating to the data gathered or particular steps which are entirely embodied in the mental process) or details of the data gathered. Thus, each dependent claim amounts to no more than generally linking the use of the abstract idea (a trained model) to a particular technological environment or field of use (deep tissue injury analysis) because they are merely extra-solution activity or incidental or token additions to the claims that do not alter or affect how the process steps are performed.
More specifically:
Claims 32, 33, 71-78 further describe details of gathered data and further evaluation steps required by an optimization algorithm, only serving to further define the abstract idea of the mental process and/or mathematical concept identified in the independent claims.
Thus, the claims are each directed to an abstract idea.
Step 2A, Prong 2
The above-identified abstract idea is not integrated into a practical application because the additional elements, either alone or in combination, generally link the use of the above-identified abstract idea to a particular technological environment or field of use.
More specifically:
Independent claim 29 recites the additional elements of:
a processor;
Independent claim 36 does not recite an input device or processor.
Even should an input device be understood to possess a particular structure (e.g., an SEM measurement device), its use in the mental process as presently claimed would amount to mere extra-solution activity. See MPEP 2106.05(b).III:
“Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not integrate a judicial exception or provide significantly more. See Bilski, 561 U.S. at 610, 95 USPQ2d at 1009 (citing Parker v. Flook, 437 U.S. 584, 590, 198 USPQ 193, 197 (1978)), and CyberSource v. Retail Decisions, 654 F.3d 1366, 1370, 99 USPQ2d 1690 (Fed. Cir. 2011) (citations omitted)”
The processor is recited at a high-level of generality (i.e., as a generic processors and memory performing a generic computer function of performing calculations and storing data, respectively) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
The evaluations of a trained model are carried out via a processor; thus, the trained model may also be regarded as evaluation steps or mathematical concepts carried out via a generically-recited computer component.
Thus, such additional elements do not serve to apply the above-identified abstract idea with, or by use of, a particular machine, effect a transformation or apply or use the above-identified abstract idea in some other meaningful way beyond generally linking the use thereof to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Furthermore, the above-identified generically recited elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. For at least these reasons, the abstract idea is not integrated into a practical application.
Moreover, the above-identified abstract idea is not integrated into a practical application under because the claimed invention merely implements the above-identified abstract idea using rules (e.g., computer instructions) executed by a computer (e.g., processor as claimed). In other words, these claims are merely directed to an abstract idea with additional generic computer elements which do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. Additionally, Applicant’s Specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. That is, like Affinity Labs of Tex. v. DirecTV, LLC, the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution. Thus, for these additional reasons, the abstract ideas identified above in the independent claims (and their respective dependent claims) are not integrated into a practical application.
Claims 32, 33, and 71-78 further describe details of gathered data and further evaluation steps required by an optimization algorithm, and thus do not contain additional elements which integrate the abstract ideas into a practical application.
Accordingly, the claims are each directed to an abstract idea.
Step 2B
None of the claims include additional elements that, when viewed as a whole, are sufficient to amount to significantly more than the abstract idea for at least the following reasons:
Independent claim 29 recites the additional elements of:
a processor;
Independent claim 36 does not recite an input device or processor and is treated as encompassed by the analysis below.
The processor is described within a system which “can comprise any suitable type of microprocessor-based device, such as a personal computer, workstation, server or handheld computing device (portable electronic device) such as a phone or tablet (Paragraph 0098).
Similarly, the trained model is interpreted as embodying process steps carried out via a generic computer. The term “neural network” is defined at Paragraph 0050 in an elementary sense such that the skilled artisan would recognize that Applicant is referring to conventionally-known neural network algorithms. Thus, the term “neural network” and its accompanying training details are what provides the closest support for the claim term “trained model” (notwithstanding the rejection under 35 U.S.C. 112(a)). While details regarding how a neural network is trained are provided throughout the specification, the most detail regarding the special programming of the neural network itself is not provided in the specification. At best, Paragraphs 0061 and 0071 state minor details regarding basic architecture of a neural network:
“In an aspect, the neural network is a single-layer neural network. In an aspect, the neural network is a multi-layer neural network. In an aspect, the neural network comprises at least one hidden layer, at least two hidden layers, at least three hidden layers, at least four hidden layers, or at least five hidden layers. In an aspect, the neural network uses a supervised learning algorithm. In an aspect, the neural network uses an unsupervised learning algorithm.”
This is a generalized description of known elements of a neural network, and does not constitute special programming or algorithms.
Accordingly, in light of Applicant’s specification, the processor and the trained model are reasonably construed as a generic computing device and known algorithms embodied thereon. Generic computer-based components do not integrate an abstract idea into a practical application. See DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (Fed. Cir. 2014) (“[A]fter Alice, there can remain no doubt: recitation of generic computer limitations does not make an otherwise ineligible claim patent eligible.”); see also MPEP § 2106.05(f). Accordingly, the claim 1 limitation of “the trained model is configured to calculate a probability value corresponding to the likelihood of the patient developing DTI” amounts to “[s]tating an abstract idea while adding the words ‘apply it with a computer,”’ which does not confer patent eligibility to the abstract idea. Alice, 573 U.S. at 223; Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437, slip op. at 18 (Fed. Cir. Apr. 18, 2025) (“[P]atents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.”).
Like SAP America vs Investpic, LLC (Federal Circuit 2018), it is clear from the claims themselves and the specification that these limitations require no improved computer resources and merely utilize already available computers with their already available basic functions to use as tools in executing the claimed process (intake of specific data to evaluate via a trained model, and further evaluations involving training the model).
Each other dependent claim merely recites steps which further define the abstract idea and data/data-processing steps as previously stated in prior analysis steps. Examiner notes that the dependent claims recite limitations which are extra-solution or part of the abstract idea itself do not constitute significantly more.
Examiner further notes that an improvement to a technology cannot come from elements determined to be part of the abstract idea, and must instead come from identified additional elements to the abstract idea.
See MPEP 2106.05(a):
It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018)). Thus, it is important for examiners to analyze the claim as a whole when determining whether the claim provides an improvement to the functioning of computers or an improvement to other technology or technical field.
The recitation of the above-identified additional limitations in the claims amount to mere instructions to implement the abstract idea on a computer. Simply using a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); and TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Moreover, implementing an abstract idea on a generic computer does not add significantly more, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
For at least the above reasons, the claims are directed to applying an abstract idea on a general purpose computer without (i) improving the performance of the computer itself, or (ii) providing a technical solution to a problem in a technical field. In other words, none of the claims provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that these claims amount to significantly more than the abstract idea itself.
Taking the additional elements individually and in combination, the additional elements do not provide significantly more. Specifically, when viewed individually, the above-identified additional elements in the independent claims do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment (processing received data using a trained model). That is, neither the general computer elements nor any other additional element adds meaningful limitations to the abstract idea because these additional elements represent insignificant extra-solution activity. When viewed as a combination, these above-identified additional elements simply instruct the practitioner to implement the claimed functions with well-understood, routine and conventional activity specified at a high level of generality in a particular technological environment. As such, there is no inventive concept sufficient to transform the claimed subject matter into a patent-eligible application. As such, the above-identified additional elements, when viewed as whole, do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Thus, the claims merely apply an abstract idea to a computer and do not (i) improve the performance of the computer itself, or (ii) provide a technical solution to a problem in a technical field.
Therefore, none of the claims amounts to significantly more than the abstract idea itself.
Accordingly, the claims are not patent eligible and rejected under 35 U.S.C. 101 as being directed to abstract ideas implemented on a generic computer in view of the Supreme Court Decision in Alice Corporation Pty. Ltd. v. CLS Bank International, et al.
Conclusion
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/JUSTIN XU/Primary Examiner, Art Unit 3791
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