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 .
Status of the Application
This action is in response to the Request for Continued Examination filed September 19, 2025. Claims 1, 11 and 18 have been amended. Claims 1-6, 8, 10-15 and 17-21 are pending and have been examined in this application.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 9/19/2025 has been entered.
Claim Objections
Claim 18 is objected to because of the following informalities:
In line 20, “the reliability score representing an interaction with a content item and the reliability score representing a confidence level” should be "the probability score representing an interaction with a content item and the reliability score representing a confidence level "
Appropriate correction is required.
Claim Rejections - 35 USC § 112
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 1-6, 8, 10-15 and 17-21 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 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 pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
In Claims 1, 11 and 18 the limitation “generating a probability score and a reliability score using the trained predictive model, the probability score representing an interaction with a content item and the reliability score representing a confidence level associated with the probability score,” is not described in the original disclosure. The specification states that in “the output is a predicted probability that the users will click on the targeted content (e.g., click rate) and a reliability score of the prediction” [0024]. This does not describe that the trained predictive model generates a probability score and a reliability score, and moreover that the reliability score represents a confidence level associated with a probability score. Accordingly, this is impermissible new matter. Claims 2-6, 8, 10; 12-15, 17 and 19-21 by being dependents of claims 1, 11 and 18 respectively are also rejected.
In Claims 1, 11 and 18 the limitation “adjusting a metric assigned to the content item, the metric adjusted based on the probability score and the reliability score, the metric controlling whether the content item will be suggested to one or more of the users,” is not described in the original disclosure. Applicant proffers that support is found in the specification at paragraphs [0035 and [0036]. However, the specification states that “if the expected click rate is higher than the predicted click rate, the prediction tool 130 provides an indication that the content cost should be lowered. If the predicted click rate is higher than the expected click rate, the prediction tool 130 provides an indication that the content cost should be higher. According to some embodiments, the prediction tool 130 may further provide a new content cost that accurately reflects the likelihood of the targeted content being selected by the users” [0035]. This does not describe that a metric assigned to a content item is adjusted based on the probability score and the reliability score, neither that the metric controls whether the content item will be suggested to the users. Accordingly, this is impermissible new matter. Claims 2-6, 8, 10; 12-15, 17 and 19-21 by being dependents of claims 1, 11 and 18 respectively are also rejected.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-6, 8, 10-15 and 17-21 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, claims 1-6, 8, 10-15 and 17-21 are directed toward at least one abstract idea without significantly more. In accordance with MPEP 2106, the rationale for this determination is explained below:
Representative claim 1 is directed towards a method, claim 11 is directed towards a device, claim 18 is directed towards a non-transitory computer-readable medium, which are statutory categories of invention.
Although, claim 1 is directed toward a statutory category of invention, the claim appears to be directed towards an abstract idea. The limitations that recite the abstract ideas are: generating a predictive model based on the first set of data using a regularized loss algorithm, the regularized loss algorithm including a loss function with a regularization term, the regularization term being a normalized logit loss to adjust the loss function, wherein the loss function is a binary cross entropy (BCE) function, and the regularization term is adapted to normalize logits used for the BCE; evaluating the output of the predictive model by determining evaluation metrics indicative of predicted performance and reliability of the predictive model; adjusting strength of regularization of the regularized loss algorithm based on the evaluation metrics; and training the predictive model using the regularized loss algorithm, generating a probability score and a reliability score using the trained predictive model, the probability score representing an interaction with a content item and a reliability score representing a confidence level associated with the probability score; and adjusting a metric assigned to the content item, the metric adjusted based on the probability score and the reliability score, the metric controlling whether the content item will be suggested to one or more of the users. These limitations, set forth mathematical relationships and calculations, which organize and manipulate content data through mathematical correlations and are thus, directed towards the abstract grouping of Mathematical Concepts in prong one of step 2A of the Alice/Mayo test (see MPEP 2106.04(a)(2) I).
This judicial exception is not integrated into a practical application because, when analyzed as a whole under prong two of step 2A of the Alice/Mayo test (see MPEP 2106.04(d)), the additional elements provided by the claim are recited at a high level of generality and amount to insignificant extra-solution activity. In particular the claim recites the additional elements of receiving a first set of data, the first set of data indicative of one or more user engagement metrics; receiving a second set of data; generating an output using the predictive model based on the second set of data, which amount to insignificant extra-solution activity because they are necessary data gathering and outputting to implement the judicial exception. See MPEP 2106.05(g). Simply adding extra-solution activity to the abstract idea is not a practical application of the abstract idea. The additional elements do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea, and the claims are directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional limitation amounts to pre and post extra-solution activities. Viewing the limitations individually, the receiving a first set of data, the first set of data indicative of one or more user engagement metrics, the receiving a second set of data, and the generating an output using the predictive model based on the second set of data are necessary data gathering of a type of data and data outputting used to implement the aforementioned abstract concept, see MPEP 2106.05(g). The courts have recognized performing repetitive calculations; receiving, processing, and storing data; automating mental tasks and receiving or transmitting data over a network to be well‐understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity. See MPEP 2106.05(d)II; Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1321, 120 USPQ2d 1353, 1362 (Fed. Cir. 2016); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015). Moreover, a processor, and memory (claim 11), also do not constitute significantly more because they simply are an attempt to limit the abstract idea to a particular technological environment1. Viewing the limitations as a combination, the claims merely instruct the practitioner to implement the abstract idea with a high-level of generality executing expected computer functions. Merely applying an exception using generic computer components cannot provide an inventive concept. Therefore, the limitations of the claim as a whole, when viewed individually and as an ordered combination, do not amount to significantly more than the abstract idea.
A review of dependent claims 2-6, 8, 10, likewise, do not recite any limitations that would remedy the deficiencies outlined above as they do not add any elements which integrate the abstract idea into a practical application or constitute significantly more. For instance, claims 2-3, 6, 8, 10, adds to the Mathematical Concepts. Claims 4-5, describes data. Thus, while they may slightly narrow the abstract idea by further describing it, they do not make it less abstract and are rejected accordingly. Further still, claims 11-15, 17-21 suffer from substantially the same deficiencies as outlined with respect to claims 1-6, 8, 10 and are also rejected accordingly.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3, 8, 11, 13 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Sato (US Publication 2023/0401488) in view of Iscen (US Publication 2025/0131694).
A. In regards to Claims 1, 11 and 18, Sato discloses a method, device and non=transitory computer-readable medium comprising:
a processor; Sato [0135];
and a memory having a plurality of instructions stored thereon that, when executed by the processor, Sato [0138]
receiving a first set of data, the first set of data indicative of one or more user engagement metrics; Sato [0075: a positive example and a negative example are prepared based on a user behavior history in the past, a combination of the user and the context is input to a prediction model; 0079: user behavior history may include, for example, a book purchase history, a video browsing history, or a restaurant visit];
receiving a second set of data; Sato [0085: model 14, which is trained by using the data collected in a facility different from the introduction destination facility];
generating an output using the predictive model based on the second set of data; Sato [0087: the model 14 can predict with a certain degree of accuracy the behavior of the users in the introduction destination facility by performing a training by using both data];
evaluating the output of the predictive model by determining evaluation metrics indicative of predicted performance and reliability of the predictive model; Sato [0075: the machine learning is performed until the prediction error converges, and the target prediction performance is acquired; 0082: the model includes training the model by using training data to create a prediction model (suggestion model) that satisfies a practical level of suggestion performance; 0087: the model can predict with a certain degree of accuracy the behavior of the users; 0109: prediction performance of the model is checked by using the evaluation data];
adjusting strength of regularization of the regularized loss algorithm based on the evaluation metrics; Sato [0210: instruction to correct the training of the local model based on the calculation result of the difference between models-evaluation unit; e.g., instructing the update of the parameter of the local model together with the value of the partial differentiation of the domain regularization portion calculated by the domain regularization calculation unit];
training the predictive model using the regularized loss algorithm; Sato [0076: using the trained prediction model, items with a high browsing probability, which is predicted with respect to the combination of the user and the context, are suggested];
generating a probability score and a reliability score using the trained predictive model, the probability score representing an interaction with a content item and the reliability score representing a confidence level associated with the probability score; Sato [0076: using the trained prediction model, items with a high browsing probability, which is predicted with respect to the combination of the user and the context, are suggested; 0082: training the model by using training data to create a prediction model that satisfies a practical level of suggestion performance; 0087: the model can also predict with a certain degree of accuracy the behavior of the users in the introduction destination facility by performing a training by using both data; 0172: loss function L applied to the training of the local model of the facility 1 is configured to include, for example, a prediction error portion and a domain regularization portion as in the following Equation:
L~(y-y _true )2+( wl_dl -( wl_d2+wl_d3)!2)2 +( w2_
dl-(w2_d2+w2_d3)/2)2];
Sato does not specifically disclose, generating a predictive model based on the first set of data using a regularized loss algorithm, the regularized loss algorithm including a loss function with a regularization term, the regularization term being a normalized logit loss to adjust the loss function, wherein the loss function is a binary cross entropy (BCE) function, and the regularization term is adapted to normalize logits used for the BCE; this is disclosed by Iscen [0051: loss function can include a neighbor consistency regularization loss function. In particular, the neighborhood consistency regularization loss function can be configured to penalize the divergence of a predicted output classification (e.g., the first classification) of a particular embedding (e.g., the first embedding) from a weighted combination of predicted neighbor classifications; 0060: alternatively and/or additionally, the similarity values may be normalized such that the neighbor consistency regularization loss function remains a probability distribution; 0110: loss function can include a cross entropy loss function and a neighbor consistency regularization loss function; 0134: the predicted classification may include a normalized value (e.g., a softmax output [a score] descriptive of one or more classification probabilities)];
additionally and/or alternatively, Iscen discloses, adjusting strength of regularization of the regularized loss algorithm based on the evaluation metrics; Iscen [0106: adjust one or more parameters of the classification model based on the cross entropy loss and the neighbor consistency regularization loss; 0057: adjusting the weights of the supervised learning loss and the neighbor consistency regularization loss based on the stage of learning. For example, the supervised learning loss may be more heavily weighted at the beginning of training, while the neighbor consistency regularization loss may receive increased weighting as more training passes occur];
also, training the predictive model using the regularized loss algorithm, Iscen [0051: the loss function can include a neighbor consistency regularization loss function. In particular, the neighborhood consistency regularization loss function can be configured to penalize the divergence of a predicted output classification];
generating a probability score and a reliability score using the trained predictive model, the probability score representing an interaction with a content item and the reliability score representing a confidence level associated with the probability score. Iscen [0016: output classification can include a prediction score descriptive of a level of certainty for one or more possible classifications; and adjusting one or more parameters of the classification model based at least in part on the loss function; 0118: the combined loss function can weight the supervised loss function and the neighbor consistency regularization loss function based on the stage of training, the similarity values of the neighbors, a classification confidence score, and/or the class prediction score for the classification].
Sato does not specifically disclose, and adjusting a metric assigned to the content item, the metric adjusted based on the probability score and the reliability score, the metric controlling whether the content item will be suggested to one or more of the users. This is disclosed by Iscen [0016: the output classification can include a prediction score descriptive of a level of certainty for one or more possible classifications; and adjusting one or more parameters of the classification model based at least in part on the loss function; 0060: selected neighbors may be weighted when generating the predicted classification and/or weighted for loss function evaluation; the weighting may be based on the similarity score for the respective embedding or may be based on a certainty score, or class prediction score, for that particular embedding; 0042: the output generated by the classification model may be a score for each of a set of content items, with each score representing an estimated likelihood that the user will respond favorably to being recommended the content item].
It would have been obvious before the effective filing date of the invention for one of ordinary skill in the art to have modified the teachings of Sato with the teachings from Iscen with the motivation to provide a regularization loss function that can be configured to penalize a divergence of a first classification of a first embedding from a weighted combination of predicted neighbor classifications for one or more neighboring embeddings to the first embedding in the embedding space, where the operations can include adjusting one or more parameters of the classification model based at least in part on the loss function. Iscen [0013].
B. In regards to Claims 3, 13 and 19, Sato does not specifically disclose, wherein adjusting the strength of regularization of the regularized loss algorithm based on the evaluation metrics comprises adjusting one or more parameters of the regularized loss algorithm to adjust the regularization term based on the evaluation metrics This is disclosed by Iscen [0110: evaluation of the loss function can then be utilized to adjust one or more parameters of the classification model(s) and/or one or more parameters of the encoder model(s); the neighbor consistency regularization loss function can be utilized for neighborhood smoothing]. The motivation being the same as stated in claim 1.
C. In regards to Claim 8, Sato does not specifically disclose, wherein the predictive model is a deep neural network model. This is disclosed by Iscen [0171: the systems and methods can introduce a neighborhood consistency regularization for effective deep learning]. The motivation being the same as stated in claim 1.
Claims 2, 4-6, 10, 12, 14-15, 17 and 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over Sato (US Publication 2023/0401488) in view of Iscen (US Publication 2025/0131694) in further view of Olteanu Roberts (US Publication 2024/0265427).
A. In regards to Claim 2, Sato does not specifically disclose, wherein the evaluation metrics include an area under the curve (AUC) indicative of predicted performance and an expected calibration error (ECE) indicative of predicted reliability. This is disclosed by Olteanu Roberts [0043: example tasks may include clicks (CTR); 0131: performance may be evaluated using area under the precision recall curve (e.g., PR AUC) and ROC AUC metrics; 0155: miscalibration may be evaluated by monitoring the Expected Calibration Error (ECE) and Normalized Cross Entropy (NCE). The ECE may provide a measure of the difference in expectation between confidence and accuracy].
It would have been obvious before the effective filing date of the invention for one of ordinary skill in the art to have modified the teachings of Sato with the teachings from Olteanu Roberts with the motivation to provide a ranked set of results for display to a user, where one or more personalized downstream models includes a first model that generates a predicted probability that a particular listing will be clicked and a second model that generates a predicted conditional probability that a good or service represented by a listing will be purchased Olteanu Roberts [0004].
B. In regards to Claim 4, Sato does not specifically disclose, wherein the one or more user engagement metrics include clickthrough rate (CTR). This is disclosed by Olteanu Roberts [0043: example tasks may include clicks (CTR)]. The motivation being the same as stated in claim 2.
C. In regards to Claims 5 and 14, Sato does not specifically disclose, wherein the second set of data includes clickthrough rate (CTR) data of a user, and the output indicates a likelihood that the user will be interested in the one or more targeted contents. This is disclosed by Olteanu Roberts [0119: allow sellers to sponsor listings (e.g., ads) through a second-price cost-per-click auction campaign; thereafter, results may be re-ranked based on a combined value score output by the personalized CTR model; 0154: Table 14 presents another set of the personalized CTR and PCCVR offline performance results against the baseline CTR; 0120: in the CTR case, p(x) denotes the predicted probability p(y.sub.CTR=1) that a candidate listing (represented by x, a vector of input features or attributes) will be clicked; 0037: the models may be designed to output a probability that an ad will be purchased by a user]. The motivation being the same as stated in claim 2.
D. In regards to Claims 6, 15 and 21, Sato does not specifically disclose, comprising:
receiving a request for content to display to an electronic device; this is disclosed by Olteanu Roberts [0119: user's actions (e.g., using computing device to click on a link on a web page) result in request for an ad];
generating, using the trained predictive model, the probability of an interaction with a content item of a set of content items; this is disclosed by Olteanu Roberts [0037: models may include Click-Through Rate (CTR) and Post-Click Conversion Rate (PCCVR) baseline models used in rankings for sets of results; as a result models may be designed to output a probability that an ad will be purchased by a user];
and selecting the content item for display on the electronic device based on the generated probability for the selected content item. This is disclosed by Olteanu Roberts [0119: advertisements may then be provided by the server to another computing device (such as the computing devices that sent the original Ad Request) for display to the user]. The motivation being the same as stated in claim 2.
E. In regards to Claim 12, Sato does not specifically disclose, wherein the evaluation metrics include an area under the curve (AUC) indicative of predicted performance and an expected calibration error (ECE) indicative of predicted reliability, and the one or more user engagement metrics include clickthrough rate (CTR). This is disclosed by Olteanu Roberts [0043: example tasks may include clicks (CTR); 0131: performance may be evaluated using area under the precision recall curve (e.g., PR AUC) and ROC AUC metrics; 0155: miscalibration may be evaluated by monitoring the Expected Calibration Error (ECE) and Normalized Cross Entropy (NCE). The ECE may provide a measure of the difference in expectation between confidence and accuracy; 0043: example tasks may include clicks (CTR)]. The motivation being the same as stated in claim 2.
F. In regards to Claims 10, 17 and 20, Sato does not specifically disclose, comprising:
receiving a third set of data, the third set of data including one or more user engagement metrics; this is disclosed by Olteanu Roberts [0110: third representation for the set of user actions is generated using a learned representations component of the personalization module];
based on the third set of data using the trained predictive model, determining a likelihood of a targeted content being selected by users; this is disclosed by Olteanu Roberts [0076: CTR models may generate a predicted probability that a particular listing will be clicked];
and upon determining the likelihood of user selection, presenting a prediction value of the targeted content. This is disclosed by Olteanu Roberts [0076: may be combined into a value score which can be used to sort and determine an order of results in a set of results]. The motivation being the same as stated in claim 2.
Response to Arguments
Applicant's filed arguments have been fully considered but have not been found persuasive.
A. Applicant argues regarding the 35 U.S.C. § 101 rejection that claim 1 is subject matter eligible. The Examiner respectfully disagrees. The claim is directed to the abstract group of Mathematical Concepts because it describes mathematical relationships that calculate, organize and manipulate content (advertising) data through mathematical correlations.
Applicant submits that the claimed features improve upon the computer related technology of content delivery by solving the technical problems of overconfidence in prediction model predictions. The Examiner respectfully disagrees. That the claims detail specifically how the regularization term solves the overconfidence/underconfidence problems, including the normalized logit loss function being introduced in the BCE function in order to normalize logits used for the BCE, is in and of itself part of the abstract idea and not an additional element that integrates the abstract idea into a practical application. A mathematical relationship may be expressed in words or using mathematical symbols. See MPEP 2106.04(a)(2) subsection I. Additionally, making a predication or overconfidence in a predication is a Mental Process as this can be done in the human mind including using pen and paper. See MPEP 2106.04(a)(2) subsection III. Even assuming arguendo that the claimed subject matter includes additional elements that improve content delivery, this would not overcome the rejection because content delivery is in and of itself an abstract idea. Thus, any improvement would be for an abstract idea. Moreover, even newly discovered judicial exceptions are still exceptions, despite their novelty. For example, the mathematical formula in Parker v. Flook, 437 U.S. 584, 591-92, 198 USPQ 193, 198 (1978), the laws of nature in Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 73-74, 101 USPQ2d 1961, 1968 (2012), and the isolated DNA in Association for Molecular Pathology v. Myriad Genetics, Inc., 133 S. Ct. 2107, 2116, 106 USPQ2d 1972, 1978 (2013) were all novel, but were considered by the Supreme Court to be judicial exceptions.
As such, the claims as a whole, in view of Alice, the claims do not connote an improvement to another technology or technical field; the claims do not amount to an improvement to the functioning of a computer itself; and the claims do not move beyond a general link of the use of the abstract idea to a particular technological environment. Therefore, the 35 U.S.C. § 101 rejection is maintained.
B. Applicant’s arguments regarding the 35 U.S.C. § 103 rejection that references do not teach, suggest, or discuss, adjusting a metric assigned to the content item, the metric adjusted based on the probability score and the reliability score, the metric controlling whether the content item will be suggested to one or more of the users. The Examiner respectfully disagrees. Iscen discloses that the output of its classification model can include a prediction score descriptive of a level of certainty for one or more possible classifications, where adjusting one or more parameters of the classification model is based at least in part on the loss function; Iscen [0016]; and selected neighbors may be weighted when generating the predicted classification and/or weighted for the loss function evaluation; where the weighting may be based on the similarity score for respective embedding or may be based on a certainty score, or class prediction score, for that particular embedding; Iscen [0060]; and that the output generated by the classification model may be a score for each of a set of content items, with each score representing an estimated likelihood that the user will respond favorably to being recommended the content item. Iscen [0042].
C. Applicant’s arguments regarding the dependent claims are rejected accordingly to independent claims 1, 11 and 18.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Errol CARVALHO whose telephone number is (571)272-9987. The examiner can normally be reached on M-F 9:30-7:00 Alt Fri.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ilana Spar can be reached on 571- 270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/E CARVALHO/
Primary Examiner, Art Unit 3622
1 See, Alice Corp. Pty Ltd. v. CLS Bank lnt'l, 134 S. Ct. 2347, 2360 (2014) (noting that none of the hardware recited “offers a meaningful limitation beyond generally linking ‘the use of the [method] to a particular technological environment,’ that is, implementation via computers” (citing Bilski v. Kappos, 561 U.S. 593, 610-11 (2010))).