DETAILED ACTION
This action is in response to the amendment filed 3 February 2026.
Claims 1 – 20 are pending and have been examined; claims a-bb have been cancelled by Applicant.
This action has been made FINAL.
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 .
Response to Amendments
Applicant’s amendment to claim 10, filed 3 February 2026, has rendered the previous 35 USC 112(b) rejection moot.
Response to Arguments
Applicant's arguments filed 3 February 2026 have been fully considered but they are not persuasive.
35 USC 101
Applicant’ argues “Rather, claim 1 relates to a method for training a neural network for few-shot learning. The method involves obtaining a sample subset that includes support and query samples, and inputting this subset into a neural network to generate feature information for the query sample and a first prediction result based on similarity with the support sample. Additionally, a second prediction result is generated using feature information from multiple groups of support samples and their corresponding labels, combined with the query sample's feature information. The neural network is then trained iteratively using two loss functions: one measuring similarity between the first prediction and the first correct label, and another measuring similarity between the first and second predictions or between the second prediction and the second correct label. This dual-loss approach improves accuracy by leveraging both intra-task and cross-task relationships. Here, the claim is not directed to a mathematical algorithm in the abstract, but rather, to few-shot training. That is, while the claim involves few-shot learning, it does not merely recite a mathematical concept (such as a math algorithm, math equation, math relationship) in isolation. Instead, it specifies a concrete technical process implemented on computing devices to solve a technical problem. Thus, claim 1 is not directed to the abstract idea grouping and is patent subject matter eligible”. Examiner respectfully disagrees.
Applicant’s claims recite the use of mathematical algorithms to produce results that are used as inputs to train a neural network. While Applicant argues that the “claim is not directed to a mathematical algorithm in the abstract, but rather, to few-shot training”, the field of use does not make the claim any less abstract. See MPEP 2106 I … Supreme Court’s Bilski and Alice Corp. decisions. Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785 F.3d 616, 623, 114 USPQ2d 1711, 1715 (Fed. Cir. 2015) ("[W]e note that Alappat has been superseded by Bilski, 561 U.S. at 605–06, and Alice Corp. v. CLS Bank Int’l, 573 U.S. 208, 110 USPQ2d 1976 (2014)"); Intellectual Ventures I LLC v. Capital One Bank (USA), N.A., 792 F.3d 1363, 1366, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015) ("An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, such as the Internet [or] a computer"). Lastly, eligibility should not be evaluated based on whether the claimed invention has utility, because "[u]tility is not the test for patent-eligible subject matter." Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1380, 118 USPQ2d 1541, 1548 (Fed. Cir. 2016).
As noted in the prior action, and supported by Applicant’s disclosure, the step of obtaining a first prediction result that corresponds to the first query sample …wherein the first prediction result is generated based on a similarity between feature information of the first support sample and the first feature information describes a mathematical algorithm which compares similarities between data1; the step of generating a second prediction result corresponding to the first query sample based on second feature information corresponding to the M groups of support samples, first labeling results corresponding to the M groups of support samples, and the first feature information of the first query sample, wherein each of the first labeling results indicates a correct result corresponding to one of the M groups of support samples also describes a mathematical algorithm which compares similarities between data2; and the step of training the first neural network based on a first loss function and a second loss function, until a preset condition is met, with the conditions of wherein the first loss function indicates a similarity between the first prediction result and a second labeling result, and the second loss function indicates a similarity between the first prediction result and the second prediction result or a similarity between the second prediction result and the second labeling result; and wherein the second labeling result is a correct result of the first query sample describes using a mathematical algorithms, e.g., loss functions, to perform the training of the neural network3.
Further, as discussed in the July 2024 Subject Matter Eligibility Examples, Example 47. Anomaly Detection, Claim 2:
“Step (c) recites training an ANN using a selected algorithm. The training algorithm is a backpropagation algorithm and a gradient descent algorithm. When given their broadest reasonable interpretation in light of the background, the backpropagation algorithm and gradient descent algorithm are mathematical calculations. The plain meaning of these terms are optimization algorithms, which compute neural network parameters using a series of mathematical calculations. The fourth paragraph of the background supports the plain meaning by stating the “gradient descent begins by initializing the values of parameters and then applying a gradient descent calculation, which uses mathematical calculations to iteratively adjust the values so they minimize a loss function.” The background also states that “backpropagation is a mathematical calculation for supervised learning of ANNs using gradient descent.””, and as such was found that, step c, which requires specific mathematical calculations (a backpropagation algorithm and a gradient descent algorithm) to perform the training of the ANN and therefore encompasses mathematical concepts”.
Applicant’s claimed invention is similar in scope to the above mentioned Example, as mathematical algorithms are used to produce the training inputs.
Applicant further argues “Furthermore, even if, for the sake of argument, claim 1 recites matters within this grouping, claim 1 integrates the alleged judicial exception into a practical application, which apply, rely on, or use the alleged judicial exception in a manner that imposes a meaningful limit on the alleged judicial exception. For example, the claim does not merely perform a mathematical algorithm which compares similarities between data; rather, it obtains a sample subset that includes support and query samples, and inputs this subset into a neural network to generate feature information for the query sample and a first prediction result based on similarity with the support sample. In addition, a second prediction result is generated using feature information from multiple groups of support samples and their corresponding labels, combined with the query sample's feature information. The neural network is then trained iteratively using two loss functions: one measuring similarity between the first prediction and the first correct label, and another measuring similarity between the first and second predictions or between the second prediction and the second correct label. This second loss function enforces similarity between the first prediction result and the second prediction result (or between the second prediction result and the second correct label). This helps the neural network generalize better by considering both intra-group features and inter-group features, thereby demonstrating integration into a practical application. Thus, for the above reasons, claim 1 is subject matter eligible.” Examiner respectfully disagrees.
While Applicant argues improvements to technology by helping “the neural network generalize better by considering both intra-group features and inter-group features”, this alleged improvement is not reflected in the claims, nor do the claim limitations tie the claim to any improvement discussed in the specification. Initially it is noted that the abstract idea itself cannot provide the technical improvement (e.g., obtaining a first prediction result that corresponds to the first query sample wherein the first prediction result is generated based on a similarity between feature information of the first support sample and the first feature information; generating a second prediction result corresponding to the first query sample based on second feature information corresponding to the M groups of support samples, first labeling results corresponding to the M groups of support samples, and the first feature information of the first query sample, wherein each of the first labeling results indicates a correct result corresponding to one of the M groups of support samples; and training the first neural network based on a first loss function and a second loss function, until a preset condition is met; wherein the first loss function indicates a similarity between the first prediction result and a second labeling result, and the second loss function indicates a similarity between the first prediction result and the second prediction result or a similarity between the second prediction result and the second labeling result; and wherein the second labeling result is a correct result of the first query sample). Additionally, many of the features argued are not recited in Applicant’s claims.
Further, even if the above quoted limitations did not comprise an abstract idea (which Examiner is NOT conceding), the training of a generically claimed neural network model with data does not produce a technical improvement, regardless of the input or the iterative nature. As stated in Recentive Analytics, 4 INC. v. FOX Corp 692 F. Supp. 3d 438 (Fed. Cir. 2025) “The requirements that the machine learning model be “iteratively trained” or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement. Recentive’s own representations about the nature of machine learning vitiate this argument: Iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. See, e.g., Opposition Br. 9 (“[U]sing a machine learning technique[] . . . necessarily includes [an] iterative[] training step . . . .” (internal quotation marks and citation omitted)); Transcript at 26:21–24 (“[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input”).
Applicant further argues “Furthermore, even if the claims are, assuming for the sake of argument, directed to an abstract idea, applicants respectfully submit that the elements of claim I recite significantly more which amounts to an inventive concept. The claims in DDR are tied to computer technology, even if there is not a technical improvement, because the claims were found to include a specific way of automating a web site creation by incorporating elements from different sources, and was found to include significantly more than the abstract idea. Here, claim I is tied to a method for training a neural network for few-shot learning. The method involves obtaining a sample subset that includes support and query samples, and inputting this subset into a neural network to generate feature information for the query sample and a first prediction result based on similarity with the support sample. Additionally, a second prediction result is generated using feature information from multiple groups of support samples and their corresponding labels, combined with the query sample's feature information. The neural network is then trained iteratively using two loss functions: one measuring similarity between the first prediction and the first correct label, and another measuring similarity between the first and second predictions or between the second prediction and the second correct label. Similar to DDR, claim 1 is tied to a specific few-shot approach that improves few-shot learning. The operations of claim 1 are not generic-they improve the precision of the prediction output result of the neural network. Each operation is necessarily rooted in computer technology and performs distinct functions that collectively accomplish the claimed method for few-shot learning. Claim 1, therefore, integrates any alleged abstract idea into a practical application and is directed to at least an inventive concept”. Examiner respectively disagrees.
Applicant’s claimed invention provides for no technical improvements, nor does it produce any result except training the first neural network based on a first loss function and a second loss function, until a preset condition is met, which is merely inputting data into a model and comparing to other data until a desired condition is met. There is no specific computer implementation claimed; even the neural network is recited at a high level of generality. While Applicant’s specification at paragraphs [0009], [0019], [0053], [0075], [0127], [0139], [0179], and [0180] mention an improvement, Applicant’s claims are not tied to that improvement, unlike those in Enfish.
The 35 USC 101 rejection of the claims is maintained.
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 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claimed invention, when the claims are taken as a whole, is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 2A – 1: The claims recite a Judicial Exception. Exemplary independent claim 1 recites the limitations of:
obtaining a first sample subset from a sample set, wherein the sample set comprises M groups of support samples, and the first sample subset comprises a first support sample and a first query sample [i.e., data collection];
inputting the first sample subset into a first neural network, wherein the first neural network generates first feature information of the first query sample [i.e., using a trained network as a tool]4, and obtaining a first prediction result that corresponds to the first query sample and that is output by the first neural network, wherein the first prediction result is generated based on a similarity between feature information of the first support sample and the first feature information;
generating a second prediction result corresponding to the first query sample based on second feature information corresponding to the M groups of support samples, first labeling results corresponding to the M groups of support samples, and the first feature information of the first query sample, wherein each of the first labeling results indicates a correct result corresponding to one of the M groups of support samples; and
training the first neural network based on a first loss function and a second loss function, until a preset condition is met;
wherein the first loss function indicates a similarity between the first prediction result and a second labeling result, and the second loss function indicates a similarity between the first prediction result and the second prediction result or a similarity between the second prediction result and the second labeling result; and
wherein the second labeling result is a correct result of the first query sample.
These limitations (bolded and italiczed), as drafted, is / are a process that, under its broadest reasonable interpretation, covers mathematical concepts. See MPEP 2106.04(a)(2) I. 5. The step of and obtaining a first prediction result that corresponds to the first query sample …wherein the first prediction result is generated based on a similarity between feature information of the first support sample and the first feature information describes a mathematical algorithm which compares similarities between data6; the step of generating a second prediction result corresponding to the first query sample based on second feature information corresponding to the M groups of support samples, first labeling results corresponding to the M groups of support samples, and the first feature information of the first query sample, wherein each of the first labeling results indicates a correct result corresponding to one of the M groups of support samples also describes a mathematical algorithm which compares similarities between data7; and the step of training the first neural network based on a first loss function and a second loss function, until a preset condition is met, with the conditions of wherein the first loss function indicates a similarity between the first prediction result and a second labeling result, and the second loss function indicates a similarity between the first prediction result and the second prediction result or a similarity between the second prediction result and the second labeling result; and wherein the second labeling result is a correct result of the first query sample describes using a mathematical algorithms, e.g., loss functions, to perform the training of the neural network8.
Step 2A – 2: This judicial exception is not integrated into a practical application, and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Exemplary independent claim 1 recites the additional limitations of: obtaining a first sample subset from a sample set, wherein the sample set comprises M groups of support samples, and the first sample subset comprises a first support sample and a first query sample, however this amounts to mere data gathering recited at a high level of generality, and thus is insignificant extra-solution activity (see MPEP 2106.05(g)); inputting the first sample subset into a first neural network, wherein the first neural network generates first feature information of the first query sample, however this provides for nothing more than mere instructions to implement an abstract idea on a generic computer (see MPEP 2106.05(f)).
Independent claim 11 recites the additional elements of a processor and a memory, however these are recited at a high level of generality, and amounts to no more than mere instructions to apply the exception using a generic computer.
Further, the claims do not provide for or recite any improvements to the functioning of a computer, or to any other technology or technical field; applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; applying the judicial exception with, or by use of, a particular machine; effecting a transformation or reduction of a particular article to a different state or thing; or applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
The claim is directed to the abstract idea.
The dependent claims have the same deficiencies as their parent claims as being directed towards an abstract idea, as the dependent claims merely narrow the scope of their parent claims, and it has been held that “[i]n defining the excluded categories, the Court has ruled that the exclusion applies if a claim involves a natural law or phenomenon or abstract idea, even if the particular natural law or phenomenon or abstract idea at issue is narrow.” (buySAFE, Inc. v. Google, Inc., 765 F.3d 1350. )
Turning to the dependent claims, none of the claimed features of the dependent claims further limit the claimed invention in such a way to direct the claimed invention to statutory subject matter (e.g. change the scope of the claimed invention as to no longer be directed towards an abstract idea, or include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements or combination of elements in the claims other than the abstract idea per se), nor do they add limitations that, when taken as a combination, result in the claim as a whole amounting to significantly more than the judicial exception. In respect to exemplary dependent claims 2 – 9:
Claims 2, 3, and 4 merely describe additional mathematical concepts, the addition of mental processes, e.g., first labeling results9, and additional data collection;
Claims 5, 6, and 9 merely further describe the type of neural network that can be used, and recite additional mathematical concepts and additional data collection;
Claims 7 and 8 merely further describe the data in the groups;
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, explained with respect to Step 2A, Prong Two, the additional elements or combination of elements in the claims other than the abstract idea per se amount to no more than mere instructions to implement the idea on a computer, or the recitation of generic computer structure that serves to perform generic computer functions previously known to the industry10 [e.g. performing repetitive calculations; receiving, processing, and storing data; electronically scanning or extracting data from a physical document; electronic recordkeeping; automating mental tasks; receiving or transmitting data over a network, e.g., using the Internet to gather data] .
Applicant’s specification, at, e.g., paragraphs [0058], [0189]-[0208], FIGs. 14 – 16, provides evidence of generic computer hardware performing generic, well-known, computer functions.
Viewed as a whole, these additional claim elements, both individually and in combination, do not provide meaningful limitations to transform the above identified abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more (e.g. improvements to another technology or technical fields, improvements to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment) than the abstract idea itself. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation11.
Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. v. CLS Bank International, 573 U.S. No. 13–298.
Allowable Subject Matter
Claims 1 – 20 would be allowable if rewritten or amended to overcome the rejections under 35 U.S.C. 112(b) and 35 U.S.C. 101 (as applicable), set forth in this Office action.
The closest prior art of record includes Tao et al. (U.S. 2024/0071050), which is directed to and discloses a system and method for domain-agnostic bias reduction with selected sampling for few-shot learning; Karlinsky et al. (U.S.2022/0058505), which is directed to and discloses TAFSSL: task adaptive feature sub-space learning for few-shot learning; Shen et al. (U.S.2023/0368038), which is directed to and discloses an improved fine-tuning strategy for few shot learning; Kolouri et al. (U.S.2020/0130177) which is directed to and discloses a systems and methods for few-shot transfer learning; Wertheimer, Davis, Luming Tang, and Bharath Hariharan. "Fine-Grained Few-shot classification with feature map reconstruction networks." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021; Li, G., & Yu, Y. (2015); Li, Guanbin, and Yizhou Yu. "Visual saliency based on multiscale deep features." Proceedings of the IEEE conference on computer vision and pattern recognition. 2015; Ganin, Yaroslav, and Victor Lempitsky. "Unsupervised domain adaptation by backpropagation." International conference on machine learning. PMLR, 2015; and Choi, Yunjey, et al. "Stargan: Unified generative adversarial networks for multi-domain image-to-image translation." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
However, with respect to exemplary independent claim 1, the closest prior art of record, either alone or taken in combination with any other references of record, do not anticipate or render obvious the claimed functionality.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
18/Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALAN S MILLER whose telephone number is (571)270-5288. The examiner can normally be reached on M-F 10am-6pm. Examiner’s fax phone number is (571) 270-6288.
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/ALAN S MILLER/Primary Examiner, Art Unit 3625
1 See, e.g., Applicant’s specification, [00119] “… performs a similarity calculation operation based on the M pieces of second feature information and the first feature information of one first query sample, to obtain first similarity information…”.
2 See, e.g., Applicant’s specification, [00119] “… generates the second prediction result based on the first similarity information and the M first labeling results corresponding to the M groups of support samples.”. [00120] “In this embodiment of this disclosure, the second prediction result of the first query sample is generated by using the similarity between the feature of the first query sample and the feature of each of the M groups of support samples, and the M first labeling results corresponding to the M groups of support samples. In a manner of calculating the similarity, the second prediction result is generated. Operation is simple, and is easy to implement”.
3 See, e.g., paragraph [00132] “Specifically, in a training process, after generating the function value of the first loss function and the function value of the second loss function, the training device may perform weighted summation on the function value of the first loss function and the function value of the second loss function, to obtain a total function value, and perform gradient derivation on the total function value, and reversely update the weighting parameters of the feature extraction network included in the first neural network. A weight of the function value of the first loss function and the function value of the second loss function is a hyperparameter. An example of a formula of the first loss function and the second loss function may be as follows…”, [00165].
4 See, e.g., Applicant’s disclosure at [0097]-[0099], [0103], [0104], [0139], [0156].
5 See MPEP 2106.04(a)(2) I - The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. The Supreme Court has identified a number of concepts falling within this grouping as abstract ideas including: a procedure for converting binary-coded decimal numerals into pure binary form, Gottschalk v. Benson, 409 U.S. 63, 65, 175 USPQ2d 673, 674 (1972); a mathematical formula for calculating an alarm limit, Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ2d 193, 195 (1978); the Arrhenius equation, Diamond v. Diehr, 450 U.S. 175, 191, 209 USPQ 1, 15 (1981); and a mathematical formula for hedging, Bilski v. Kappos, 561 U.S. 593, 611, 95 USPQ 2d 1001, 1004 (2010).
The Court’s rationale for identifying these "mathematical concepts" as judicial exceptions is that a ‘‘mathematical formula as such is not accorded the protection of our patent laws,’’ Diehr, 450 U.S. at 191, 209 USPQ at 15 (citing Benson, 409 U.S. 63, 175 USPQ 673), and thus ‘‘the discovery of [a mathematical formula] cannot support a patent unless there is some other inventive concept in its application.’’ Flook, 437 U.S. at 594, 198 USPQ at 199. In the past, the Supreme Court sometimes described mathematical concepts as laws of nature, and at other times described these concepts as judicial exceptions without specifying a particular type of exception. See, e.g., Benson, 409 U.S. at 65, 175 USPQ2d at 674; Flook, 437 U.S. at 589, 198 USPQ2d at 197; Mackay Radio & Telegraph Co. v. Radio Corp. of Am., 306 U.S. 86, 94, 40 USPQ 199, 202 (1939) (‘‘[A] scientific truth, or the mathematical expression of it, is not patentable invention[.]’’). More recent opinions of the Supreme Court, however, have affirmatively characterized mathematical relationships and formulas as abstract ideas. See, e.g., Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 218, 110 USPQ2d 1976, 1981 (2014) (describing Flook as holding "that a mathematical formula for computing ‘alarm limits’ in a catalytic conversion process was also a patent-ineligible abstract idea."); Bilski v. Kappos, 561 U.S. 593, 611-12, 95 USPQ2d 1001, 1010 (2010) (noting that the claimed "concept of hedging, described in claim 1 and reduced to a mathematical formula in claim 4, is an unpatentable abstract idea,").
6 See, e.g., Applicant’s specification, [00119] “… performs a similarity calculation operation based on the M pieces of second feature information and the first feature information of one first query sample, to obtain first similarity information…”.
7 See, e.g., Applicant’s specification, [00119] “… generates the second prediction result based on the first similarity information and the M first labeling results corresponding to the M groups of support samples.”. [00120] “In this embodiment of this disclosure, the second prediction result of the first query sample is generated by using the similarity between the feature of the first query sample and the feature of each of the M groups of support samples, and the M first labeling results corresponding to the M groups of support samples. In a manner of calculating the similarity, the second prediction result is generated. Operation is simple, and is easy to implement”.
8 See, e.g., paragraph [00132] “Specifically, in a training process, after generating the function value of the first loss function and the function value of the second loss function, the training device may perform weighted summation on the function value of the first loss function and the function value of the second loss function, to obtain a total function value, and perform gradient derivation on the total function value, and reversely update the weighting parameters of the feature extraction network included in the first neural network. A weight of the function value of the first loss function and the function value of the second loss function is a hyperparameter. An example of a formula of the first loss function and the second loss function may be as follows…”, [00165].
9 See MPEP 2106.04(a)(2) III.
10 “It is well-settled that mere recitation of concrete, tangible components is insufficient to confer patent eligibility to an otherwise abstract idea. Rather, the components must involve more than performance of “‘well understood, routine, conventional activit[ies]’ previously known to the industry.” Alice, 134 S. Ct. at 2359 (quoting Mayo, 132 S.Ct. at 1294)”. Id, pages 10-11. “Likewise, the server fails to add an inventive concept because it is simply a generic computer that “administer[ s]” digital images using a known “arbitrary data bank system.” Id. at col. 5 ll. 45–46. But “[f]or the role of a computer in a computer-implemented invention to be deemed meaningful in the context of this analysis, it must involve more than performance of ‘well-understood, routine, [and] conventional activities previously known to the industry.’” Content Extraction, 776 F.3d at 1347–48 (quoting Alice, 134 S. Ct at 2359). “These steps fall squarely within our precedent finding generic computer components insufficient to add an inventive concept to an otherwise abstract idea. Alice, 134 S. Ct. at 2360 (“Nearly every computer will include a ‘communications controller’ and a ‘data storage unit’ capable of performing the basic calculation, storage, and transmission functions required by the method claims.”); Content Extraction, 776 F.3d at 1345, 1348 (“storing information” into memory, and using a computer to “translate the shapes on a physical page into typeface characters,” insufficient confer patent eligibility); Mortg. Grader, 811 F.3d at 1324–25 (generic computer components such as an “interface,” “network,” and “database,” fail to satisfy the inventive concept requirement); Intellectual Ventures I, 792 F.3d at 1368 (a “database” and “a communication medium” “are all generic computer elements”); BuySAFE v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014) (“That a computer receives and sends the information over a network—with no further specification—is not even arguably inventive.”)”. TLI Communications LLC v. AV Automotive L.L.C., (No. 15-1372, (Fed. Cir. May 17, 2016)), at *12-13.
See additionally MPEP 2106.05(d).
11 “Nor, in addressing the second step of Alice, does claiming the improved speed or efficiency inherent with applying the abstract idea on a computer provide a sufficient inventive concept. See Bancorp Servs., LLC v. Sun Life Assurance Co. of Can., 687 F.3d 1266, 1278 (Fed. Cir. 2012) (“[T]he fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter.”); CLS Bank, Int’l v. Alice Corp., 717 F.3d 1269, 1286 (Fed. Cir. 2013) (en banc) aff’d, 134 S. Ct. 2347 (2014) (“[S]imply appending generic computer functionality to lend speed or efficiency to the performance of an otherwise abstract concept does not meaningfully limit claim scope for purposes of patent eligibility.” (citations omitted))”. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 115 U.S.P.Q.2d 1636 (Fed. Cir. 2015).