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 .
Style
In this action unitalicized bold is used for claim language, while italicized bold is used for emphasis.
Applicant Reply
“The claims may be amended by canceling particular claims, by presenting new claims, or by rewriting particular claims as indicated in 37 CFR 1.121(c). The requirements of 37 CFR 1.111(b) must be complied with by pointing out the specific distinctions believed to render the claims patentable over the references in presenting arguments in support of new claims and amendments. . . . The prompt development of a clear issue requires that the replies of the applicant meet the objections to and rejections of the claims. Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP § 2163.06. . . . An amendment which does not comply with the provisions of 37 CFR 1.121(b), (c), (d), and (h) may be held not fully responsive. See MPEP § 714.” MPEP § 714.02. Generic statements or listing of numerous paragraphs do not “specifically point out the support for” claim amendments. “With respect to newly added or amended claims, applicant should show support in the original disclosure for the new or amended claims. See, e.g., Hyatt v. Dudas, 492 F.3d 1365, 1370, n.4, 83 USPQ2d 1373, 1376, n.4 (Fed. Cir. 2007) (citing MPEP § 2163.04 which provides that a ‘simple statement such as ‘applicant has not pointed out where the new (or amended) claim is supported, nor does there appear to be a written description of the claim limitation ‘___’ in the application as filed’ may be sufficient where the claim is a new or amended claim, the support for the limitation is not apparent, and applicant has not pointed out where the limitation is supported.’)” MPEP § 2163(II)(A).
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 1-7, 9-14, 16-18, and 20-23 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) and the claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
All claims are found to be directed to one of the four statutory categories, unless otherwise indicated in this action. See also Spec. ¶20.
Step 2A Prongs One and Two (Alice Step 1): According to Office guidance, claims that read on math do not recite an abstract idea at step 2A1, when the claims fail to refer to the math by name.1 The MPEP also equates “recit[ing] a judicial exception” with “state[ing]” or “describ[ing]” an abstract idea in the claims.2 Consistent with this guidance, an abstract idea may be first recited in a dependent claim even though the independent claims read on that abstract idea. Claim limitations which recite any of the abstract idea groupings set forth in the manual are found to be directed, as a whole, to an abstract idea unless otherwise indicated.3 The claims do not recite additional elements that integrate the abstract ideas into a practical application.4 To confer patent eligibility to an otherwise abstract idea, claims may recite a specific means or method of solving a specific problem in a technological field.5
Independent Claims
1. A method, comprising: generating, by a computing device, a plurality of features based on input data; (This reads on mathematical operations and mathematical relationships. That the mathematical operations are carried out on a computer and any corresponding mathematical relationships are stored on a computer is a mere instruction to apply the abstract idea on a computer.) generating, by the computing device, a plurality of sequential arrays of a fixed length based on the input data; (This reads on mathematical operations and mathematical relationships. That the mathematical operations are carried out on a computer and any corresponding mathematical relationships are stored on a computer is a mere instruction to apply the abstract idea on a computer.) generating, by the computing device, a final sequential array of a predetermined shape based on the plurality of sequential arrays of the fixed length; (This reads on mathematical operations and mathematical relationships. That the mathematical operations are carried out on a computer and any corresponding mathematical relationships are stored on a computer is a mere instruction to apply the abstract idea on a computer.) generating, by the computing device, a training data set for a deep learning model based on the final sequential array of the predetermined shape and the plurality of features; (This reads on mathematical operations and mathematical relationships. That the mathematical operations are carried out on a computer and any corresponding mathematical relationships are stored on a computer is a mere instruction to apply the abstract idea on a computer.) generating, by the computing device, predictions on the input data by utilizing a previously trained model; (This reads on an instruction to implement a mental process (predicting) using generic computer components (a “trained model.”)) training, by the computing device, the deep learning model based on the generated training data set and the predictions on the input data; (Generic training of a generic machine learning model on data that is modified using math is no more than an instruction to apply an exception by using the modified data on an ordinary computer model in its ordinary capacity. Nothing in the claims taken individually or as an ordered combination indicates any improvement to the relevant technology that would constitute an inventive concept. It is noted here, that the Specification only mentions a “previously trained model” once. Spec. ¶70. See Spec. ¶¶1-84. The Specification describes the “previously trained model” consistent with an earlier iteration of the “deep learning model.” See Spec. ¶¶69-70 and Fig. 5 (“In embodiments, in the generate predictions step 265, the generated predictions can be based on a previously trained model from the model training 250. . . . at the training step 252, the model training 250 trains a deep learning model[.]”) Based on this description, the claims are not directed to two models in an unconventional configuration, but rather one model being updated using training data and its own output, in a continuous process. Therefore, the limitations above are found to read on generic computer components.) generating, by the computing device, recovery point objective (RPO) predictions of RPO drifts in a domain by utilizing at least one bidirectional long short-term memory (LSTM) layer of the deep learning model; (Generating a prediction of RPO drifts by utilizing a BiLSTM reads on an instruction to implement the mental process of generating a prediction using generic computer components in a particular field of use.) outputting, by the computing device, a notification which includes the RPO predictions of the RPO drifts in the domain; (Outputting the calculation is mere extra-solution activity.) and performing, by the computing device, preventative actions to mitigate downtime based on the RPO predictions and the RPO drifts. (Performing generic “preventative actions” based on RPO predictions and drifts, with the intended use of “mitigate[ing] downtime” is a mere instruction to apply an exception. Specific, unconventional, operations that reduce downtime would generally be directed to an improvement in a technological field. This is distinguished from claim language merely reciting generic “preventative actions” intended “to mitigate downtime.” In other words, claim language merely asserting an improvement does not support a finding that the claims are directed to that improvement. The claims must include specific components or steps that result in the asserted improvement. See MPEP § 2106.04(d)(1). (“[I]f the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. . . . It should be noted that while this consideration is often referred to in an abbreviated manner as the ‘improvements consideration,’ the word ‘improvements’ in the context of this consideration is limited to improvements to the functioning of a computer or any other technology/technical field, whether in Step 2A Prong Two or in Step 2B.”))
Independent claim 13 recites a manufacture implementing the process of claim 1. Therefore, the same analysis applies. In addition, the claim recites “A computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to” carry out the operations of claim 1. This is a mere instruction to apply the abstract idea to a computer.
Independent claim 17 recites a machine implementing the process of claim 1. Therefore, the same analysis applies. In addition, the claim recites “A system comprising: a processor, a computer readable memory, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to:” carry out the operations of claim 1. This is a mere instruction to apply the abstract idea to a computer. In addition, claim 17 recites “wherein the predictions on the input data correspond to the second domain which is different from a first domain of the input data.” (This merely limits the mental process of predicting to a modified data environment. That is, predicting is a mental process. Predicting in a new field (new domain) merely limits the prediction to a data environment associated with a particular field of use. Note that this also reads on the technique of transfer learning. The application of predictions in one domain to another domain reads on transfer learning, which uses a pattern learning in one domain (environment or situation) and applies it to a similar domain. Using experience in one domain to make predictions about another similar domain reads on a mental process.
Step 2B (Alice Step 2): The rejected claims do not recite additional elements that amount to significantly more than the judicial exception.
All additional limitations that do not integrate the claimed judicial exception into a practical application also fail to amount to significantly more, for the reasons given at step 2A2. All limitations found to be extra-solution activity at step 2A2 are found to be WURC, including limitations that read on mere data gathering, data storage, and data input/output/transfer. Specifically, the limitation of “outputting, by the computing device, a notification which includes the RPO predictions of the RPO drifts in the domain” reads on outputting data, which has been found to be WURC. Should any other claim limitations be rejected at step 2A1 as extra-solution activity but omitted here, it should be understood that such limitations are also found to be WURC at this step. Generic data input/output, storage, repetitive processing operations, and generic display of information and have been found to be generic WURC operations that do not transform the abstract idea into patent eligible subject matter, at the Alice step two analysis.6 Other aspects of generic computing have also been found to be WURC.7 Further, the description itself may provide support for a finding that claim elements are WURC. The analysis under § 112(a) as to whether a claim element is “so well-known that it need not be described in detail in the patent specification” is the same as the analysis as to whether the claim element is widely prevalent or in common use.8 Similarly, generic descriptions in the Specification of claimed components and features has been found to support a conclusion that the claimed components were conventional.9 Improvements to the relevant technology may support a finding that the claims include a patent eligible inventive concept. But some mechanism that results in any asserted improvements must be recited in the claim, and the Specification must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing the improvement.10 This applies to the dependent claims below.
Dependent Claims:
2. The method of claim 1, wherein the plurality of features comprises system configuration changes, system and application monitoring and network metrics, events, performance, logs, and historical series for a recovery point objective (RPO). (This merely limits the data environment used to train the model, to a field of use.)
3. The method of claim 1, wherein the fixed length of the sequential arrays comprises a predetermined number of timestamps within a time interval. (This merely limits the data environment used to train the model, to a field of use. Specifically, time stamps limit to fields of use which use time series data, or fields to which time is important to the goal of the model.)
4. The method of claim 3, wherein the fixed length is a configurable parameter. (Changing the length of a parameter is merely a mathematical operation.)
5. The method of claim 1, wherein the predetermined shape includes a number of observations, an array length, and a number of features. (The “shape,” as best understood, refers to the relationship of numbers in sets of arrays. This reads on a mathematical relationship. Further the data itself merely limits to a data environment associated with a field of use.)
6. The method of claim 1, wherein the training the deep learning model based on the training data set further comprises training the deep learning model based on the training data set using the at least one bidirectional (LSTM) layer. (This merely limits to one known type of machine learning model, by invoking an BiLSTM as a generic tool to train a model. This is merely an instruction to apply an exception using generic computer components.)
7. The method of claim 6, wherein the at least one bidirectional LSTM layer preserves information from both the past and the future of the input data. (This merely describes an aspect of the mathematical relationships associated with BiLSTM layers.)
9. The method of claim 1, further comprising sending the RPO predictions of the RPO drifts to a dashboard; displaying the RPO predictions of the RPO drifts, tables of root causes, and graphs of monitored metrics on the dashboard for customer visualization. (The claimed outputting of data reads on insignificant extra-solution activity and is WURC. Outputting RPO predictions, drifts, tables of root causes, and graphs of monitored metrics merely limits the field of use to a particular data environment.)
10. The method of claim 1, further comprising generating a confidence score for each of the RPO predictions of the RPO drifts based on a distribution of the RPO predictions of the RPO drifts from the deep learning model; (This reads on math.) and prioritizing a plurality of actions on the RPO drifts based on the confidence store for each of the RPO predictions of the RPO drifts. (This reads on mathematical/mental processes.)
11. The method of claim 1, further comprising predicting another recovery point objective (RPO) for another domain based on the RPO predictions of the RPO drifts in the domain. (The use for prediction of RPO merely limits to a particular environment. The application of predictions in one domain to another domain reads on transfer learning, which uses a pattern learning in one domain (environment or situation) and applies it to a similar domain. Using experience in one domain to make predictions about another similar domain reads on a mental process. (While this is not a basis for a rejection in this office action, in the event that the Office issues guidance before the next office action, when amending it may be helpful to consider that this claim is directed to the generic machine learning technique of transfer learning applied to the environment of RPO prediction.))
12. The method of claim 1, wherein the computing device includes software provided as a service in a cloud environment. (This merely recites using computing devices in their ordinary capacity. Further the “software provided as a service” aspect reads on a contractual relationship or a commercial interaction. See MPEP § 2104.06(a)(2)(II)(B) (“"Commercial interactions" or "legal interactions" include agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations.”))
For rejections of claims 14 and 16 see rejections of claims 6 and 10, respectively.
18. The system of claim 17, wherein the program instructions are further executable to: train the deep learning model based on the training data set using the at least one bidirectional LSTM layer; (See rejection of claim 10.) send the RPO predictions of the RPO drifts to a dashboard; and display the RPO predictions of the RPO drifts, tables of root causes, and graphs of monitored metrics on the dashboard for customer visualization. (See rejection of claim 9.)
For rejection of claim 20, see rejection of claim and 10.
21. The method of claim 1, wherein the at least one bidirectional LSTM layer comprises first and second bidirectional LSTM layers which allow for both backward and forward information in a sequence at every time interval and a dense layer which generates the RPO predictions of the RPO drifts. (This is merely an instruction to implement the abstract ideas of the independent claims using generic computer components including a generic BiLSTM with a dense layer.)
22. The method of claim 21, wherein the plurality of features include historical data points from a predetermined time period. (This merely limits to a data environment associated with a particular field of use.)
23. The method of claim 22, further comprising: evaluating the training data set to assign a confidence score; (Evaluating a training data set “to assign a confidence score” reads on a mental process and on math.) and generating a probable cause for the RPO drifts based on the assigned confidence score. (Generating a probable cause for the RPO drifts based on the confidence score reads on a mental process and on math.)
All dependent claims are rejected as containing the material of the claims from which they depend.
Response to Arguments
Applicant's arguments filed 30 March 2026 have been fully considered but they are not persuasive.
Rejections under § 112
All rejections under this section are withdrawn in response to claim amendments.
Rejections under § 101
Most of the arguments filed by Applicant on 11 December 2025 appear to have been copied into the Remarks filed 30 March 2026. In addition, the Applicant Remarks include new arguments asserting technological improvements resulting from RPO predictions. Responses to these arguments are addressed at the end of this section.
Applicant takes the position that the claims are eligible because they merely involve math without reciting a mathematical concept. In support of this position, Applicant cites Thales Visionix, Inc. v. United States, 850 F.3d 1343, 1348-49 (Fed. Cir. 2017). The court in Thales determined that the claims on appeal were patent eligible despite “utiliz[ing] mathematical equations” because they were directed to an “unconventional utilization of intertial sensors” that “reduces errors in an intertial system.” Thales at 1348. In other words, the claims were eligible because they were directed to an improvement of something other than the math used to implement the claimed invention. Further, the Thales Court describes this arrangement as “analogous to” claims expressly reciting the Arrhenius equation, indicating that omission of the equation in the claims was not dispositive. Thales at 1348. A fair reading of Thales might be that claims directed to an unconventional arrangement resulting in a technological improvement do not become ineligible for the sole reason that their implementation necessarily utilizes math. The claims of this application, in their current form, remain directed to the math recited in the claims with an instruction to use this math for modifying RPO’s. Without any specific limitations that one of ordinary skill would understand as improving the technological environment, it is not clear why the claims of this application would patent eligible under Thales.
Applicant lists various cases that recite different abstract ideas, and asserts that the claims in this application do not recite the same ideas. It is not clear what rule is being applied so it is unclear how to respond.
Applicant cites the 2019 and 2025 guidance. But nothing in this guidance is tied back to the claim language in a way that would support a finding that the claims are patent eligible. Note that merely claiming a machine learning model is not sufficient for a determination of patent eligibility under the guidance, simply because example 39 shows patent eligible claims directed to a model. See Rem. 10.
Applicant explains that the claims are directed to an improvement because they learn in one domain and generate predictions in another domain without the need for designing a model for the second domain from scratch. Rem 10. No specific operations recited in the claims are offered as providing this asserted improvement. Further, without any specific way of improving the way in which models trained in one domain can be implemented in a different domain, this merely describes a field of use. Using a model in another domain does not, in of itself, provide an improvement. Unsupported attorney arguments merely stating that the model will work well in the second domain is also insufficient for a finding that the claims are directed to an improvement. Applicant cites claim language that includes an assertion of improved operations. But merely stating that the claims are directed to “mitigat[ing] downtime” does not make it so. Similarly, Applicant’s assertion that implementations of the invention proactively achieve SLA’s with improved customer satisfaction lacks the factual support and technical reasoning that would be required for a finding that these asserted improvements result from the claimed combination. For an invention to be directed to an improvement, the claims must recite components or steps that one of ordinary skill in the art would understand as resulting in the improvement. Any connection between the components and steps recited in the claims and the asserted improvement is not addressed in the remarks.
The Remarks assert that the claimed invention is directed to improving “the technological environment” by using “early warnings of RPA related issues” to avoid “clients not being able to meet service level agreements and [] a decline in client satisfaction.” Rem. 9. Clearly, providing early warnings of problems so that they can be avoided or mitigated is an improvement. But it is not technological in nature, notwithstanding the invocation of a “technological environment.” Simply put, reciting generic training and utilization of a model to be used in a particular data environment is not, alone, a technological improvement.
The Remarks assert a technological improvement because claim 1 recites “perform[ing] preventative actions to mitigate downtime based on the RPO predictions of the RPO drifts.” But the invocation of generic “preventative actions” based on a prediction from a generic model is not, itself, a technological improvement. One of the more software-patent-friendly cases, McRO, helps clarify that patent eligibility based on particular rules resulting in the improvement to a technological process was different than merely using a computer as a tool. See MPEP § 2106.05(a)(II) (“The court relied on the specification's explanation of how the claimed rules enabled the automation of specific animation tasks that previously could not be automated. 837 F.3d at 1313, 120 USPQ2d at 1101. The McRO court indicated that it was the incorporation of the particular claimed rules in computer animation that "improved [the] existing technological process", unlike cases such as Alice where a computer was merely used as a tool to perform an existing process. 837 F.3d at 1314, 120 USPQ2d at 1102. The McRO court also noted that the claims at issue described a specific way (use of particular rules to set morph weights and transitions through phonemes) to solve the problem of producing accurate and realistic lip synchronization and facial expressions in animated characters, rather than merely claiming the idea of a solution or outcome, and thus were not directed to an abstract idea. 837 F.3d at 1313, 120 USPQ2d at 1101.”) The claims of this application are not analogous to those of McRO because they do not recite a specific set of rules that result in any particular way of predicting RPO drifts or any specific way of providing early warning. Rather, the claims use conventional computer components to make decisions and effectively automate predicting RPO drifts utilizing generic machine learning. Broadband iTV, Inc. v. Amazon.com, Inc., 113 F.4th 1359, 1370 (Fed. Cir. 2024). For the foregoing reasons, the rejection under this section is maintained.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAUL M KNIGHT whose telephone number is (571) 272-8646. The examiner can normally be reached Monday - Friday 9-5 ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michelle Bechtold can be reached on (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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PAUL M. KNIGHTExaminerArt Unit 2148
/PAUL M KNIGHT/Examiner, Art Unit 2148
1 This distinction between claims which read on math and claims which recite an abstract idea is based on official USPTO Guidance. The 2019 Subject Matter Eligibility (SME) Examples instructs examiners that a claim reciting “training the neural network” where the background describes training as “using stochastic learning with backpropagation which is a type of machine learning algorithm that uses the gradient of a mathematical loss function to adjust the weights of the network” “does not recite any mathematical relationships, formulas, or calculations.” See 2019 SME Example 39, PP. 8-9 (emphasis added). In this example, the plain meaning of “training the neural network” read in light of the disclosure reads on backpropagation using the gradient of a mathematical loss function. See MPEP § 2111.01. In contrast, the 2024 SME Examples instructs examiners that a claim reciting “training, by the computer, the ANN . . . wherein the selected training algorithm includes a backpropagation algorithm and a gradient descent algorithm” does recite an abstract idea because “[t]he plain meaning of [backpropagation algorithm and gradient descent algorithm] are optimization algorithms, which compute neural network parameters using a series of mathematical calculations.” 2024 PEG Example 47, PP. 4-6. The Memorandum of August 4, 2025; Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101, P. 3 also directs examiners that “training the neural network” recited in Example 39 merely “involve[s] . . . mathematical concepts” and contrasts claim 2 of example 47 as “referring to [specific] mathematical calculations by name[.]” (Emphasis added.)
2 “For instance, the claims in Diehr . . . clearly stated a mathematical equation . . . and the claims in Mayo . . . clearly stated laws of nature . . . such that the claims ‘set forth’ an identifiable judicial exception. Alternatively, the claims in Alice Corp. . . . described the concept of intermediated settlement without ever explicitly using the words ‘intermediated’ or ‘settlement.’” MPEP § 2106.04(II)(A).
3 “By grouping the abstract ideas, the examiners’ focus has been shifted from relying on individual cases to generally applying the wide body of case law spanning all technologies and claim types. . . . If the identified limitation(s) falls within at least one of the groupings of abstract ideas, it is reasonable to conclude that the claim recites an abstract idea in Step 2A Prong One.” MPEP § 2106.04(a). See also MPEP 2104(a)(2).
4 Step 2A prongs one and two are evaluated individually, consistent with the framework in the MPEP. Evaluation of relationships between abstract ideas and additional elements in one location promotes clarity of the record.
5 “In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. . . . It should be noted that while this consideration is often referred to in an abbreviated manner as the ‘improvements consideration,’ the word ‘improvements’ in the context of this consideration is limited to improvements to the functioning of a computer or any other technology/technical field, whether in Step 2A Prong Two or in Step 2B.” MPEP 2106.04(d)(1). See also Koninklijke KPN N.V. v. Gemalto M2M GmbH, 942 F.3d 1143, 1150-1152 (Fed. Cir. 2019).
6 See MPEP § 2106.05(d)(II) listing operations including “receiving or transmitting data,” “storing and retrieving data in memory,” and “performing repetitive calculations” as WURC. “The claims at issue do not require any nonconventional computer, network, or display components, or even a non-conventional and non-generic arrangement of known, conventional pieces, but merely call for performance of the claimed information collection, analysis, and display functions on a set of generic computer components and display devices.” Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1355 (Fed. Cir. 2016) (emphasis added, internal quotes omitted).
7 “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). Here, the server simply receives data, ‘extract[s] classification information . . . from the received data,’ and ‘stor[es] the digital images . . . taking into consideration the classification information.’ See ‘295 patent, col. 10 ll. 1-17 (Claim 17). . . . 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 Commc'ns LLC v. AV Auto., LLC, 823 F.3d 607, 614 (Fed. Cir. 2016), Emphasis Added.
8 “The analysis as to whether an element (or combination of elements) is widely prevalent or in common use is the same as the analysis under 35 U.S.C. 112(a) as to whether an element is so well-known that it need not be described in detail in the patent specification. See Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1377, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (supporting the position that amplification was well-understood, routine, conventional for purposes of subject matter eligibility by observing that the patentee expressly argued during prosecution of the application that amplification was a technique readily practiced by those skilled in the art to overcome the rejection of the claim under 35 U.S.C. 112, first paragraph)[.]” MPEP § 2106.05(d)(I).
9 “Similarly, claim elements or combinations of claim elements that are routine, conventional or well-understood cannot transform the claims. (Citing BSG Tech LLC v. BuySeasons, Inc., 899 F.3d 1281, 1290-1291 (Fed. Cir. 2018)). When the patent's specification ‘describes the components and features listed in the claims generically,’ it ‘support[s] the conclusion that these components and features are conventional.’ Weisner v. Google LLC, 51 F.4th 1073, 1083-84 (Fed. Cir. 2022); see also Beteiro, LLC v. DraftKings Inc., 104 F.4th 1350, 1357-58 (Fed. Cir. 2024).” Broadband iTV, Inc. v. Amazon.com, Inc., 113 F.4th 1359 (Fed. Cir. 2024)
10 “If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology.” MPEP § 2106.05(a).
Prosecution Insights