CTNF 18/389,273 CTNF 90534 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 . Information Disclosure Statement All information disclosure statements were submitted prior to the first action and are incompliance with the provisions of 37 C.F.R. § 1.97. Accordingly, they have been considered. Closest Prior Art No rejection is given under 35 U.S.C. §§ 102 or 103, because no reasonable construction of the claims read on the art found during search. It is noted here, that the claims are unclear for several reasons making claim interpretation speculative. In an attempt at compact prosecution, a full search was conducted, but no art was found teaching, for instance “ the loss function is defined to pessimistically estimate a loss with respect to uncertainty of the nuisance model by using a worst value within a range in which the nuisance model is more certain than a predetermined value. ” Similarly, no art was found teaching “ a loss function partially including a nuisance model which is an estimation object not necessarily as a final output [.]” Note however, clarifying amendments may result in a prior art rejection because clarification may result in a better understanding of the invention. “[A]n examiner should not simply speculate about the meaning of the claim language and then enter an obviousness rejection in view of that speculative interpretation. In re Steele , 305 F.2d 859,134 USPQ 292 (CCPA 1962)[.]” MPEP § 2143.03. The following is a list of the closest prior art: Shi (Adapting Neural Networks for the Estimation of Treatment Effects; 2019) teaches “First, we fit models for the conditional outcome Q(t, x) = [Y |t, x] and the propensity score g(x) = P(T = 1|x). Then, we plug these fitted models into a downstream estimator. The strong predictive performance of neural networks motivates their use for effect estimation [e.g. Sha+16; Joh+16; Lou+17; AS17; Ala+17; Sch+18; Yoo+18; Far+18]. We will use neural networks as models for the conditional outcome and propensity score.” Shi p. 1. Shiba (Using Propensity Scores for Causal Inference: Pitfalls and Tips, 2021) Ghosh (Propensity score synthetic augmentation matching using generative adversarial networks (PSSAM-GAN); July 2021) Dalli 2022/0398460 ¶81. 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 § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 AIA Claim s 1-8 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention. Generally : separately listed claim elements are construed as distinct components, that all claim terms must be given weight, there is presumed to be a difference in meaning and scope when different words or phrases are used in separate claims, and repeated and consistent descriptions in the specification indicate the proper scope of a claimed term. “[C]laims must ‘conform to the invention as set forth in the remainder of the specification and the terms and phrases used in the claims must find clear support or antecedent basis in the description so that the meaning of the terms in the claims may be ascertainable by reference to the description.’ 37 C.F.R. § 1.75(d)(1).” Phillips v. AWH Corp. , 415 F.3d 1303, 1316 (Fed. Cir. 2005) (as cited in MPEP § 2111). Therefore, use of two different terms in the claims that both rely on the description of a single structure in the Specification may render at least one term indefinite because there is no way to determine which term should be construed in view of the description of the single structure. All independent claims recite “ a processor configured to execute the instructions to: . . . learn a model [.]” Generally, in English, models are “trained” using executable instructions. Executing instructions to “learn a model” is inconsistent with conventional English usage of the term “learn.” Where machine translations are used, this type of unique phrasing may be avoided in the future, by reading and editing any machine translation of the claims before filing an application with the Office. While models can be said to “learn,” this refers to internal processes, not a process carried out by a processor on a model (i.e. the processer or instructions train or teach the model, whereby the model learns.) This phrasing is unclear in English because the only plausible interpretation requires replacing “learn” with “teach” or “train.” It is submitted that claims which require mental editing before the meaning of the claims is understood, are indefinite. Further, the use of “learn” is problematic because “the instructions to: . . . learn a model” imply instructions are training the model, but “learn” implies something the model is doing itself. This makes it unclear whether the learning is a result of the instructions. In any case, using terms in their ordinary and conventional manner consistent with English syntax, such as using “train” when referring to operations that cause a model to learn will overcome this rejection. All independent claims substantially recite “ based on a loss function partially including a nuisance model which is an estimation object not necessary as a final output [.]” It is not clear whether the loss function or the nuisance model “is an estimation object not necessarily as a final output.” While a loss function is generally not itself an output, it is less clear what would be meant by a model that could be a final output. Since neither the loss function or the nuisance model is generally considered itself to be something which can be “a final output,” either option is equally unlikely rendering the claim language ambiguous. It is again noted that, where machine translations are used, this type of unique phrasing may be avoided in the future by reading and editing any machine translation of the claims before filing an application with the Office. Note also that “not necessarily as a final output” is itself ambiguous because “not necessarily” implies without requiring the existence of “a final output.” See MPEP § 2173.05b. Also, it is not clear what is meant by an “estimation object[.]” This appears to be an applicant invented term, but the terms is not defined in the Specification or used in a way that would impart any objective meaning. The use of mere indistinct words in the claims does not satisfy the definiteness requirement. All independent claims substantially recite “ a loss function partially including a nuisance model [.]” Presumably, this refers to a loss function that includes an output from a nuisance model. But the plain meaning indicates a model itself is within the loss function. Unless some explanation in the Specification was overlooked, this relationship seems to be nonsensical. Since the one interpretation is partially inconsistent with the plain meaning of the claim language, and the second interpretation, as best understood, appears to refer to a nonsensical combination, the claim language is indefinite. See also Spec. ¶20, explaining that a value of the weight predicted by a model trained using supervised learning, is substituted into the loss function. All independent claims substantially recite a “ wherein the loss function is defined to pessimistically estimate a loss with respect to uncertainty of the nuisance model by using a worst value within a range in which the nuisance model is more certain than a predetermined value. ” Whether or not a value is “worst” is subjective. From the context of the claim, the “worst” value could be the least certain input value above some threshold (i.e. an argmin value above a threshold). Alternatively, the worst value could refer to the input value associated with the highest cost/loss value in the cost/loss function. Neither is clearly claimed so it is not clear which is meant. Further, the term “worst” is inherently subjective, potentially leaving open other subjective interpretations. See MPEP § 2173.05b. Claim 2 recites “ the processor optimizes the nuisance model and the loss function simultaneously and adversarially. ” Models are commonly configured to be optimized adversarially using loss functions. But it is not clear what is meant by a model and a loss function being optimized simultaneously and adverbially. First, it is unclear what is meant by a “optimiz[ing] . . . a loss function.” Generally, the loss function generates a loss value that is used to optimize a model. But this language appears to be directed to creation of an optimal loss function by somehow setting up the loss function and another model in an adversarial configuration. Unless some explanation has been overlooked, this appears to be non-sensical. The specification, as best understood, describes two terms that work against each other within the loss function, where one term is the output of the nuisance model. See e.g. Spec. ¶44. Claim 3 recites “ using a loss function related to the nuisance model and a loss function related to the model for performing the causal inference.” Since claim 1 has already introduced “a loss function,” it is not clear if either or both of the loss functions in claim 1 refer to the same claim element as claim 1. Further, a claim term is generally construed as referring to a single claim element. Here, it appears that “a/the loss function” is being used to refer to two or three different loss functions. Using a different term for each function (i.e. first/second loss function) allows the claim drafter clearly distinguish between different claim elements. Claim 4 recites “ wherein the loss function includes the nuisance model as a weight .” It is not clear what is meant by inclusion of a nuisance model in the loss function. While inclusion of the output of the model in the loss function would be clear, it must be noted that a “weight” in machine learning generally refers to a scalar attached to an activation function. If something is being multiplied the output of the nuisance model, merely denoting the inclusion “as a weight” is insufficient. Note again, that this type of renaming claim elements where the nuisance model is now a “nuisance model as a weight” is unconventional in domestic claim drafting because it does not require any specific structure or require steps operations to be performed. It is suggested that a clear articulation of the relationship between the loss function and the nuisance model may overcome this rejection. Claim 5 recites “ wherein the loss function calculates a weighted loss using the nuisance model as a weight for the loss. ” This claim is indefinite for substantially the same reasons as the rejection of claim 4. Claim 6 recites “ wherein the loss function includes estimation of conditional causal effects by the model for performing the causal inference. ” It is not clear whether “estimation” is singular or plural. For terms that are singular, the indefinite article is required the first time the term is used to provide antecedent basis for subsequent recitations of the term (i.e. “an estimation.”) All dependent claims are rejected as containing the limitations of the claims from which they depend. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-8 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. 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 learning device comprising: a memory configured to store instructions; and a processor configured to execute the instructions to: (This is merely an instruction to apply the judicial exception using generic computer components.) acquire learning data including an explanatory variable, an action, and information of outcome of the action; (Acquiring learning data is extra-solution activity.) and learn a model for performing causal inference, using the learning data, based on a loss function partially including a nuisance model which is an estimation object not necessary as a final output, (Using a loss function to “learn a model” reads on mathematical operations. This claim is directed to details of a loss function and operations carried out using the loss function. A loss function is a mathematical relationship. While specific details of a loss function may be part of an inventive concept where they provide an improvement to the technological area of machine learning, it is not clear how the claimed operations result in the improvements described in the Specification. See Spec. ¶18. It is noted that the Specification appears to describe a way of using a modified cost function to improve accuracy of an inference model. See Spec. ¶18. A clear connection between operations in the claims and an improvement of to the accuracy of a model may be a way forward. It is noted that the translation of the original application is somewhat difficult to understand, so merely pasting sections of the Specification without a clear connection between claim operations and an improvement is unlikely to be persuasive. However, an improvement to the model itself based on a specific cost function may constitute an improvement to the technology.) wherein the loss function is defined to pessimistically estimate a loss with respect to uncertainty of the nuisance model by using a worst value within a range in which the nuisance model is more certain than a predetermined value. (This further limits the abstract math in the loss function.) Independent claim 7 is rejected for the reasons given in the rejection of claim 1. Independent claim 8 is rejected for the reasons given in the rejection of claim 1. The claim also recites “ A non-transitory computer-readable recording medium recording a program, the program causing a computer to execute processing comprising: ” the operations of claim 1. This is merely an instruction to apply the judicial exception using generic computer components. 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. All independent claims substantially recite “ acquire learning data including an explanatory variable, an action, and information of outcome of the action [.]” Acquiring learning data is WURC. Should any other claim limitations be rejected at step 2A1 as extra-solution activity but omitted in the section directly above, 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 learning device according to claim 1, wherein the processor optimizes the nuisance model and the loss function simultaneously and adversarially. (This recites mathematical operations based on a loss function.) 3. The learning device according to claim 1, wherein the processor performs learning using a loss function related to the nuisance model and a loss function related to the model for performing the causal inference. (This recites mathematical operations based on a loss function.) 4. The learning device according to claim 1, wherein the loss function includes the nuisance model as a weight. (This recites mathematical operations and relationships related to a loss function.) 5. The learning device according to claim 1, wherein the loss function calculates a weighted loss using the nuisance model as a weight for the loss. (This merely further limits the abstract math associated with the loss function.) 6. The learning device according to claim 1, wherein the loss function includes estimation of conditional causal effects by the model for performing the causal inference. (This merely further limits the abstract math associated with the loss function.) All dependent claims are rejected as containing the material of the claims from which they depend. Conclusion 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. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michelle Bechtold can be reached on (571. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. PAUL M. KNIGHT /PAUL M KNIGHT/ Primary Examiner, Art Unit 2148 Application/Control Number: 18/389,273 Page 2 Art Unit: 2148 Application/Control Number: 18/389,273 Page 3 Art Unit: 2148 Application/Control Number: 18/389,273 Page 4 Art Unit: 2148 Application/Control Number: 18/389,273 Page 5 Art Unit: 2148 Application/Control Number: 18/389,273 Page 6 Art Unit: 2148 Application/Control Number: 18/389,273 Page 7 Art Unit: 2148 Application/Control Number: 18/389,273 Page 8 Art Unit: 2148 Application/Control Number: 18/389,273 Page 9 Art Unit: 2148 Application/Control Number: 18/389,273 Page 10 Art Unit: 2148 Application/Control Number: 18/389,273 Page 11 Art Unit: 2148 Application/Control Number: 18/389,273 Page 14 Art Unit: 2148 Application/Control Number: 18/389,273 Page 15 Art Unit: 2148 Application/Control Number: 18/389,273 Page 16 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).
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