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 1-20 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 computer-implemented method, executed on a computing device, comprising: (This is a mere instruction to apply the subsequently claimed abstract ideas using conventional computer components. Throughout this office action, it should be understood that the use of generic computer components to implement an abstract idea are determined to be mere instructions to apply the abstract ideas.) processing a plurality of input/output (IO) requests associated with a plurality of storage objects of a storage system; (Processing I/O requests is mere extra-solution activity.) dividing the plurality of storage objects into a plurality of classes using a classification-based machine learning model; (Dividing objects into classes reads on a mental process. Using a “classification-based machine learning model” to implement the mental process is a mere instruction to implement the mental process using conventional computer components.) and forecasting a temperature for each storage object based upon, at least in part, the plurality of classes. (Forecasting the temperature for each storage object based on the classes reads on a mental process. Taken as a whole, the claim is directed to the abstract mental process of classifying storage objects (i.e. files) based on some parameter and forecasting future file access rates, and instruction to implement this process by utilizing generic computer components, including a generic model.)
Independent claim 8 is rejected for the reasons given in the rejection of claim 1. The claim also recites “[a] computer program product residing on a non-transitory computer readable medium having a plurality of instructions stored thereon which, when executed by a processor, cause the processor to perform” the operations of claim 1. This is merely an instruction to apply the judicial exception on a computer.
Independent claim 15 is rejected for the reasons given in the rejection of claim 1. The claim also recites “A computing system comprising: a memory; and a processor configured to” carry out the operation of claim 1. This is merely an instruction to apply the judicial exception on a computer.
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. The independent claims recite the following limitations, which read on operations that have been found to be WURC: “processing a plurality of input/output (IO) requests associated with a plurality of storage objects of a storage system[.]” 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 computer-implemented method of claim 1, wherein dividing the plurality of storage objects into the plurality of classes includes generating a plurality of IO features using the plurality of IO requests. (The “IO features” are described in the Specification as various types of I/O operations (i.e. IOPS, read I/O, write I/O.) The claimed “dividing,” as best understood, refers to classifying the various I/O operations based on type. This reads on a mental process.)
3. The computer-implemented method of claim 2, wherein the plurality of IO features include one or more of a number of IO requests per second (IOPS); a total number of read IO requests; a total number of write IO requests; a percentage of sequential read IO requests; and a percentage of sequential write IO requests. (This merely limits the data environment to a particular field of use.)
4. The computer-implemented method of claim 2, wherein dividing the plurality of storage objects into the plurality of classes (This reads on a mental process.) includes processing the plurality of IO features using the classification-based machine learning model. (This is merely an instruction to implement the mental process using generic computer components.)
5. The computer-implemented method of claim 1, wherein dividing the plurality of storage objects into a plurality of classes (This reads on a mental process.) using a classification-based machine learning model (This is merely an instruction to implement the mental process using generic computer components.) includes determining a class probability for each storage object. (This reads on a mental process.)
6. The computer-implemented method of claim 5, further comprising: tiering each storage object of the plurality of storage objects into a tier of a plurality of tiers based upon, at least in part, the plurality of classes. (The “tiering” is interpreted here as the decision to rank a given storage object, not necessarily the actual moving of the storage object to a more quickly accessible location. Based on this interpretation, the claimed “tiering” reads on a mental process. Note that merely moving the object in response, reads on a mere instruction to apply the mental process. Basing tiering on the “plurality of classes” also reads on a mental process of using the class of the object to determine the tier.)
7. The computer-implemented method of claim 6, wherein tiering each storage object of the plurality of storage objects is further based upon, at least in part, the class probability for each storage object. (Basing the tiering the class probability for each storage object reads on a mental process. Further, this reads on the mathematical process of statistical estimation.)
For rejections of claims 9-14, see rejections of claims 2-7.
For rejections of claims 16-20, see rejections of claims 2-6.
All dependent claims are rejected as containing the material of the claims from which they depend.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 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 substantially recite “dividing the plurality of storage objects into a plurality of classes using a classification-based machine learning model[.]”All claim terms must be given weight. It is not clear how to weight the language “classification-based” as modifying the claimed “machine learning model.” This language could be interpreted to limit to models that provides classification for some input, but this would make the language redundant with “dividing the plurality of storage objects into a plurality of classes using a . . . machine learning model,” effectively reading the language out of the claims. This makes the claim unclear because the claim language purports to limit the model to a particular structure, while ultimately repeating separately recited function, without indicating how the structure should be limited. Since it cannot be determined how a “classification-based model” model would be different than an ordinary “model” used to divide storage objects into classes, the claim language is indefinite. This rejection may be overcome by deleting “classification-based.” Alternatively, if some specific structural limitation to the model is meant, the specific structural limitation may be amended into the claims (e.g. indicating that the model has multiple outputs, each indicating a different classification), assuming support.
All independent claims substantially recite “dividing the plurality of storage objects into a plurality of classes using a classification-based machine learning model[.]” Claims 6 (13 and 20) substantially recite “tiering each storage object of the plurality of storage objects into a tier of a plurality of tiers based upon, at least in part, the plurality of classes[.]” It is not clear how “dividing the plurality of storage objects into . . . classes” is distinguished from “tiering each storage object . . . into a tier of a plurality of tiers.” Tiering can refer to moving the storage object, but the BRI of “tiering” also reasonably reads on merely classifying the storage object for relation to a given tier. This second meaning would render the separately claimed “dividing the plurality of storage objects into a plurality of classes” superfluous because both limitations would have the same scope. That is, both limitations would refer to classifying storage objects based on anticipated I/O operations. But the use of separate terms in a claim implies distinct claim elements (or operations.) Similarly, there is a presumed difference in meaning and scope when different terms are used in separate claims. Since the dependent claims use a separate term, “tiering,” which overlaps with the claimed “dividing the plurality of storage objects into a plurality of classes,” there is no internally consistent way to interpret “dividing . . . into classes” and “tiering” while adhering to conventional rules of claim interpretation. Note that narrowing the meaning of “tiering” to require moving the data object would be inconsistent with applying BRI. This rejection may be overcome by clarifying that “tiering” refers to the operation of moving the data storage objects to a given storage tier. (Note that paragraph 76 provides support for amending the claims in this way.)
All dependent claims are rejected as containing the limitations of the claims from which they depend.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Liu (RLTiering: A Cost-Driven Auto-Tiering System for Two-Tier Cloud Storage Using Deep Reinforcement Learning, 19 December 2022 (See notes below column 1 on p. 501)) and Herodotou (Automating Distributed Tiered Storage Management in Cluster Computing, 2019).
1. A computer-implemented method, executed on a computing device, comprising: processing a plurality of input/output (IO) requests associated with a plurality of storage objects of a storage system; (“For a data object, the state st at time t describes its information including the object size V, the storage tier e(t), the number of GET requests g(t), the number of PUT requests p(t) and the size of retrieved data r(t).” Liu p. 508. “Hence, in the state space S, we define the state at time t, i.e., s(t) E S, as the data object information in the past tau slots” Liu p. 508. Note that this defines the state to include some set of get and put operations, where the size of the set depends on tau.) dividing the plurality of storage objects into a plurality of classes using a classification-based machine learning model; (With respect to claim interpretation, note that the recited “classification-based model” is not described in the Specification in such a way as to limit to a particular model structure. See Spec. ¶55 (“A classification-based machine learning model (e.g., machine learning model426) is a machine learning model configured to classify storage objects into classes based on the plurality of IO requests processed for that storage object.”) Liu teaches “State. For a data object, the state st at time t describes its information including the object size V, the storage tier e(t), the number of GET requests g(t), the number of PUT requests p(t) and the size of retrieved data r(t).” Liu p. 508. Note that GET and PUT refer to I/O operations. Note also that the decision to locate a given object on a given tier would be understood by one of ordinary skill as dividing the objects into classes based on anticipated use.
Liu does not expressly teach the techniques in the reference to a plurality of storage objects. While one of ordinary skill in the art would draw the inference that the method of Liu is being applied to a plurality of storage objects, a secondary reference is cited for an express teaching here, in the interest of compact prosecution.
Herodotou teaches “The above two observations have motivated our approach of modeling file access patterns and creating a feature-based classifier to predict whether a file will be accessed in the near future (and hence, should be upgraded) or it has become cold (and hence, should be downgraded). The overall approach comprises data preparation, normalization, online incremental training, and binary classification with gradient boosted trees (discussed next).” Herodotou P. 5. “Specifically, an XGBoost model will return a probability score indicating how likely is f to be accessed in the next w minutes. . . . The probability score is then used by the policies to decide which file(s) to upgrade or downgrade.” Herodotou p. 7. “[W]e maintain the last k access times for each file[.]” Herodotou p. 5. (For tiering recited in dependent claims 6-7, note also that the scoring (classification) of files is a separate operation from the operation of upgrading/downgrading.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Herodotou because classification of multiple data objects allows tiering of multiple data objects, thereby improving system capacity.) and forecasting a temperature for each storage object based upon, at least in part, the plurality of classes. (An action at time t indicates the storage tier at the next time slot which the agent believes will lead to cost effectiveness. Actions come from the space of storage tiers. Hence, the action space is denoted as [Equation 20] where at = 1 and at = 0 respectively indicate that the agent decides to store a data object in the hot tier or the cold tier at the next time slot, after observing the state st at the end of t. Data migration occurs when the action is different from the current storage tier at t, i.e., at at /= et.)
2. The computer-implemented method of claim 1, wherein dividing the plurality of storage objects into the plurality of classes includes generating a plurality of IO features using the plurality of IO requests. (“For a data object, the state st at time t describes its information including the object size V, the storage tier e(t), the number of GET requests g(t), the number of PUT requests p(t) and the size of retrieved data r(t).” Liu p. 508. “Hence, in the state space S, we define the state at time t, i.e., s(t) E S, as the data object information in the past tau slots” Liu p. 508. Note that this defines the state to include some set of get and put operations, the size of which depends on tau.)
3. The computer-implemented method of claim 2, wherein the plurality of IO features include one or more of a number of IO requests per second (IOPS); a total number of read IO requests; a total number of write IO requests; a percentage of sequential read IO requests; and a percentage of sequential write IO requests. (See rejection of claim 2 teaching “the number of GET request” and “the number of PUT requests.” See also Liu p. 508.)
4. The computer-implemented method of claim 2, wherein dividing the plurality of storage objects into the plurality of classes includes processing the plurality of IO features using the classification-based machine learning model. (See rejection of claim 1. Note that the decision to choose a given tier teaches “classification” of the object to be located on a given tier.)
5. The computer-implemented method of claim 1, wherein dividing the plurality of storage objects into a plurality of classes using a classification-based machine learning model includes determining a class probability for each storage object. (“State Transition Probability P. The state transition probability indicates the probabilities that the environment transits from one state to another. For example, at time t, the environment receives an action at from the agent after its state st is observed and then transits from st to st+1 with the probability P(st+1 | st, at). In our scenario, the storage tier of a data object is determined with certainty after the execution of an action. Thus, P(st+1 | st, at). is always 1 for st, st+1 E S and at E A.” Liu p. 509 col. 1. Note that, given a location in one tier, the probability that the data object moves to another tier reads on the “class probability” of being located on a given tier in the next cycle of the algorithm.)
6. The computer-implemented method of claim 5, further comprising: tiering each storage object of the plurality of storage objects into a tier of a plurality of tiers based upon, at least in part, the plurality of classes. (In our scenario, the storage tier of a data object is determined with certainty after the execution of an action. Thus, P(st+1 | st, at). is always 1 for st, st+1 E S and at E A.” Liu p. 509 col. 1. See also rejection of claim 5 for context. Note that the claimed action refers to moving the data object to the tier based on the state transition probability.)
7. The computer-implemented method of claim 6, wherein tiering each storage object of the plurality of storage objects is further based upon, at least in part, the class probability for each storage object. (In our scenario, the storage tier of a data object is determined with certainty after the execution of an action. Thus, P(st+1 | st, at). is always 1 for st, st+1 E S and at E A.” Liu p. 509 col. 1. See also rejection of claim 5 for context. Note that the claimed action refers to moving the data object to the tier based on the state transition probability.)
For rejections of claims 8-14, see rejections of claims 1-7.
For rejections of claims 15-20, see rejections of claims 1-6.
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.
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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