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 method, comprising: receiving, by a device, time series data from one or more internet of things (IoT) devices; (Sending and receiving data is mere extra-solution activity used in conjunction wth the abstract ideas recited below.) defining, by the device, a first quantity of steps into past data utilized to make future predictions associated with one or more parameters associated with the one or more IoT devices; (Defining “by the device” is an instruction to apply the mental process and/or mathematical operation of “defining . . . a first quantity of steps onto the past data utilized to make future predictions” on generic computer components. This is not repeated for each recitation of “the device” in subsequent claim limitations. It should be understood that any claim language directed to implementation of mental or mathematical operations on generic computer components is a mere instruction to apply an exception. Requiring the “future preditions” to be “associated with one or more parameters associated with the one or more IoT devices” merely limits to a field of use.) defining, by the device, a second quantity of steps into the future predictions; defining, by the device, a third quantity of steps to skip in the future predictions; (The defining the second and third “quantity of steps” are mental processes and/or mathematical operations, similar to defining the first quantity of steps.) determining, by the device, whether the second quantity is equal to the third quantity; (This reads on a mental process/mathematical operation.) and selectively: (The claimed selection reads on a mental process.) processing, by the device, the time series data, with each of a plurality of machine learning models, to generate a plurality of future predictions that do not overlap based on the second quantity being less than or equal to the third quantity, (Using time series data to generate figure prediction that do not overlap based on the second quantity being equal to the third quantity is merely an instruction to apply an exception using a generic model. The selective processing based on the relative second and third quantity also reads on a mental process and/or mathematical operation, implemented using generic computing components.) merging, by the device, the plurality of future predictions into a list of future predictions, (This reads on a mental process carried out “by the device.) providing, by the device, the list for display, and (This is mere extra-solution activity.) causing the one or more IoT devices to modify the one or more parameters based on the plurality of future predictions to prevent potential issues associated with the one or more IoT devices from occurring; or processing, by the device, the time series data, with each of the plurality of machine learning models, to generate another plurality of future predictions that do overlap based on the second quantity not being less than or equal to the third quantity. (This reads on an instruction to use generic computing components to implement a mental process/mathematical operation. Note that the newly amended langauge is written as an optional limitation, with the claims only require the “causing” step or the “processing” step. In the interest of compact prosecution, note that modifying one or more paremeters based on predictions reads on a mental process and on mathematical operations. The language “to prevent . . .” is written as an intended use. Intended use language is explained in MPEP §§ 2103 and 2111.02. “Claim scope is not limited by claim language that suggests or makes optional but does not require steps to be performed, or by claim language that does not limit a claim to a particular structure.” MPEP § 2111.04.)
8. A device, comprising: one or more memories; and one or more processors to: (This is merely an instruction to apply the abstract ideas recited below, using generic computing components.) receive time series data from one or more Internet of things (IoT) devices; define a first quantity of steps into past data utilized to make future predictions associated with one or more parameters associated with the one or more IoT devices; define a second quantity of steps into the future predictions; define a third quantity of steps to skip in the future predictions; determine whether the second quantity is less than or equal to the third quantity; and selectively: process the time series data, with each of a plurality of machine learning models, to generate a plurality of future predictions that do not overlap based on the second quantity being less than or equal to the third quantity, merge the plurality of future predictions into a list of future predictions, cause the one or more IoT devices to modify the one or more parameters based on the plurality of future predictions to prevent potential issues associated with the one or more IoT devices from occurring; or process the time series data, with each of the plurality of machine learning models, to generate another plurality of future predictions that do overlap based on the second quantity not being less than or equal to the third quantity, (See rejection of claim 1.) average values of the other plurality of future predictions that overlap to generate averaged future predictions, (See rejection of claim 2.) merge the other plurality of future predictions, based on the averaged future predictions, into another list of future predictions, (See rejection of claim 3.) and perform one or more actions based on the list or the other list. (This is merely a generic instruction to apply the exception.)
15. A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to: (This is merely an instruction to apply the abstract ideas recited below, using generic computing components.) receive time series data from one or more Internet of things (IoT) devices; define a first quantity of steps into past data utilized to make future predictions associated with one or more parameters associated with the one or more IoT devices; define a second quantity of steps into the future predictions; define a third quantity of steps to skip in the future predictions; determine whether the second quantity is less than or equal to the third quantity; selectively: process the time series data, with each of a plurality of machine learning models, to generate a plurality of future predictions that do not overlap based on the second quantity being less than or equal to the third quantity, merge the plurality of future predictions into a list of future predictions and cause the one or more IoT devices to modify the one or more parameters based on the plurality of future predictions to prevent potential issues associated with the one or more IoT devices from occurring; or process the time series data, with each of the plurality of machine learning models, to generate another plurality of future predictions that do overlap based on the second quantity not being less than or equal to the third quantity, (See rejection of claim 1.) average values of the other plurality of future predictions that overlap to generate averaged future predictions, (See rejection of claim 2.) merge the other plurality of future predictions, based on the averaged future predictions, into another list of future predictions, and provide the list or the other list for display. (See rejection of claim 3.)
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 following limitations have been found to recite extra-solution activity:
In claim 1: “receiving, by a device, time series data from one or more internet of things (IoT) devices[.]”
In claim 1: “providing, by the device, the list for display[.]”
In claim 3 (below): “providing the other list for display[.]”
The above limitations are determined to be WURC based on cases which have determined that basic I/O and display operations are mere WURC.
This finding is based on cases which have recognized that generic input-output operations, repetitive processing operations, and storage operations are WURC.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
Dependent Claims:
2. The method of claim 1, further comprising: averaging values of the other plurality of future predictions that overlap to generate averaged future predictions. (This reads on a mental process and/or mathematical operations.)
3. The method of claim 2, further comprising: merging the other plurality of future predictions, based on the averaged future predictions, into another list of future predictions; (This reads on a mental process and/or mathematical operations.) and providing the other list for display. (Displaying results is mere extra solution activity.)
4. The method of claim 3, wherein merging the other plurality of future predictions, based on the averaged future predictions, into the other list of future predictions comprises: applying a weight to each future prediction of the other plurality of future predictions to generate a plurality of weighted future predictions; and combining the plurality of weighted future predictions to generate the other list of future predictions. (This reads on a mental process and/or mathematical operations.)
5. The method of claim 1, wherein the time series data is telemetry data received from the one or more IoT devices. (This merely limits the field of use to the corresponding data environment.)
6. The method of claim 1, wherein the first quantity of steps includes steps into the past data utilized to make the second quantity of steps into the future predictions. (This further limits the mental processes and mathematical operations.)
7. The method of claim 1, wherein the third quantity of steps to skip includes steps skipped in the second quantity of steps into the future predictions. (This further limits the mental processes and mathematical operations.)
9. The device of claim 8, wherein the plurality of machine learning models are trained to make the plurality of future predictions. (This is merely a generic instruction to apply the exception.)
10. The device of claim 8, wherein each of the plurality of machine learning models includes one of: an autoregressive integrated moving average machine learning model, a long short-term memory machine learning model, or a probabilistic machine learning model. (This is merely instruction to implement the abstract ideas using one of a list of generic computing components in the form of generic models.)
11. The device of claim 8, wherein the plurality of machine learning models includes different types of machine learning models. (This is merely instruction to implement the abstract ideas using generic computing components in the form of generic models.)
12. The device of claim 8, wherein one or more of the plurality of machine learning models generating the plurality of future predictions at a greatest future time include probabilistic machine learning models. (This is merely an instruction to implement the claimed abstract ideas using generic computer components.)
13. The device of claim 8, wherein the one or more processors are further to: train the plurality of machine learning models to perform different tasks. (This is merely an instruction to apply a judicial exception using generic computer components.)
14. The device of claim 8, wherein the one or more processors, to merge the plurality of future predictions into the list of future predictions, are to: apply a weight to each future prediction of the plurality of future predictions to generate a plurality of weighted future predictions; and combine the plurality of weighted future predictions to generate the list of future predictions. (This reads on a mental process and on mathematical operations.)
16. The non-transitory computer-readable medium of claim 15, wherein the plurality of machine learning models are trained to make the plurality of future predictions. (See rejection of claim 9.)
17. The non-transitory computer-readable medium of claim 15, wherein each of the plurality of machine learning models includes one of: an autoregressive integrated moving average machine learning model, a long short-term memory machine learning model, or a probabilistic machine learning model. (See rejection of claim 10.)
18. The non-transitory computer-readable medium of claim 15, wherein the plurality of machine learning models includes different types of machine learning models. (See rejection of claim 11.)
19. The non-transitory computer-readable medium of claim 15, wherein one or more of the plurality of machine learning models generating the plurality of future predictions at a greatest future time include probabilistic machine learning models. (See rejection of claim 12.)
20. The non-transitory computer-readable medium of claim 15, wherein the one or more instructions further cause the device to: train the plurality of machine learning models to perform different tasks. (See rejection of claim 13.)
All dependent claims are rejected as containing the material of the claims from which they depend.
Response to Arguments
Applicant's arguments filed 12/10/2025 have been fully considered but they are not persuasive.
No specific arguments are put forth in the Remarks. It is submitted that the claims fail to encompass the operations described in the Specification as providing the asserted improvement. The Specification describes the benefits of combining overlapping time series data used for long term forecasting see Spec. ¶¶11-12 and 26-27, and indicates these predictions may be used to more accurately update IoT device parameters, among other uses. See Spec. ¶¶24 and 26-27. The claims fail to require this combination of operations. That is, the claims fail to recite using the combined overlapping data (e.g. the averaged future predictions of claim 2) to in the “causing the one or more IoT devices to modify the one or more parameters” of claim 1. This applies to all claims in their current form. “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).
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.
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) 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.
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).