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 .
DETAILED ACTION
Status of the Application
The following is a non-Final Office Action.
In response to Examiner's communication of 1/30/2026, Applicant responded on 4/2/2026. Amended claims 21, 22, 27, 28, 31, 32, 36, and 37. Cancelled claims 39 and 40. Added claims 41 and 42.
Claims 21-38, 41, and 42 are pending in this application and have been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/2/2026 has been entered.
Response to Amendment
Applicant's amendments to claims 21, 22, 27, 28, 31, 32, 36, and 37 are not sufficient to overcome the 101 rejections set forth in the previous action.
Applicant's amendments to claims 21, 22, 27, 28, 31, 32, 36, and 37 are sufficient to overcome the prior art rejections set forth in the previous action.
Response to Arguments – 35 USC § 101
Applicant’s arguments with respect to the rejections have been fully considered, but they are not persuasive.
Applicant submits, “…amended independent claim 21 recites a two-stage computer-implemented process for training a particular neural network and then implementing that particular neural network at runtime.…the claim read as a whole cannot practically be performed in the human mind and thus is not directed to a mental process (see Office Action at p.16 alleging the contrary). Here, the claimed operations, including dynamic adjustment of gate strengths of a multi-layer neural network, iterative accuracy-constrained training, processing of time-series weather data across multiple geographic locations, and generating digital numerical likelihood scores, are inherently computational, require digital numerical processing, and cannot be carried out practically or reliably by a human mind...the currently pending claims do not recite any mathematical concepts. The Office Action at p.17 characterizes certain elements as allegedly being mathematical concepts…Amended independent claim 21 is analogous to the level of training detail identified in Example 39. For example, Applicant's independent claim requires randomizing initial strengths of a plurality of gates, generating a digital numerical value representing a likelihood, and dynamically updating the initial strengths. Although these claim requirements set out necessary operations for training and updating a model, none of these claim requirements sets forth or describes any particular mathematical formula or algorithm. They simply recite functional machine-learning operations, precisely as in Example 39…the USPTO's Example 47 recites named algorithms such as "a backpropagation algorithm" and "a gradient descent algorithm." The Memo explains that such recitations do constitute mathematical concepts because they expressly describe specific mathematical calculations. Id. Applicant's claims contain no such recitations. Therefore, the pending claims, like Example 39, do not trigger a finding of a judicial exception…When viewed holistically, the amended claims are directed to a specific neural network architecture trained through a multi-step accuracy-constrained training loop operating on structured labeled weather data, producing digital numerical likelihood scores, processing real-time data assembled into digital vectors, and returning outputs to an immutable ledger...As a whole, the amended claims are not directed to any abstraction but rather a concrete technological process carried out by a specific neural network architecture...Applicant respectfully submits that under Step 2A Prong One, the pending claims do not recite a mental process, do not recite a mathematical concept, and therefore do not recite any judicial exception…amended independent claim 21 recites a specific two-stage computer-implemented method that materially improves both (i) the functioning of a neural network architecture and (ii) the technological field of automated significant-event detection using real-time environmental data. The claim does not describe abstract data manipulation, but instead defines a concrete pipeline of technological operations that begins with iterative accuracy-bounded neural network training and ends with deployment of the trained system on real-world weather event data, with the resulting outputs stored in an immutable ledger…The claimed training phase itself integrates any alleged exception into a practical application by specifying a structural neural network with a plurality of artificial cells and gates whose gate strengths encode relationship weights between cell outputs and inputs. Amended independent claim 21 further requires randomizing initial gate strengths, generating digital numerical likelihood values based on portions of the training data, comparing these likelihoods to labeled weather event outcomes across multiple locations and time periods, and dynamically updating the gate strengths until a threshold model accuracy is met. These operations improve the functioning of the neural network by enabling it to refine internal parameters through a data-dependent, iterative training loop that is tied to a particular technical domain: environmental event detection. The requirement of dynamically updating initial gate strengths based on probabilistic comparisons to labeled, multi-dimensional training data is not conventional, generic computer operation. Rather, it is a specific technological improvement to the way the network learns and represents relationships among weather events over time and across locations…The runtime phase of the currently pending claims likewise supports a practical application. The claims require receiving real-time event data corresponding to one or more locations in the environment, assembling the data into digital input vectors, applying those vectors to the specifically trained neural network, and receiving network outputs identifying event data with significance levels above predetermined thresholds. These operations are necessarily tied to real-world physical conditions and cannot reasonably be understood as mental processes or abstract calculations. The use of "digital input vectors" reflects a concrete digital data-structuring step, further grounding the claim in a technological environment. The neural network in its specifically trained and configured form is then used to evaluate, classify, and surface potentially significant weather events in real time, which provides a clear technical improvement to the field of environmental event-detection systems...the requirement that the outputted significant-event data be returned for storage in an immutable ledger provides an additional meaningful indication of integration into a practical application. Immutable ledgers are technical data-storage constructs that require specialized mechanisms for cryptographic integrity, distributed consensus, or append-only data structures. Such storage is not conventional in standard neural network classification systems and ties the end-to-end process to a concrete computing environment designed to ensure tamper-resistant event logging. This feature demonstrates that the claim goes beyond classifying information and extends to a real, technological solution supporting auditability, reliability, and downstream use in weather-related safety, compliance, or other similar reporting infrastructures…When considered as a whole, the claimed technology therefore reflects a coordinated sequence of computer-implemented operations that improves both (1) the internal workings of a neural network through dynamic gate-strength updates tied to labeled weather data and (2) the broader technical field of automated weather event detection using real-time sensor inputs. The USPTO Memo explains that claims improving computer functionality or another technical field are integrated into a practical application and are patent eligible under Step 2A Prong Two. See Memo, p.2. Applicant's claims clearly meet this standard. The currently pending claims do not merely "process data," but instead define specific, structured, and technologically meaningful steps that provide real-world utility and enhance the functioning of a computer system deployed for environmental monitoring. Accordingly, even if an exception were implicated, the pending claims integrate any such exception into a practical application and therefore satisfy Step 2A Prong Two…” The Examiner respectfully disagrees.
While Applicant’s amendments further prosecution, however the claims as a whole, unlike Example 39, by Applicant’s own admission, recite and direct to, …processing of time-series weather data across multiple geographic locations, and generating numerical likelihood scores…generating a numerical value representing a likelihood…producing numerical likelihood scores, processing real-time data assembled into vectors, and returning outputs to an immutable ledger…environmental event detection… learns and represents relationships among weather events over time and across locations…supporting auditability, reliability, and downstream use in weather-related safety, compliance, or other similar reporting infrastructures…, which is a problem directed to a mental process (i.e. human observing weather events, human using mathematical concepts to predict and determine significant weather events, and humans recording predicted significant weather events) and mathematical concepts (i.e. human observing weather events, human using mathematical concepts to predict and determine significant weather events, and humans recording predicted significant weather events), as established in Step 2A Prong 1. This problem does not specifically arise in the realm of computer technology, but rather, this problem existed and was addressed long before the advent of computers. Thus, the claims do not recite a technical improvement to a technical problem or necessarily roots in computing technologies. The alleged solutions are solutions directed to solving abstract ideas, which are still abstract ideas. Additionally, pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements, i.e. computer, neural network, recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components. Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer, neural network, performing extra solution activities. Therefore, as a whole, the additional elements do not integrate the abstract ideas into a practical application in Step 2A Prong 2.
Even novel and newly discovered judicial exceptions are still exceptions, despite their novelty. July 2015 Update, p. 3; see SAP America Inc. v. Investpic, LLC, No. 2017-2081, slip op. at 2 (Fed Cir. May 15, 2018).
Simply reciting specific limitations that narrow the abstract idea does not make an abstract idea non-abstract. 79 Fed. Reg. 74631; buySAFE Inc. v. Google, Inc., 765 F.3d 1350, 1355 (2014); see SAP America at p. 12. As discussed in SAP America, no matter how much of an advance the claims recite, when “the advance lies entirely in the realm of abstract ideas, with no plausibly alleged innovation in the non-abstract application realm,” “[a]n advance of that nature is ineligible for patenting.” Id. at p. 3.
Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures “can be carried out in existing computers long in use, no new machinery being necessary.” 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of “anonymous loan shopping” recited in a computer system claim is an abstract idea because it could be “performed by humans without a computer”).
An example of a case in which a computer was used as a tool to perform a mental process is Mortgage Grader, 811 F.3d. at 1324, 117 USPQ2d at 1699. The patentee in Mortgage Grader claimed a computer-implemented system for enabling borrowers to anonymously shop for loan packages offered by a plurality of lenders, comprising a database that stores loan package data from the lenders, and a computer system providing an interface and a grading module. The interface prompts a borrower to enter personal information, which the grading module uses to calculate the borrower’s credit grading, and allows the borrower to identify and compare loan packages in the database using the credit grading. 811 F.3d. at 1318, 117 USPQ2d at 1695. The Federal Circuit determined that these claims were directed to the concept of “anonymous loan shopping”, which was a concept that could be “performed by humans without a computer.” 811 F.3d. at 1324, 117 USPQ2d at 1699. Another example is Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018), in which the patentee claimed methods for parsing and evaluating data using a computer processing system. The Federal Circuit determined that these claims were directed to mental processes of parsing and comparing data, because the steps were recited at a high level of generality and merely used computers as a tool to perform the processes. 881 F.3d at 1366, 125 USPQ2d at 1652-53.
TLI Communications provides an example of a claim invoking computers and other machinery merely as a tool to perform an existing process. The court stated that the claims describe steps of recording, administration and archiving of digital images, and found them to be directed to the abstract idea of classifying and storing digital images in an organized manner. 823 F.3d at 612, 118 USPQ2d at 1747. The court then turned to the additional elements of performing these functions using a telephone unit and a server and noted that these elements were being used in their ordinary capacity (i.e., the telephone unit is used to make calls and operate as a digital camera including compressing images and transmitting those images, and the server simply receives data, extracts classification information from the received data, and stores the digital images based on the extracted information). 823 F.3d at 612-13, 118 USPQ2d at 1747-48. In other words, the claims invoked the telephone unit and server merely as tools to execute the abstract idea. Thus, the court found that the additional elements did not add significantly more to the abstract idea because they were simply applying the abstract idea on a telephone network without any recitation of details of how to carry out the abstract idea.
[E]xamples where the courts have found the additional elements to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process include:
i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
iii. A process for monitoring audit log data that is executed on a general-purpose computer where the increased speed in the process comes solely from the capabilities of the general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016);
v. Requiring the use of software to tailor information and provide it to the user on a generic computer, Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015)
A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the “mathematical concepts” grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word “calculating” in order to be considered a mathematical calculation. For example, a step of “determining” a variable or number using mathematical methods or “performing” a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.
Examples of mathematical calculations recited in a claim include:
i. performing a resampled statistical analysis to generate a resampled distribution, SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163-65, 127 USPQ2d 1597, 1598-1600 (Fed. Cir. 2018), modifying SAP America, Inc. v. InvestPic, LLC, 890 F.3d 1016, 126 USPQ2d 1638 (Fed. Cir. 2018);
vi. calculating the difference between local and average data values, In re Abele, 684 F.2d 902, 903, 214 USPQ 682, 683-84 (CCPA 1982).
Response to Arguments – Prior Art
Applicant’s arguments with respect to the rejections have been fully considered, but they are not persuasive.
The closest prior art are by US Patent Publication to US20200372349A1 to Ospina et al., (hereinafter referred to as “Ospina”) in view of US Patent Publication to US20200359550A1 to Tran et al., (hereinafter referred to as “Tran”)
However, the teachings of the references do not teach the specific ordered sequence of limitations of independent claims 21, 32,
Claim 21:
during a training phase, iteratively training, until a threshold level of accuracy is met,
a neural network to automatically identify significant events in the environment, wherein the neural network comprises a plurality of artificial cells interconnected via a plurality of gates, and wherein each gate encodes a strength of a relationship in the connection between an output of one artificial cell and an input of another artificial cell, the training comprising:
receiving training data that comprises labeled weather event data for a plurality of locations over a plurality of time periods;
randomizing initial strengths of the plurality of gates of the neural network;
generating, based on providing a portion of the training data as input to the neural network, a digital numerical value representing a likelihood that a weather event in the training data will be significant for at least one location amongst the plurality of locations;
comparing the generated digital numerical value to the labeled weather event data of the received training data; and
based on the comparing, dynamically updating the initial strengths of the plurality of gates of the neural network;
during a runtime phase:
receiving real-time weather event data that corresponds to a respective one or more locations in the environment;
assembling the real-time weather event data into a plurality of digital input vectors;
applying the plurality of digital input vectors to the trained neural network;
receiving, as output from the trained neural network, weather event data having a respective significance level that is greater than a respective predetermined threshold value; and
returning the outputted weather event data for storage in an immutable ledger.
Claim 32:
iteratively training, until a threshold level of accuracy is met, a machine learning model to automatically identify significant events in the environment, wherein the machine learning model includes a plurality of artificial cells interconnected via a plurality of gates each encoding a strength of a relationship between artificial cells to which the gate is connected, wherein the machine learning model was trained using training data labeled with significant events over a plurality time periods for a plurality of locations, and the training randomizes initial strengths of the plurality of gates, generates a digital numerical value representing a likelihood that a weather event in the training data will be significant for at least one location amongst the plurality of locations, and compares the generated digital numerical value to the labeled weather event data of the training data;
receiving events data for one or more locations, the events data comprising environmental measurements for inferring significance of events at the one or more locations;
assembling the events data into a plurality of digital input vectors;
applying the plurality of digital input vectors to the trained machine learning model;
receiving, as output from the trained machine learning model, indications of the events associated with the events data having a respective significance level that is greater than a respective predetermined threshold value; and
returning the output for storage in an immutable ledger.
No Non-Patent literature teach the specific ordered sequence of limitations of independent claims 21, 32.
The prior art rejection is hereby withdrawn.
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 21-38, 41, and 42 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 21 recite, A … method for automatically identifying significant events in an environment, the method comprising:
during a training phase, iteratively training, until a threshold level of accuracy is met,
…, the training comprising:
receiving training data that comprises labeled weather event data for a plurality of locations over a plurality of time periods;
…;
generating, based on providing a portion of the training data as input to the …, a … numerical value representing a likelihood that a weather event in the training data will be significant for at least one location amongst the plurality of locations;
comparing the generated … numerical value to the labeled weather event data of the received training data; and
…;
during a runtime phase:
receiving real-time weather event data that corresponds to a respective one or more locations in the environment;
assembling the real-time weather event data into a plurality of … input vectors;
applying the plurality of … input vectors to the trained …;
receiving, as output from the trained …, weather event data having a respective significance level that is greater than a respective predetermined threshold value; and
returning the outputted weather event data for storage in an immutable ledger.
Claim 32 recite, A … method for automatically identifying significant events in an environment, the method comprising:
iteratively training, until a threshold level of accuracy is met, a … model to automatically identify significant events in the environment, … model was trained using training data labeled with significant events over a plurality time periods for a plurality of locations, and the …, generates a … numerical value representing a likelihood that a weather event in the training data will be significant for at least one location amongst the plurality of locations, and compares the generated … numerical value to the labeled weather event data of the training data;
receiving events data for one or more locations, the events data comprising environmental measurements for inferring significance of events at the one or more locations;
assembling the events data into a plurality of … input vectors;
applying the plurality of … input vectors to the trained machine learning model;
receiving, as output from the trained … model, indications of the events associated with the events data having a respective significance level that is greater than a respective predetermined threshold value; and
returning the output for storage in an immutable ledger.
Analyzing under Step 2A, Prong 1:
The limitations regarding, …automatically identifying significant events in an environment…during a training phase, iteratively training, until a threshold level of accuracy is met, …, the training comprising: receiving training data that comprises labeled weather event data for a plurality of locations over a plurality of time periods; …; generating, based on providing a portion of the training data as input to the …, a … numerical value representing a likelihood that a weather event in the training data will be significant for at least one location amongst the plurality of locations; comparing the generated … numerical value to the labeled weather event data of the received training data; and …; during a runtime phase: receiving real-time weather event data that corresponds to a respective one or more locations in the environment; assembling the real-time weather event data into a plurality of … input vectors; applying the plurality of … input vectors to the trained …; receiving, as output from the trained …, weather event data having a respective significance level that is greater than a respective predetermined threshold value; and returning the outputted weather event data for storage in an immutable ledger…iteratively training, until a threshold level of accuracy is met, a … model to automatically identify significant events in the environment, … model was trained using training data labeled with significant events over a plurality time periods for a plurality of locations, and the …, generates a … numerical value representing a likelihood that a weather event in the training data will be significant for at least one location amongst the plurality of locations, and compares the generated … numerical value to the labeled weather event data of the training data; receiving events data for one or more locations, the events data comprising environmental measurements for inferring significance of events at the one or more locations; assembling the events data into a plurality of … input vectors; applying the plurality of … input vectors to the trained machine learning model; receiving, as output from the trained … model, indications of the events associated with the events data having a respective significance level that is greater than a respective predetermined threshold value; and returning the output for storage in an immutable ledger…, under the broadest reasonable interpretation, can include a human using their mind and using pen and paper to perform the identified limitations; therefore, the claims recite a mental process.
Further, …automatically identifying significant events in an environment…during a training phase, iteratively training, until a threshold level of accuracy is met, …, the training comprising: receiving training data that comprises labeled weather event data for a plurality of locations over a plurality of time periods; …; generating, based on providing a portion of the training data as input to the …, a … numerical value representing a likelihood that a weather event in the training data will be significant for at least one location amongst the plurality of locations; comparing the generated … numerical value to the labeled weather event data of the received training data; and …; during a runtime phase: receiving real-time weather event data that corresponds to a respective one or more locations in the environment; assembling the real-time weather event data into a plurality of … input vectors; applying the plurality of … input vectors to the trained …; receiving, as output from the trained …, weather event data having a respective significance level that is greater than a respective predetermined threshold value; and returning the outputted weather event data for storage in an immutable ledger…iteratively training, until a threshold level of accuracy is met, a … model to automatically identify significant events in the environment, … model was trained using training data labeled with significant events over a plurality time periods for a plurality of locations, and the …, generates a … numerical value representing a likelihood that a weather event in the training data will be significant for at least one location amongst the plurality of locations, and compares the generated … numerical value to the labeled weather event data of the training data; receiving events data for one or more locations, the events data comprising environmental measurements for inferring significance of events at the one or more locations; assembling the events data into a plurality of … input vectors; applying the plurality of … input vectors to the trained machine learning model; receiving, as output from the trained … model, indications of the events associated with the events data having a respective significance level that is greater than a respective predetermined threshold value; and returning the output for storage in an immutable ledger…, are mathematical concepts.
Accordingly, the claims recite and directed to a mental process, mathematical concepts, and thus, the claims are directed to an abstract idea under the first prong of Step 2A.
Analyzing under Step 2A, Prong 2:
This judicial exception is not integrated into a practical application under the second prong of Step 2A.
In particular, the claims recite the additional elements beyond the recited abstract idea identified under Step 2A, Prong 1, such as:
Claim 21, 32: computer-based, a neural network to automatically identify significant events in the environment, wherein the neural network comprises a plurality of artificial cells interconnected via a plurality of gates, and wherein each gate encodes a strength of a relationship in the connection between an output of one artificial cell and an input of another artificial cell, randomizing initial strengths of the plurality of gates of the neural network, based on the comparing, dynamically updating the initial strengths of the plurality of gates of the neural network, digital, machine learning, wherein the machine learning model includes a plurality of artificial cells interconnected via a plurality of gates each encoding a strength of a relationship between artificial cells to which the gate is connected, wherein the machine learning, training randomizes initial strengths of the plurality of gates
Claim 27: satellites
Claim 38: Long Short-Term Memory (LSTM) model
Claim 41, 42: sensor
, and pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components.
Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer.
Additionally, with respect to, “…receiving…”, “…generating…”, “…providing…”, “…assembling…”, “…applying…”, “…publishing…”, “…returning…”, these elements do not add a meaningful limitations to integrate the abstract idea into a practical application because they are extra-solution activity, pre and post solution activity - i.e. data gathering – “…receiving…”, “…providing…”, “…assembling…”, “…applying…” data output – “…generating…”, “…returning…”, “…publishing…”, “…receiving, as output…”, “…returning…”
Analyzing under Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B.
As noted above, the aforementioned additional elements beyond the recited abstract idea are not sufficient to amount to significantly more than the recited abstract idea because, as an order combination, the additional elements are no more than mere instructions to implement the idea using generic computer components (i.e. apply it).
Additionally, as an order combination, the additional elements append the recited abstract idea to well-understood, routine, and conventional activities in the field as individually evinced by the applicant’s own disclosure, as required by the Berkheimer Memo, in at least:
[0021] Once the model is trained, it may be put into operation. In some embodiments, for each new event occurring on a particular field, the event analysis module may calculate a probability for whether the event has significance in terms of traceability. If the calculated probability "P" is greater than a predefined threshold (e.g., P > 0.8) , then the event may be automatically posted to an ledger. In some embodiments, the ledger may be a distributed ledger, such as a blockchain.
[0046] For illustrative purposes, the ML model 440 will be described with reference to a LSTM class ML model ("LSTM model"). LSTM models may be desirable because they can remember values over arbitrary time intervals. This, in turn, may allow for classifying events in input time series data for particular entities, as there may be lags of unknown duration between important events in the time series. However, other types of ML models are consistent with the disclosure, as are algorithmic models.
[0059] The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.
[0060] The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD- ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.
[0061] Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.
[0062] Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.
[0063] Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.
[0064] These computer readable program instructions may be provided to a processor of a computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.
[0065] The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
[0066] The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be accomplished as one step, executed concurrently, substantially concurrently, in a partially or wholly temporally overlapping manner, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.
[0067] Embodiments of the present invention may also be delivered as part of a service engagement with a client corporation, nonprofit organization, government entity, internal organizational structure, or the like. Aspects of these embodiments may include configuring a computer system to perform, and deploying software and web services that implement, some or all of the methods described herein. Aspects of these embodiments may also include analyzing the client's operations, creating recommendations responsive to the analysis, generating software to implement portions of the recommendations, integrating the software into existing processes and infrastructure, metering use of the systems, allocating expenses to users of the systems, and billing for the use of the systems. This service engagement may be directed at providing both the cloud services and the cloud controller services, may be limited to only providing cloud controller services, or some combination thereof. Accordingly, these embodiments may further comprise receiving billing information from other entities and associating that billing information with end-users of the cloud.
[0068] The descriptions of the various embodiments of the present disclosure have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
[0069] Therefore, it is desired that the embodiments described herein be considered in all respects as illustrative, not restrictive, and that reference be made to the appended claims for determining the scope of the invention.
Furthermore, as an ordered combination, these elements amount to generic computer components receiving or transmitting data over a network, performing repetitive calculations, electronic record keeping, and storing and retrieving information in memory, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d).
Moreover, the remaining elements of dependent claims do not transform the recited abstract idea into a patent eligible invention because these remaining elements merely recite further abstract limitations that provide nothing more than simply a narrowing of the abstract idea recited in the independent claims.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components to “apply” the recited abstract idea, perform insignificant extra-solution activity, and generally link the abstract idea to a technical environment. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 21-38, 41, and 42 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action.
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/PO HAN LEE/Primary Examiner, Art Unit 3623
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