Status of the Application
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The amendment filed on December 30, 2025 has been entered. The following has occurred: Claims 1, 9, and 17 have been amended; and Claims 4, 12, and 18 were previously cancelled.
Claims 1-3, 5-11, 13-17, 19, and 20 are pending.
Response to Amendment
35 U.S.C 112(b) rejection has been added in light of the amendment.
35 U.S.C. 101 rejection has been maintained in light of the amendment.
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 1-3, 5-11, 13-17, 19, and 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.
Claims 1, 9, and 17 recite, “performing feature selection to identify relevant features correlated with a number of incidents and to reduce dimensionality and complexity of the regressive incident prediction module” (bold emphasis) which lacks antecedent basis. The following limitation however recites “training in a first epoch, … a regressive incident prediction module.” It because unclear whether if “the regressive incident prediction module” in the performing limitation is supposed to be “a regressive incident prediction module” and the following limitation is supposed to recite “training… the regressive incident prediction module” or is the training step supposed to be performed prior to the performing step. For the purpose of expediting compact prosecution, the Examiner will interpret “the regressive incident prediction module” in the performing step to be “a regressive incident prediction module” and the following limitation of training step, “a regressive incident prediction module” to be “the regressive incident prediction module.”
Claims 2, 3, 5-8, 10, 11, 13-16, 19, and 20 depend from claims 1, 9, and 17 above and therefore inherit the 35 U.S.C. 112 deficiencies of their parent claim.
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 1-3, 5-11, 13-17, 19, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Is the claim to a process, machine, manufacture or composition of matter? (MPEP 2106.03)
In the present application, claims 1-3 and 5-8 are directed to a method (i.e. a process), claims 9-11 and 13-16 are directed to a system (i.e. a machine), and claims 17, 19 and 20 are directed to a computer product (i.e. an article of manufacture). Thus, the eligibility analysis proceeds to Step 2A.1.
Step 2A. 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon? (MPEP 2106.04)
While claims 1, 9, and 17, are directed to different categories, the language and scope are substantially the same and have been addressed together below.
The abstract idea recited in claims 1, 9, and 17, is:
capture data from one or more products at a customer location;
extract customer-specific usage-related data and manufacturing data from the (captured/collected) data
performing preliminary operations on the customer-specific usage-related data and the manufacturing data to generate the first training dataset, the preliminary operations including: (i) null data handling in which missing values in at least one feature column are replaced with a median value for that feature column; (ii) encoding textual categorical values into numerical values using one-hot encoding; and (iii) performing feature selection to identify relevant features correlated with a number of incidents and to reduce dimensionality and complexity of the regressive incident prediction module,
train in a first epoch a regressive incident prediction module to predict a number of future incidents for products at customer locations using a training dataset composed of a plurality of training samples, each training sample of the plurality of training samples corresponding customer-specific usage-related data and manufacturing data, each training sample of the plurality of training samples to adjust weights in the regressive incident prediction module, wherein training the regressive incident prediction module includes inputting different portions of the training dataset and comparing predictions of numbers of future incidents with target values of the first training samples to adjust weights in the regressive incident prediction module;
training in one or more subsequent epochs, the regressive incident prediction module using a second training dataset comprising the first training dataset with adjusted weights and bias values;
receive a customer-specific usage-related data for a first product at a customer location;
generate a feature vector for the first product, the feature vector representing one or more features from the customer-specific usage-related data;
input the feature vector to the regressive incident prediction module to predict a number of future incidents for the product at the customer location, wherein the training dataset is generated from a corpus of historical product utilization and environment data (claim 17); and
generate an extended warranty for the first product, the extended warranty including a price determined by the predicted number of future incidents for the product at the customer location.
The claimed invention is directed to an abstract idea of determining pricing for an extended warranty for a product.
The limitations above suggest a process similar to collecting information (e.g., [A] capture data from products; [F] receive customer-specific usage-related data; [H] input feature vector including historical product utilization and environment data) and analyzing the information (e.g., [B] extract customer-specific usage-related data and manufacturing data; [C]-[E] train regressive incident production module; [G] generate a feature vector; and [I] generate extended warranty information with pricing). Because the limitations above closely follow the steps of collecting information and analyzing the collected information, and the steps involved human judgements, observations, and evaluations that can be practically or reasonably performed in the human mind, the claims recite an abstract idea consistent with the “mental processes” grouping of the abstract ideas, set forth in MPEP 2106.04(a)(2)(III).
Additionally and alternatively, the same claim limitations above recite a fundamental economic practice long prevalent in our system of commerce in the form of advertising, marketing, or sales activity or behaviors for customer engagement of commercial industry. Under the broadest reasonable interpretation, other than the additional elements of computer components, the limitations recite a process of collecting specific usage related information for a product from a customer, analyzing the collected information into a regressive incident prediction module/algorithm to make a prediction of possibility of a number of future incidents for the customer’s product, to generate and provide an extended warranty service with pricing to the customer. Because the limitations above closely follow the steps standard in commercial interaction for a business practice of providing an extended warranty service with pricing of a product to customers, the claims recite an abstract idea consistent with the “certain methods of organizing human activity” grouping of the abstract ideas, set forth in MPEP 2106.04(a)(2)(II).
Additionally and alternatively, under the broadest reasonable interpretation in view of the specification (para. [0035], [0040]-[0044] stating mathematical algorithms and calculations), the limitations [C]-[E], and [G]-[I] recite steps of generating training set, training a regressive incident prediction module/algorithm, generating a feature vector, inputting the feature vector to the regressive incident prediction module/algorithm for calculation of a possibility or predicted number of future incidents, then further determine (i.e., calculation) a price for the extended warranty based on the predicted number of future incidents for the customer’s product. Because the limitations in [C]-[E], and [G]-[I] amount to forms of performing mathematical calculations, the claims recite an abstract idea consistent with the “mathematical concepts” grouping of the abstract ideas, set forth in MPEP 2106.04(a)(2)(I).
“For example, in a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for Step 2A, Prong One to make the analysis clear on the record.” MPEP 2106.04, subsection II.B. Under such circumstances, however, the Supreme Court has treated such claims in the same manner as claims reciting a single judicial exception. Id. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)). Here, the claimed invention falls within the mental process/certain method of organizing human activity grouping of abstract ideas, and the steps fall within the mathematical concepts grouping of abstract ideas. The limitations are considered together as a single abstract idea for further analysis. (Step 2A, Prong One: YES).
Step 2A. 2: Does the claim recite additional elements that integrate the judicial exception into a practical application? (MPEP 2106.04)
This judicial exception is not integrated into a practical application because the additional elements merely add instructions to apply the abstract idea to a computer and insignificant extra-solution activity.
The additional elements considered include:
Claim 1: “at a warranty system”; “by the warranty system”; “capturing telemetry data, to a warranty system,” “from the telemetry data”;
Claim 9: “A system comprising: one or more non-transitory machine-readable mediums configured to store instructions; and one or more processors configured to execute the instructions stored on the one or more non-transitory machine-readable mediums, wherein execution of the instructions causes the one or more processors to”; “capture telemetry data,” “from the telemetry data”;
Claim 17: “A non-transitory, computer-readable storage medium has encoded thereon instructions that, when executed by one or more processors, causes a process to be carried out, the process comprising:” “capturing telemetry data”; “from the telemetry data”;
The additional element of a system comprising generic computer elements are found to recite mere instructions to apply a generic computer and technology to execute the method in the recited claim limitations, as merely using a computer to transmit, manipulate, and display information is not an improvement to a technology or technical field. The above-mentioned additional elements merely recite computer elements to capture, extract, train, receive, generate, input, and determine information. The additional elements are recited at a high-level of generality and amount to no more than mere instructions to apply the exception using generic computer components, i.e., these generic computing elements are merely being used to perform the tasks of the abstract idea, see MPEP 2106.05(f). There is no indication from the specification that the computer elements are anything but generic hardware and/or software, and the combination of elements is simply a generic computing system (see Applicant’s Specification at least at paragraphs [0027] and [0047] indicating process 500 may be implemented or performed by any suitable hardware, or combination of hardware and software, including without limitation the system shown and described with respect to Fig. 1, the computing device shown and described with respect to Fig. 6, or a combination thereof. For example, in some embodiments, the operations, functions, or actions illustrated in process 500 may be performed, for example, in whole or in part by telemetry collection system 106, incident prediction module 108, and warranty pricing module 110, or any combination of these including other components of warranty system 102 described with respect to Fig. 1. Also in paragraphs [0052]-[0062] indicating the computer system can be any generic computer device comprising a number of components that are generic and indiscriminate device (e.g., processor, memory, and user interface)).
Further, the function of limitations [A]-[I] are steps of adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea as discussed in MPEP 2106.05(f). In combination with the additional element limitations, “capture telemetry data from one or more products at a customer locations;” “extract customer-specific usage-related data and manufacturing data from the telemetry data;” “performing preliminary operations on the customer-specific usage-related data and the manufacturing data to generate the first training dataset, the preliminary operations including: (i) null data handling in which missing values in at least one feature column are replaced with a median value for that feature column; (ii) encoding textual categorical values into numerical values using one-hot encoding; and (iii) performing feature selection to identify relevant features correlated with a number of incidents and to reduce dimensionality and complexity of the regressive incident prediction module”, “train in a first epoch a regressive incident prediction module to predict a number of future incidents for products at customer locations using a training dataset composed of a plurality of training samples…” “training in one or more subsequent epochs, the regressive incident prediction module using a second training dataset comprising the first training dataset with adjusted weights and bias values;” “generate a feature vector for the first product, the feature vector representing one or more features from the customer-specific usage-related data;” “input the feature vector to the regressive incident prediction module to predict a number of future incidents for the product at the customer location;” and “generate an extended warranty for the first product, the extended warranty including a price determined by the predicted number of future incidents for the product at the customer location”; the claims are recited at a high level of generality that only recites the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished, see MPEP 2106.05(f). The combination of these additional elements are no more than mere instructions to apply the exception using a generic computer.
Similarly, reciting the abstract idea as software functions used to program a generic computer is not significant or meaningful: generic computers are programmed with software to perform various functions every day. A programmed generic computer is not a particular machine and by itself does not amount to an inventive concept because, as discussed in MPEP 2106.05(a), adding the words “apply it” (or an equivalent) with the judicial exception, or more instructions to implement an abstract idea on a computer, as discussed in Alice, 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)), is not enough to integrate the exception into a practical application. Further, it is not relevant that a human may perform a task differently from a computer. It is necessarily true that a human might apply an abstract idea in a different manner from a computer. What matters is the application, “stating an abstract idea while adding the words ‘apply it with a computer’” will not render an abstract idea non-abstract. Tranxition v. Lenovo, Nos. 2015-1907, -1941, -1958 (Fed. Cir. Nov. 16, 2016), slip op. at 7-8.
Here, the instructions entirely comprise the abstract idea, leaving little if any aspects of the claim for further consideration under Step 2A Prong 2. In short, the role of the generic computing elements recited in claims 1, 9, and 17, is the same as the role of the computer in the claims considered by the Supreme Court in Alice, and the claim as whole amounts merely to an instruction to apply the abstract idea on the generic computerised system. Therefore, the claims have failed to integrate a practical application (2106.04(d)). Under the MPEP 2106.05, this supports the conclusion that the claim is directed to an abstract idea, and the analysis proceeds to Step 2B.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? (MPEP 2106.05)
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the bold portions of the limitations recited above, were all considered to be an abstract idea in Step2A-Prong Two. The additional elements and analysis of Step2A-Prong two is carried over. For the same reason, these elements are not sufficient to provide an inventive concept. Applicant has merely recited elements that instruct the user to apply the abstract idea to a computer or other machinery. When considered individually and in combination the conclusion, as discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer to perform the above-mentioned limitations amount to no more than mere instructions to apply the function of the limitations to the exception using generic computer component, as discussed in MPEP 2106.05(f). The claim as a whole merely describes how to generally “apply” the concept for determining pricing for an extended warranty for a product. Thus, viewed as a whole, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea. For these reasons there is no inventive concept in the claims and thus are ineligible.
As for dependent claims 2, 3, 10, and 11, these claims recite limitations that further define the abstract idea noted in the independent claims. The claims further recite additional descriptive information regarding to the trained incident prediction module that do not change abstract idea of the independent claim. No additional element is recited that integrates the abstract idea into a practical application. The additional detail of the claim limitations does not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technology environment. The claims are ineligible.
As for dependent claims 5, 6, 13, 14, and 19, these claims recite limitations that further define the abstract idea noted in the independent claims. The claims further recite additional descriptive information regarding to the feature information that do not change abstract idea of the independent claim. No additional element is recited that integrates the abstract idea into a practical application. The additional detail of the claim limitations does not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technology environment. The claims are ineligible.
As for dependent claims 7, 8, 15, 16, and 20, these claims recite limitations that further define the abstract idea noted in the independent claims. The claims further recite additional descriptive information regarding to the customer-specific usage related data that do not change abstract idea of the independent claim. No additional element is recited that integrates the abstract idea into a practical application. The additional detail of the claim limitations does not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technology environment. The claims are ineligible.
In summary, the dependent claims considered both individually and as ordered combination do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. The claims do not recite an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment. Therefore, claims 1-3, 5-11, 13-17, 19, and 20 are rejected under 35 U.S.C. 101.
Allowable Subject Matter over Prior Art
The closest prior art found are:
Vanga et al. (US 20200320539 A1) is directed to projecting warranty cost for an electronic product, such as an enterprise server, based on usage data, which specifically discloses:
training, at a warranty system, a module to predict a number of future incidents for products at customer locations using a training dataset (para. [0017] “the cognitive warranty system 118 includes a warranty model training module 122, which may use the customer specific usage data 120 to generate a warranty cost model” para. [0015] disclosing the generating of warranty cost projection model using customer specific usage information acquired from existing information processing system during operation of the product by different customers and employing the developed model to project the warranty cost associated with future transactions involving the same and/or similar information processing system operated by the customer. In para. [0036] disclosing the usage data of the same customer includes the geographical location of the information processing system operated by the same customer. Para. [0029] and [0034] disclosing “the neural network 500 may be trained using customer-specific usage data to provide a projected warranty cost that may be more accurate than the model that is generated using the cost factors associated with default warranty provisions.”);
receiving, by the warranty system (para. [0015], [0017], and [0032]: information processing system), a customer-specific usage-related data for a product at a customer location (para. [0015], [0017], and [0032] disclosing the retrieving or acquiring of customer-specific usage-related data for a product. Further in claims 3, 10, 17, and para. [0036] disclosing the customer specific usage data acquired includes geographical location/attribute);
generating, by the warranty system, from the customer-specific usage-related data (para. [0017] disclosing customer specific usage data generated from information processing system);
generating, by the warranty system, an extended warranty for the first product, the extended warranty including a price determined from the predicted number of future incidents for the product at the customer location (para. [0015], “the usage data may be employed to develop a model for projecting the warranty cost associated with future transactions involving the same and/or similar information processing system operated by the same customer. Certain aspects of the invention recognize that a warranty cost projection model may be generated based on usage data associated with the information processing system acquired during operation of the system by the customer.”).
However, Vanga does not expressly disclose the specific features of (italic emphasis):
training, at a warranty system, a regressive incident prediction module to predict a number of future incidents for products at customer locations using a training dataset composed of a plurality of training samples, each training sample of the plurality of training samples corresponding customer-specific usage-related data and manufacturing data, each training sample of the plurality of training samples to adjust weights in the regressive incident prediction module, wherein training the regressive incident prediction module includes inputting different portions of the training dataset and comparing predictions of numbers of future incidents with target values of the training samples to adjust weights in the regressive incident prediction module;
generate a feature vector for the first product, the feature vector representing one or more features from the customer-specific usage-related data;
input the feature vector to the regressive incident prediction module to predict a number of future incidents for the product at the customer location.
Chowdhury et al. (US 20200380336 A1) is directed to predicting hardware component failure system and method, which specifically teaches (italic emphasis),
generate a feature vector for the first product, the feature vector representing one or more features from the customer-specific usage-related data (para. [0007], [0010], [0013], and [0057]-[0058] teaches the telemetry data used in form a vector for the neural network is representative of the feature vector from the customer specific usage related data from the computer component);
input the feature vector to the regressive incident prediction module to predict a number of future incidents for the product at the customer location (para. [0034], [0049], [0050], [0062] teaching inputting of feature vector for predicting a number of future incidents/failure for the product at the customer location based on the feature vector using a trained predicted model).
However, Chowdhury does not expressly teach the specific features of (italic emphasis):
training, at a warranty system, a regressive incident prediction module to predict a number of future incidents for products at customer locations using a training dataset composed of a plurality of training samples, each training sample of the plurality of training samples corresponding customer-specific usage-related data and manufacturing data, each training sample of the plurality of training samples to adjust weights in the regressive incident prediction module, wherein training the regressive incident prediction module includes inputting different portions of the training dataset and comparing predictions of numbers of future incidents with target values of the training samples to adjust weights in the regressive incident prediction module;
Zheng et al. (US 20210279596 A1) is directed to a system and method for Predictive Maintenance using Trace Norm Generative Adversarial Networks, which specifically teaches:
training, at a warranty system, a regressive incident prediction module to predict a number of future incidents for products (In para. [0027] and [0039] which teaches the training of failure prediction model such as linear regression DNN to predict future failures of products).
However, Zheng does not expressly teach the specific features of (italic emphasis):
training, at a warranty system, a regressive incident prediction module to predict a number of future incidents for products at customer locations using a training dataset composed of a plurality of training samples, each training sample of the plurality of training samples corresponding customer-specific usage-related data and manufacturing data, each training sample of the plurality of training samples to adjust weights in the regressive incident prediction module, wherein training the regressive incident prediction module includes inputting different portions of the training dataset and comparing predictions of numbers of future incidents with target values of the training samples to adjust weights in the regressive incident prediction module;
Dinwiddie et al. (US 20190213600 A1) is directed to a systems and Methods determine a failure rate or time period for anticipated failure for a component of a product or the product itself utilizing data sets comprising a product identifier, a component identifier, and a user identifier. Product warranty registration is accomplished using the product or component identifier with a warranty identifier that enables determination of in-use date of a particular product or component, which results in more accurate determinations of component failure rates and warranty periods.
Singh et al. (US 20190171961 A1) is directed to system for monitoring alert sequences from work vehicles and determining and/or predicting a probability of a machine failure occurring and probable parts used to repair the work vehicle should a machine failure occur.
Fraser et al. (US 20220397097 A1) is directed to a system and method for monitoring turbine performance to predicted fault or failure based on historical data from the target turbine.
Chan et al. (US 20080177613 A1) is directed to a method and system of predicting a maintenance schedule and estimating a cost for warranty service of systems.
Sethi et al. (US 20210350213 A1) is directed to methods, apparatus, and processor-readable storage media for automated configuration determinations for data center devices using artificial intelligence.
N. I. B. Jamahir, H. B. A. Majid and A. B. A. Samah, "An Overview on Warranty Cost Modelling in Two-Dimensional Warranty Using Neuro-Fuzzy Approach," 2012 Third International Conference on Intelligent Systems Modelling and Simulation, Kota Kinabalu, Malaysia, 2012, pp. 137-142, doi: 10.1109/ISMS.2012.121; is directed to neuro-fuzzy framework apply in warranty cost modelling.
Zheng (US 20220351842 A1) is directed to a computer system includes memory hardware configured to store a machine learning model, historical feature vector inputs, and computer-executable instructions. The historical feature vector inputs include historical claim data structures specific to multiple entities. The system includes processor hardware configured to execute the instructions. The instructions include training the machine learning model with the historical feature vector inputs to generate a claim prediction output, obtaining a set of multiple patient entities, and for each patient entity in the set of multiple patient entities, obtaining structured claim data specific to the patient entity and structured demographic data specific to the patient entity, generating a feature vector input according to the structured claim data, processing, by the machine learning model, the feature vector input to generate the claim prediction output, storing the claim prediction output as a predicted future claim value specific to the patient entity, and assigning the patient entity to a first entity subset or to a second entity subset according to the structured demographic data specific to the patient entity.
However, the combination of the above references does not explicitly teach the specific configuration of feature training in a first epoch, at a warranty system, a regressive incident prediction module to predict a number of future incidents for products at customer locations using a training dataset composed of a plurality of training samples, each training sample of the plurality of training samples corresponding customer-specific usage-related data and manufacturing data, each training sample of the plurality of training samples to adjust weights in the regressive incident prediction module, wherein training the regressive incident prediction module includes inputting different portions of the first training dataset and comparing predictions of numbers of future incidents with target values of the training samples to adjust weights in the regressive incident prediction module (i.e. in the particular manner it is claimed in the context of the whole claim is not disclosed, taught or suggested in the prior art(s)).
Examiner notes that the underlined limitations above, in combination with the other limitations found within the independent claims are found to be allowable over the prior art of record.
The prior art of record neither anticipates nor fairly and reasonably teach the independent claims 1, 9, and 17.
Examiner notes that while applicant has overcome the art of record, the application is not in condition for allowance, given the outstanding rejection under 35 U.S.C. 101.
Response to Remarks
35 U.S.C. 101 Rejections:
The Applicant’s remarks are fully considered, however, it is found to be unpersuasive.
Per Applicant’s remarks on pages 8-10, the Examiner respectfully disagrees.
The Examiner asserts the newly added steps reciting, “(i) replacing missing values with a median value, (ii) one-hot encoding textual categorical values, and (iii) performing feature selection to reduce dimensionality and complexity of the regressive incident prediction module” are all mathematical steps that can be performed mentally and manually, for the purpose of converting raw collected data information into training dataset. However, this does not change the abstract idea of the claimed invention, which is collecting usage information to be analyzed to make prediction of possible incident for the purpose of offering of extended warranty on products. The additional elements of computer system with machine learning application are merely applied for the expected result.
It is reflected in Enfish and Mentone Solutions, there is a fundamental difference between computer functionality improvements (improvement of the technology or technical field), on the one hand, and uses of existing computers as tools to perform a particular task (collecting, analyzing, and displaying information), on the other. The alleged advantages that the Applicant touts do not concern an improvement to computer capabilities or any machinery but instead relate to an alleged improvement in capturing, extracting, training, receiving, inputting, and generating information for a desirable result, which a computer is used as a mere tool in its ordinary capacity. The claimed invention does not improve the method or system for how the steps are performed in an improved technological capacity, but rather, merely use/utilize a computer device that can achieve the desirable result from generic computer function. To further clarify, the Applicant reflected a business need of the abstract idea for the collecting of information, training regressive predictive model to make prediction of possible incidents for the offering of extended warranty on products. The computer system itself or specific technology is not improved in anyway other than being applied as a tool/instrument for the judicial exception (abstract idea).
(Page 8)
At Alice step one, we determine whether the claims are directed to an abstract idea. Alice, 573 U.S. at 217. In cases involving software, step one often “turns on whether the claims focus on specific asserted improvements in computer capabilities or instead on a process or system that qualifies [as] an abstract idea for which computers are invoked merely as a tool.” Uniloc USA, Inc. v. LG Elecs. USA, Inc., 957 F.3d 1303, 1306–07 (Fed. Cir. 2020) (citing Customedia Techs., LLC v. Dish Network Corp., 951 F.3d 1359, 1364 (Fed. Cir. 2020)). “We have routinely held software claims patent eligible under Alice step one when they are directed to improvements to the functionality of a computer or network platform itself.” Id. at 1307 (collecting cases).
(Page 11)
This case is nothing like the claims we held ineligible in Two-Way Media Ltd. v. Comcast Cable Communications, LLC, 874 F.3d 1329 (Fed. Cir. 2017). There, the claims recited a method of transmitting packets of information over a communications network comprising: converting information into streams of digital packets; routing the streams to users; controlling the routing; and monitoring the reception of packets by the users. Id. at 1334. We held the claims ineligible because they merely recited a series of abstract steps (“converting,” “routing,” “controlling,” “monitoring,” and “accumulating records”) using “result-based functional language” without the means for achieving any purported technological improvement. Id. at 1337. Here, there is no functional claiming, nor are there abstract steps.
The 101 rejection has been maintained in light of the amended claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nathan C Uber can be reached on (571) 270-3923. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WENREN CHEN/Primary Examiner, Art Unit 3626