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
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.
Status of the Claims
Claims 1,8,17 are amended
Claims 1-20 are pending
The rejection under 35 USC 101 is maintained.
Response to Applicant Remarks
Applicant’s well-articulated remarks have been considered but are unpersuasive for the reasons below.
Regarding the rejection under 35 USC 101, Applicant argues that the claimed invention cannot be practiced in the human mind . (Applicant’s 5/9/26 remarks, p.9), “For instance, independent claim 1, as amended, recites: in response to determining that
the suspected false distress order is representative of an actual distress order, transmitting, via
mobile communication equipment associated with a subject matter engineer entity, tool data to
facilitate actions by the subject matter engineer entity via the mobile communication equipment
to remedy the actual distress order, wherein the tool data is a result of generating intelligence
data based on hierarchical classification infrastructures and previously misidentified false
distress orders, and adjusting the intelligence data based on the previously misidentified false
distress orders, boosting a number of boosting stages applied to hyper-parameter estimator
values of groups of hyper-parameter estimator values, applying a defined maximum depth
value associated with the groups of hyper-parameter estimator values, and supplying a
probable cause for the actual distress order and information data required to effectuate the
actions to resolve the actual distress order.”)
The examiner concurs that at the granularity currently claimed, the invention is arguably incapable of being practiced by a human. Nevertheless, the examiner points out that inventions directed toward application of known machine learning concepts to new fields may not be patent eligible. (See e.g., Recentive Analytics, Inc., V. Fox Corp, Fed Cir 2025, pp.9-10, “
The district court granted Fox’s motion to dismiss,
concluding that the patents were ineligible under the two
step inquiry of Alice Corporation v. CLS Bank Interna
tional, 573 U.S. 208 (2014). The court first found that the
asserted claims were “directed to the abstract ideas of
producing network maps and event schedules, respective
ly, using known generic mathematical techniques.”
Recentive, 692 F. Supp. 3d at 451. The court then found
at step two of Alice that the patents’ claims were not
directed to an “inventive concept” that would “amount[] to
significantly more than a patent upon the [ineligible
concept] itself,” id. at 456 (second alteration in original)
(quoting Alice, 573 U.S. at 217–18), because the machine
learning limitations were no more than “broad, function
ally described, well-known techniques” and claimed “only
generic and conventional computing devices,… This case presents a question of first impression:
whether claims that do no more than apply established
methods of machine learning to a new data environment
are patent eligible. We hold that they are not.”). The examiner respectfully suggests that Applicant’s claimed invention is likewise an abstract idea implemented by known mathematical principals practiced in machine learning. That is, although application of machine learning toward the business problem of a “false distress order” may indeed be novel, the claimed machine learning elements (e.g. hyperparameters, boosting stages, etc) appear to be techniques known to machine learning practitioners. Accordingly, the examiner cannot consider the claimed invention to be a technical improvement to the field of machine learning so much as applying known techniques to a business field per Recentive Analytics v. Fox Corp.
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) without significantly more. Regarding independent claims 1,8,17 the claimed invention recites an abstract idea without significantly more. The claims recites the abstract idea of determining supply chain false alarms which is a method of organizing human activity or mathematical concept. Other than reciting a processor and models nothing in the claims precludes the steps from being performed mentally. But for the processor and models the limitations on receive customer order, determine order is a suspected false distress order, determine suspected false distress order is a false distress order, determine predicted time value for order, determine false distress order is actual distress order, transmit tool data to engineer, tools data is generated based on hierarchical classification and previously misidentified distress orders, boosting stages applied to hyper parameters estimator values to a max depth is a process that under its broadest reasonable interpretation is a method of organizing human activity (Fundamental economic practice relating to commercial interactions). To the extent that the claim recites a classification or regression model or trained artificial intelligence model, the examiner understands these to employ machine learning techniques, patent ineligible mathematical concepts. However at the level they are claimed, they appear to only recite using generic computers to implement ineligible high level mathematical concepts known in machine learning. (See e.g., Recentive Analytics v. Fox. Corp, p.12, “The requirements that the machine learning model be
“iteratively trained” or dynamically adjusted in the Machine Learning Training patents do not represent a
technological improvement. Recentive’s own representations about the nature of machine learning vitiate this
argument:
Iterative training using selected training material and dynamic adjustments based on real-time
changes are incident to the very nature of machine learning. “ Thus, the claims recite an abstract idea.”)
The judicial exception is not integrated into a practical application. The computers are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components. The additional element(s) does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Simply implementing the abstract idea on a generic computer environment is not a practical application of the abstract idea and does not take the claim out of the mental process or method of organizing human activity grouping.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, with respect to integration of the abstract idea into a practical application, the additional element of a processor and models amounts to no more than mere instructions to apply the exception using a generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Collecting, analyzing and displaying information, and receiving and transmitting over a network are conventional in the computing arts. (MPEP 2106.05h; See also MPEP 2106.05, Alice v. CLS, “. Nearly every computer will include a ‘communications controller’ and ‘data storage unit’ capable of performing the basic calculation, storage, and transmission functions required by the method claims.”). Although the claims recite concepts in machine learning, the examiner respectfully suggests these are known mathematical concepts that amount to applying the abstract idea to a particular environment. (See e.g. Sipper, “Strong(er) Gradient Boosting”, 4/2023, https://pub.towardsai.net/strong-er-gradient-boosting-6eb617566328, discussing usage of hyperparameters and boosting stages to enhance AI performance). The claims are not patent eligible.
Regarding the dependent claims, these claims are directed to limitations which serve to limit the supply chain false distress determination steps. The subject matter of claims 2 (trained on gradient boosting framework), 3 (initialized with parameters), 4/18 (adapted with parameters), 5/19 (evaluation with validation dataset), 6/20 (evaluation with testing dataset), 7 (evaluated based on feature importance), 9 (second model is developed based on previous active order data), 10 (active order attributes), 11 (developed with a target variable), 12 (second model developed with time values), 13 (random forest initialization), 14 (second model is trained using previous good order data), 15 (second model is evaluated using testing dataset), 16 (second model is evaluated using metric) appear to add additional steps to the abstract idea, implemented by generic computers. To the extent these claims describe the training, testing and updating steps for machine learning models, these appear to also add steps that fall under mathematical concepts. These claims neither introduce a new abstract idea nor additional limitations which are significantly more than an abstract idea. They provide descriptive details that offer helpful context, but have no impact on statutory subject matter eligibility.
Therefore the limitations on the invention, when viewed individually and in ordered combination are directed to in-eligible subject matter.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALLEN C CHEIN whose telephone number is (571)270-7985. The examiner can normally be reached Monday-Friday 8am -5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Florian Zeender can be reached at (571) 272-6790. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALLEN C CHEIN/Primary Examiner, Art Unit 3627
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