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
RESPONSE TO ARGUMENTS
35 USC 101 REJECTION
The examiner acknowledges the amendment of claims 1, 4, 8-9, 14-15 & 17, addition of claims 21-25 and the cancellation of claims 3, 7, 13, 16 & 20 filed 02/18/2026. After carefully reviewing applicant amendments, 35 USC 101 guidance and claim limitations, examiner respectfully disagrees.
Applicant submits amended claims are no longer directed to a mental process because they require specialized neural network models on hardware. Specifically, new claims require a multilingual transformer-based text embedding model, a CNN-based image model, MLP classifiers generating per-modality first portion probability distributions, a multimodal fusion model generating candidate code designations, probability aggregation over a first subset, and threshold based automatic transmission. Applicant concludes that above operations cannot be practically be performed by humans, mentally or with assistance, at the scale and complexity contemplated, so the claims are not directed to a mental process under Step 2A, Prong 1.
In response, examiner submits claims scope discloses the following: receiving item information, producing candidate code designations, aggregating probabilities, applying a thresholds and transmitting a selected code. Examiner submits claim still recites mathematical concepts like embeddings, probability distributions, aggregation, thresholding and classification/coding of items.
Applicant submits claims recite a specific improvement in computer functionality, not just applying it on a computer. Applicant references Enfish and McRO as analogous reasoning to overcome 35 USC 101 Alice rejection.
In response, examiner submits claim do not improve the functioning of the computer itself. Instead, claims use a computer and known ML models as tools to improve a business/administrative classification result assigning a code designation to an item. Examiner submits the claims above is not the kind of computer centric improvement at issue in Enfish, and not the specific rules-based animation automation at issue in McRO. Examiner also submits that the claims improve the quality of the output decision, not the operation of the machine. Claims do not change how the memory or processor operates, how the database stores information, or how neural network inference is executed. Examiner still views claims as using generic computing components to perform a more elaborate classification workflow.
Applicant submits the claims are integrated into a practical application because they recite concrete, non-generic ML components and coordination.
In response, examiner submits amendments are functional or specify what kinds of model to use and what outputs to produce. Examiner submits claims would need to disclose how the models are technically configured or operated in a way that meaningfully limits the claim. Examiner submits generic components do not integrate an the invention into a practical application.
Applicant submits BASCOM and Berkheimer and that specific ordered combination is more than generic tools. Applicant also submits that no prior art has been found in claims 1-20 in their current form, and argue that this supports that rationale that the ordered combination is not well-understood, routine, and conventional.
In response, examiner submits a distinction between no prior art being found and the claims being not well understood, routine and conventional in the Alice test. Patent Eligibility and Novelty are distinct as a claim can be new and still directed to an abstract idea implemented via generic components.
Applicant submits claims require a particular ML structure and sequence. Applicant also submits claims specific multimodal ML technique and therefore avoids preemption.
In response, examiner submits the absence of preemption does not establish eligibility. Examiner submits claims are results oriented as they claim the desired output structure and model roles at a high level, without sufficiently reciting the technical mechanics that would confine the claims to a concrete implementation. Examiner submits claims are broad enough to cover many ways of using a transformer, CNN, MLP and fusion model to assign code designations, which means it remains directed to the abstract idea of automated classification/coding using generic computing technology.
In view of above arguments, examiner submits rejection is sufficient and respectfully maintained.
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 1-2, 4-6, 8-12, 14-15, 17-19 & 21-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to as ineligible under subject eligibility test. In the Subject Matter Eligibility Test for Products and Processes (Federal Register, Vol. 79, No. 241, dated Tuesday, December 16, 2014, page 74621), The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional device elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea.
Claims 1, 9 & 15
Step 1
This step inquires “is the claim to a process, article of machine, manufacture or composition of matter?” Yes,
Claims 1 & 15 - “Systems” or “Non-Transitory CRM” are machines.
Claim 9 – “Method” is a process.
Step 2A - Prong 1
This step inquires “does the claim recite an abstract idea, law or natural phenomenon”. This claim appears to directed to an abstract idea.
The limitation of “ a non-transitory memory; a processor communicatively coupled to the non-transitory memory, wherein the processor is configured to read a set of instructions to: receive, from a requesting system, a request for a code designation of an item; receive, from the requesting system, a textual description and one or more images associated with the item; generate, using a multilingual transformer-based text embedding model, text embeddings from the textual description and classify, via a multi-layer perceptron, a first probability distribution associated with a first portion of the code designation based on the text embeddings; generate, using a convolutional neural network-based image model, image embeddings from the one or more images and classify, via the multi-layer perceptron, a second probability distribution associated with the first portion of the code designation based on the image embeddings; generate, using a multimodal fusion model, a plurality of candidate code designations based on the first probability distribution and the second probability distribution, wherein each candidate code designation in the plurality of candidate code designations comprises the first portion of the code designation and a second portion of the code designation; aggregate respective probabilities of respective candidate code designations in a first subset of the plurality of candidate code designations; and in accordance with a determination that an aggregated probability of the first subset of the plurality of candidate code designations is larger than a threshold: transmit a selected code designation from the first subset of the plurality of candidate code designations to the requesting system as the code designation associated with the item.”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind (e.g. mathematical concepts, mental processes or certain methods of organizing human activity) but for the recitation of generic computer components. That is, other than reciting “a non-transitory memory; a database configured to store a trained text classification model and a trained image classification model; a processor communicatively coupled to the non-transitory memory, wherein the processor is configured to read a set of instructions” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “a non-transitory memory; a database configured to store a trained text classification model and a trained image classification model; a processor communicatively coupled to the non-transitory memory, wherein the processor is configured to read a set of instructions” language, “receiving, obtaining, generating, aggregate, transmit” in the context of this claim encompasses covers performance of the limitation in the mind (e.g. mathematical concepts, mental processes or certain methods of organizing human activity).
STEP 2A – PRONG 1 - CONCLUSION
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A - Prong 2
This step inquires “does the claim recite additional elements that integrate the judicial exception into a practical application”. This judicial exception is not integrated into a practical application. In particular, the claim recites two additional element – using a “a non-transitory memory; a database configured to store a trained text classification model and a trained image classification model; a processor communicatively coupled to the non-transitory memory, wherein the processor is configured to read a set of instructions” to perform “receiving, obtaining, generating, aggregate, transmit” steps. The “a non-transitory memory; a database configured to store a trained text classification model and a trained image classification model; a processor communicatively coupled to the non-transitory memory, wherein the processor is configured to read a set of instructions” are recited at a high-level of generality (i.e., as a generic processor) “ a non-transitory memory; a processor communicatively coupled to the non-transitory memory, wherein the processor is configured to read a set of instructions to: receive, from a requesting system, a request for a code designation of an item; receive, from the requesting system, a textual description and one or more images associated with the item; generate, using a multilingual transformer-based text embedding model, text embeddings from the textual description and classify, via a multi-layer perceptron, a first probability distribution associated with a first portion of the code designation based on the text embeddings; generate, using a convolutional neural network-based image model, image embeddings from the one or more images and classify, via the multi-layer perceptron, a second probability distribution associated with the first portion of the code designation based on the image embeddings; generate, using a multimodal fusion model, a plurality of candidate code designations based on the first probability distribution and the second probability distribution, wherein each candidate code designation in the plurality of candidate code designations comprises the first portion of the code designation and a second portion of the code designation; aggregate respective probabilities of respective candidate code designations in a first subset of the plurality of candidate code designations; and in accordance with a determination that an aggregated probability of the first subset of the plurality of candidate code designations is larger than a threshold: transmit a selected code designation from the first subset of the plurality of candidate code designations to the requesting system as the code designation associated with the item.” such that it amounts no more than mere instructions to apply the exception using a generic computer component.
STEP 2A – PRONG 2 - CONCLUSION
Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B
The critical inquiry here is does the claim recite additional elements that amount to “significantly more” than the judicial exception? The claim does 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 using a “a non-transitory memory; a database configured to store a trained text classification model and a trained image classification model; a processor communicatively coupled to the non-transitory memory, wherein the processor is configured to read a set of instructions” to perform “receiving, obtaining, generating, aggregate, transmit “steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Dependent Claims
As to claims 2, 10 & 18, this claim is directed to mental process (“(1) looking at a list of candidate codes sharing the same first portion (2) picking the one with the highest probability/confidence (3) associate the code with the item”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claims 4 & 17, this claim is directed to generic computer components (“trained text classification model”), mental process (“human classifier could read the description and also use category (e.g. shoes, electronics) to help assign a code”) and insignificant extra-solution activity (“routine data preprocessing and data analysis”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claims 5 & 11, this claim is directed to generic computer components (“second trained text classification model”), mental process (“if a human could see low confidence, consult a second expert (second model) and if the second expert first portion suggestion matches one of the existing candidates, pick that code and send it”) and insignificant extra-solution activity (“picking a code”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claim 6, 12 & 19, this claim is directed to mental process (“use the description and images, apply country specific rules, decide the more specific code digits”) and insignificant extra-solution activity (“abstract idea of commercial coding/classification”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claims 8 & 14, this claim is directed to generic computer components (“training database, model”), mental process (“data augmentation tied to classification”) and insignificant extra-solution activity (“data preparation/model training”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claim 21, this claim is directed to generic computer components (“convolutional neural network based image model, multi-layer perception”), mental process (“human looking at product images, taking into account category information, and mentally deciding the first code portion”) and insignificant extra-solution activity (“routine feature extraction/preprocessing/data analysis”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claim 22, this claim is directed to generic computer components (“multimodal fusion model, meta classifier, custom multi layer perception having two hidden layers”), mental process (“combining text information and image information to choose the first code option, including resolving ambiguity when one source is missing or unclear”) and insignificant extra-solution activity (“routine model combination/tuning/data combination”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claim 23, this claim is directed to generic computer components (“convolutional neural network based image model”), mental process (“considering alternate views or modified versions of the same item image to assist categorization”) and insignificant extra-solution activity (“data preparation/ model training”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claim 24, this claim is directed to generic computer components (“text classification model, multilingual transform based text embedding model”), mental process (“reading the item title/category/details etc… and using said information to help assign a code.”) and insignificant extra-solution activity (“routine data preprocessing and data analysis”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claim 25, this claim is directed to generic computer components (“trained country specific classifier, K-Nearest Neighbor classifier”), mental process (“use the description and images, apply country specific rules and decide the more specific code digits”) and insignificant extra-solution activity (“routine classifier use/post classification refinement”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
CONCLUSION
No prior art has been found for claims 1-2, 4-6, 8-12, 14-15, 17-19 & 21-25 in their current form.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Stephen P Coleman whose telephone number is (571)270-5931. The examiner can normally be reached Monday-Thursday 8AM-5PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Moyer can be reached at (571) 272-9523. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Stephen P. Coleman
Primary Examiner
Art Unit 2675
/STEPHEN P COLEMAN/Primary Examiner, Art Unit 2675
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