DETAILED ACTION
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 9 and 17 requires “generating automatically an interpretation the first plurality of syntactical elements by applying a first trained computer-implemented neural network, wherein the first trained computer implemented neural network is trained using first training syntactical elements and the first trained neural network learns to make semantic-based inferences from received syntactical elements; identifying probabilistically by applying a second trained computer implemented neural network the one or more objects in accordance with the interpreting of the first plurality of syntactical elements, wherein the second trained neural network is trained using a second training set that comprises second training syntactical elements and training images and the second trained neural network learns during training correspondences between each of a plurality of subsets of the second training syntactical elements and one or more patterns of pixels in the training images; generating a communication comprising a second plurality of syntactical elements by applying a third trained computer-implemented neural network, wherein the second plurality of syntactical elements refers to a plurality of attributes that are associated with the identified one or more objects”. The specifications do not disclose sufficient detail to demonstrate possession of these claimed neural-networks. In particular, the specifications fails to disclose the structure or architecture of any of the recite neural networks, how “semantic-based inferences” are generated or represented, how correspondences between syntactical elements and pixel patterns are learned, or any specific algorithm, model topology, or training regime enabling the claimed learning behavior. The disclosure at various paragraphs of the specifications (¶26-30, ¶33-34, ¶39-40, ¶164-165, ¶190-191) merely states that neural network perform these functions, without describing how those functions are achieved. Such high-level, result-oriented descriptions do not demonstrate possession of the full scope of the claimed invention. Dependent claims 2-8, 10-16 and 18-20 do not cure the deficiencies identified in respective independent claims 1, 9 and 17. Accordingly, these dependent claims are not supported by adequate written description for at least the same reasons as claims 1, 9 and 17.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
Claims 1, 9 and 17 broadly encompasses interpretation of textual and oral syntactical elements, probabilistic object identification in video, semantic context inference, latency-aware object identification, and training and deployment of one or more neural networks performing all of the claimed functions. However, the specifications various paragraphs (¶26-30, ¶33-34, ¶39-40, ¶164-165, ¶190-191) does not teach how to train the claimed neural networks to perform semantic inference, how probabilities and contextual inferences are computed, how latency considerations affect identification, how audio interpretation is integrated in video processing, and how a single neural network can be trained to perform all recited roles (claim 6, 14, and 20). As a result, a person of ordinary skill in the art would be required to engage in undue experimentation, including designing architectures, selecting representations, and inventing training strategies not disclosed in the specifications. Therefore, claims 1-20 are not enabled across their full scope. Dependent claims 2-8, 10-16 and 18-20 do not cure the deficiencies identified in respective independent claims 1, 9 and 17. Accordingly, these dependent claims are not enabled across their full scope for at least the same reasons as claims 1, 9 and 17.
Response to Arguments
Applicant's arguments filed 3/11/2026 directed to 35 U.S.C. 112(a) have been fully considered but they are not persuasive.
While the specifications does describe the use of neural networks in a general sense, it does not reasonably convey to one of ordinary skill in the art that the inventors had possession of the specific architecture now being claimed, namely, a system comprising three distinct trained neural networks, each having a different functional role and training paradigm as recited in claims 1, 9 and 17.
In particular, the specification consistently describes an “adaptive system” that may employ neural networks for various tasks, such as object identification or response generation, but these disclosures are generic and interchangeable, rather than indicative of a modular, multi-network pipeline. For example, the specifications explains that object identification may be performed through application of a neural network and that responses may be generated using neural networks of statistical learning approaches. However, such disclosures merely demonstrate that neural networks may be used within the system, not that the inventors possessed three separately trained neural networks with the distinct roles now claimed. The present claim requires (i) a first trained computer-implemented neural network trained using first training syntactical elements and the first trained neural network learns to make semantic-based inferences from the first plurality of syntactical elements received, (ii) a second trained neural network trained using a second training set that comprises second training syntactical elements and training images, and (iii) a third neural network that generates a communication comprising syntactical elements referring to the object attributes. The specifications does not describe this tripartite architecture, nor does it separate training processes or datasets corresponding to each of these recited neural networks.
Applicant’s reliance on disclosures relating to semantic processing and syntactical elements is likewise not persuasive. Although the specifications discusses parsing queries into syntactical elements and forming semantic chains, these descriptions are mainly framed in terms of rule-based or structure representations (e.g. semantic chains, RDF-like relationships), rather than a trained neural network that learns to make semantic-based inferences from syntactical inputs, as claimed. These cited portions of the specifications do not describe a neural network being trained on syntactical elements to perform the claimed interpretive function.
With respect to the second neural network, the specifications does disclose training using labeled images, where labels may correspond to words or phrases, and matching pixel patterns to such labels. However, this disclosure does not support the claimed requirement that the neural network is trained using “a plurality of subsets of syntactical elements” and learns “correspondences between each of those subsets…and one or more patterns of pixels”. The specifications describes associating labels with images, but does not describe partitional syntactical elements into subsets or using such subsets in a training process to learn structured correspondences with image features. Nor does the specifications describe a multimodal training paradigm in which syntactical structures, as opposed to simple labels, are jointly learned with image data in a manner required by the claim.
With respect to the third neural network, the specifications describes generating responses or explanations including assembling outputs from words or phrases and providing recommendations or attributes. However, these disclosures are consistent with template-based or rule-based generation, and do not demonstrate possession of a trained neural network that generates a communication comprising syntactical elements as claimed. The specifications does not describe training a neural network for natural language generation or otherwise indicate that the output communication is produced by a trained neural model, as opposed to deterministic or statistical assembly techniques.
Taken together, the cited portions of the specifications demonstrate that neural networks may be used generally within the disclosed system, but they do not convey possession of the specific combination of three distinct trained neural networks, each with the particular training inputs, learned relationships, and functional roles as claimed. Accordingly, the specifications does not provide adequate written description support for the full scope of the claimed invention, and the rejections under 35 U.S.C. 112(a) is maintained.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/KEVIN KY/Primary Examiner, Art Unit 2671