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 .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-12 are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al (20220197306) in view of Namazifar et al (20240428787).
As per claim 1, Cella et al (20220197306) teaches a processor-implemented method (as an automation process – para 0002) comprising:
receiving, via an Input/Output (I/O) interface, one or more voice inputs from an inspector while inspecting an artifact, wherein the artifact is a physical entity subjected to inspection (as, human inspectors can label the data in detail, from types of defects, to favorable properties or other characteristics – see para 0367, starting with “observing interaction of a set of human inspectors….or other characteristics” with the human inspector input – see para 0367, which can be in the form of spoken input, recognized by the NLP (natural language processing) module – para 1567);
pre-processing, via one or more hardware processors, the one or more voice inputs of the inspector
transforming, via the one or more hardware processors, the predefined language text into one or more structured defects for a structured defect log on a domain model of artifact under inspection (as, the tracking of defects, as noted above, can be automated after training, and identified/categorized, using a machine learning model – see para 0367 – “or other characteristics, such that a machine learning system can learn, using the training data set, to identify the same characteristics”,
analyzing, via the one or more hardware processors, the structured defect log to map a defect name, a defect type, and a defect location to each sub-section of the physical entity represented in the domain model using the learning model to train on the voiced input of the human inspectors, in terms of defects, types of defects, and categories of desirable properties – para 0367, and then using that trained model to perform the task of labeling defects – para 0367);
and converting, via the one or more hardware processors, the analyzed one or more structured defects
For example, a service technician may capture a set of photos that show a damaged part. In embodiments, the platform may process the inputs, such as using an artificial intelligence system (such as a robotic process automation system trained on a training set of expert service visit data), to determine a recommended action, which in embodiments may involve replacement of a part and/or repair of a part. The platform may, in some such embodiments, automatically determine (such as using an artificial intelligence system, such as robotic process automation trained on an expert data set) whether a replacement part is readily available and/or whether an additive manufacturing system should produce the replacement part, such as to reduce delay, to save costs, or the like. .
As per claim 1, Cella et al (20220197306) teaches a generalized machine learning model, to learn the specific application of determining and categorizing defects (after training, would be a smaller, specialized, domain model); however, does not explicitly teach/state a generalized LLM and a smaller domain specialized model; Namazifar et al (20240428787) teaches an application of a dialog system (generating text to speech back to the user, from the user’s noise filtered speech (para 0071) in the form of a virtual assistant – para 0017), teaching a “shortlister” faster model for each domain (para 0108), off of generalized LLM’s (para 0046—0047). Therefore, it would have been obvious to one of ordinary skill in the art of language modeling to further define the language model implementation of Cella et al (20220197306) with a dialog based system with specific language models for a domain, as taught by Namazifar et al (20240428787), because it would advantageously reduce the response latency of the system (see Namazifar et al (20240428787) para 0108, reducing latency while improving the accuracy of the skill information).
As per claim 2, the combination of Cella et al (20220197306) in view of Namazifar et al (20240428787) teaches the processor-implemented method of claim 1, wherein a voice to text conversion model is integrated with the large language model and a domain model of artifact to convert one or more defects spoken by the inspector into the one or more structured defects represented by the domain model (see Cella et al (20220197306), as, human inspectors can label the data in detail, from types of defects, to favorable properties or other characteristics – see para 0367, starting with “observing interactions of a set of human inspectors….or other characteristics” from the one or more voice inputs from the inspector (as human inspector input – see para 0367, which can be in the form of spoken input, recognized by the NLP (natural language processing) module – para 1567; and see Namazifar et al (20240428787), teaching a “shortlister” faster model for each domain (para 0108), off of generalized LLM’s (para 0046—0047) ) .
As per claim 3, the combination of Cella et al (20220197306) in view of Namazifar et al (20240428787) teaches the processor-implemented method of claim 1, wherein the defect type, the defect location and the sub-section of the artifact is identified (as, human inspectors can label the data in detail, from types of defects, to favorable properties or other characteristics – see para 0367, starting with “observing interaction of a set of human inspectors….or other characteristics” from the one or more voice inputs from the inspector (as human inspector input – see para 0367, which can be in the form of spoken input, recognized by the NLP (natural language processing) module – para 1567).
As per claim 4, the combination of Cella et al (20220197306) in view of Namazifar et al (20240428787) teaches the value-chain entities (para 0270, first 4 words) processor-implemented method of claim 1, wherein the one or more structured defects are represented in at least one of an orthogonal view, a two-dimensional (2D) view, a three-dimensional (3D) view of the artifact being inspected for verification (examiner notes that the claim limitations are in the alternative format -- Cella et al (20220197306) teaches the use of the disclosed value-chain entities for “quality control processes” and “inspectors” – see these words in para 0270; and in para 0367 – performing visual inspections used in video/still images – “a process involving visual inspection using video or still images from a camera….defects or favorable properties are automatically classified and detected in a set of video or still images”).
Claims 5-8 are system claims that perform the steps found in method claims 1-4 above and as such, claims 5-8 are similar in scope and content to claims 1-4 above; therefore, claims 5-8 are rejected under similar rationale as presented against claims 1-4 above. Furthermore, Cella et al (20220197306) teaches processors executing stored instructions in memory – para 0014.
Claims 9-12 are non-transitory machine readable information storage medium claims executing steps that perform the steps found in method claims 1-4 above and as such, claims 9-12 are similar in scope and content to claims 1-4 above; therefore, claims 9-12 are rejected under similar rationale as presented against claims 1-4 above. Cella et al (20220197306)
teaches storage devices storing instructions to execute the disclosed steps – see para 1065.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see related art listed on the PTO-892 form.
Furthermore, the following references were found that teach commonly found elements in applicants specification/claims:
As to dialog based systems that synthesize speech prompts to users, using LLM’s and specific domain models:
Khorshid et al (20240119932) – para 0112, 0184
Rollwage et al (20240404514) – para 0834, back on para 0347 and para 0337
Barros (20250190466) – para 0012, 0044.
As to automatic systems tracking defects in products:
Arbel et al (20230266819) teaches tracking the types of defects found in images of products using visual inspectors (para 0049, 0054).
Ha et al (20180330511) teaches defect type tracking and using ASR, NLP as an interface – para 0093, 0141-0142.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael Opsasnick, telephone number (571)272-7623, who is available Monday-Friday, 9am-5pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Mr. Richemond Dorvil, can be reached at (571)272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael N Opsasnick/Primary Examiner, Art Unit 2658 07/19/2026
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