DETAILED ACTION
This Non-Final action is responsive to the application filed 1/31/2024.
In the application Claims 1-20 are pending. Claims 1 and 18 are the independent claims.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
4. Acknowledgement is made to applicant’s claim for priority to provisional applications 63/482679, filed 2/1/2023.
Drawings
5. The Drawings filed on 1/31/2024 have been approved.
Claim Objections
6. Claim 1 is objected to because of the following grammatical informalities: The claim recites “…a tool for selecting datasets, characterizing a type of event, for training the AI module…”. However, the manner in which the claim is drafted regarding the tool is grammatically incorrect has it separates the tool for selecting the dataset from the characterization of the event and the training. For example, if the tool selects and characterizes event type it should recite language similar to: “a tool for selecting datasets and characterizing a type of event for training the AI module;”. Appropriate corrections are required.
Claim Rejections - 35 USC § 112
7. The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
8. With respect to the first prong of this analysis, a claim element that does not include the term “means” or “step” triggers a rebuttable presumption that 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, does not apply. When the claim limitation does not use the term “means,” examiners should determine whether the presumption that 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, paragraph 6 does not apply is overcome. The presumption may be overcome if the claim limitation uses a generic placeholder (a term that is simply a substitute for the term “means”). The following is a list of non-structural generic placeholders that may invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, paragraph 6: “mechanism for,” “module for,” “device for,” “unit for,” “component for,” “element for,” “member for,” “apparatus for,” “machine for,” or “system for.”. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim elements in this application that use the word “tool for…” are presumed to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Claim 1 recites:
“tool for selecting datasets”
“tool for enhancing datasets”
“tool for building the AI model”
The “tool for” is a generic placeholder of the structure while the selecting, enhancing and building are the function. Thus, the tool lacks structural meaning and is described purely by its functions without details of its implementation.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may amend the claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
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.
9. Claims 1 and 18 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites in step i) “…training the AI module…”
However, there is insufficient antecedent basis for the underlined terms in the claim. Nowhere does the claim previous recite “a AI module”, instead it recites an “AI model”. The model and module are not the same has “module” is self-contained versus “model” being a representation.
Claim 1 recites: “…characterizing a type of event…” & “…match parameters of the datasets to sounds…” & “operating in a field of operation”
Claim 18 recites: “…characterizing a type of event…” & “…match parameters of the datasets to sounds…” & “during a field operation”
however, it is unclear as to the scope of these phrases as used in the claims, which are vague and render the claims indefinite. It’s unclear as to the extent an event is adequately characterized and by whom. For example, it’s unclear what constitutes proper “characterization” for selection by the tool. Furthermore, is the characterizing performed by manual labeling or automated classification or a combination? The word “match” lacks clear meaning has it cannot be determined what constitutes a proper match and which parameters are being matched. Reciting a field of operation encompasses virtually any occurrence related to a vehicle. For example, is the field a virtual testing operation or real-world deployment? Thus, one of ordinary skill in the art cannot determine the scope of these limitations. Appropriate corrections are required.
Dependent claims 2-17 and 19-20 are rejected under 35 U.S.C. 112(b) has they inherit the deficiency of the Independent 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.
10. Claims 1 and 18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without significantly more.
The determination of whether a claim recites patent ineligible subject matter is a 2-step inquiry.
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), see MPEP 2106.03, or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis: see MPEP 2106.04
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon? see MPEP 2106.04(II)(A)(1)
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? see MPEP 2106.04(II)(A)(2) and 2106.05(a) thru (d) for explanations.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? see MPEP 2106.05
101 Analysis – Step 1
Claim 1 is directed to “A system …” (machine). Claim 18 is directed to “A method…” (process). Therefore, the claims are within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes. see MPEP 2106(A)(II)(1) and MPEP 2106.04(a)-(c)
Independent claim 1 includes limitations that recite an abstract idea (emphasized below [with the category of abstract idea in brackets]). Furthermore, Independent claim 18 recites similar subject matter and are rejected under the same rationale.
Claim 1. A system comprising: a processor system; b) a memory system, the memory system storing machine instructions, which, when implemented, cause the processor system to
generate tools for building an Artificial Intelligence (AI) model [MPEP 2106.05(f) Mere Instructions to Apply an Exception] that identifies events associated with a vehicle [mental process];
the tools including i) a tool for selecting datasets, characterizing a type of event, for training the AI module [mental process];
ii) a tool for enhancing the datasets, the tool for enhancing the datasets being configured to alter the datasets to match parameters of the datasets to sounds, as received at the AI module while operating in a field of operation [mathematical concept]; and
iii) a tool for building the AI model based on the datasets that were enhanced [mathematical concept]
The Examiner submits that the foregoing bolded limitation(s) above: constitute a “mathematical concept” & “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind or via pen/paper.
Claim 1 recites the process of identifying events associated with a vehicle which a user can perform based on observation, for example a door closing/opening event or vehicle starting its engine. In addition, selecting a dataset and characterizing an event can be performed mentally by a user choosing the dataset and then classifying it as related to different engine sounds. These limitations fall under a mental process has it involves human observations, evaluations, judgements and opinions.
Furthermore, enhancing the dataset based on parameter matching and building an AI model are processes that require mathematical algorithms/operations which fall under mathematical concepts.
Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. see MPEP 2106.04(II)(A)(2) and MPEP 2106.04(d)(2). It must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations”, while the bolded portions continue to represent the “abstract idea”.):
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application. Regarding the additional limitations of “generate tools for building”. The Examiner submits that these limitations describe generic instructions to apply the abstract idea using a computer.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the Revised Guidance, representative independent claims 1 and 18 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “a processor system” & “a memory system”, amounts to nothing 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. And as discussed above the examiner submits that these limitations are insignificant extra-solution activities. See MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015) in addition to -Collecting information, analyzing it, and displaying certain results of the collection and analysis (Electric Power Group), Collecting data, recognizing certain data within the collected data set and storing the recognized data in memory (Content Extraction).
Dependent claims 2-4, 12 and 19, -do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claim are directed toward additional aspects of the judicial exception and do not integrate the judicial exception into a practical application. The claims describe adding noise, setting signal-to-noise ratios and adjusting gains, detecting event ranges and feature extraction. These elements fall under mathematical concepts has they involve statistical data processing and mathematical transformation of datasets which is an abstract idea. Therefore, the claims are not patent eligible under the same rationale as provided for in the rejection of the Independent claims. Therefore, the claims are ineligible under 35 USC §101.
Dependent claims 5-8, -do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claim are directed toward additional aspects of the judicial exception and do not integrate the judicial exception into a practical application. The claims describe steps of filtering, selecting and characterizing which fall under mental process. These limitations fall under a mental process has it involves human observations, evaluations, judgements and opinions. Therefore, the claims are not patent eligible under the same rationale as provided for in the rejection of the Independent claims. Therefore, the claims are ineligible under 35 USC §101.
Dependent claims 9-11, 15-17 and 20, -do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claim are directed toward additional aspects of the judicial exception and do not integrate the judicial exception into a practical application. The claims describe placement of AI modules in various locations of a vehicle for data collection, storage of an AI model and use of sensors which amounts to data gathering which fall under Insignificant Extra-Solution Activity. Therefore, the claims are not patent eligible under the same rationale as provided for in the rejection of the Independent claims. Therefore, the claims are ineligible under 35 USC §101.
Dependent claim 13, -do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claim are directed toward additional aspects of the judicial exception and do not integrate the judicial exception into a practical application. The claim describes an interrupt that communicates with an AI module and then provides an alert to another device based on detected event. However, at its core the claim is detecting events via AI processing and sending alerts (data transmission) which fall under post-solution activity under Insignificant Extra-Solution Activity. Thus, the claims only send the notification/alert to a device without modifying or describing how the alert improves its operational features. Therefore, the claims are not patent eligible under the same rationale as provided for in the rejection of the Independent claims. Therefore, the claims are ineligible under 35 USC §101.
Dependent claims 14, do describe significantly more than an abstract idea has they involve specific technical implementation with physical components via IoT modem and position locator.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
11. Claim(s) 1-2, 4-13, 16 and 18-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sallem (U.S. Pub 2021/0065733, published Mar. 4, 2021).
Regarding Independent claim 1, Sallem discloses A system comprising:
a processor system; b) a memory system, the memory system storing machine instructions, which, when implemented, cause the processor system to generate tools for building an Artificial Intelligence (AI) model that identifies events associated with a vehicle (see paragraphs 66-67, discloses a processor and memory system for building a model via machine learning (ML) technologies and audio classification for vehicle application including classifier training to identify sound based events of the vehicle);
the tools including i) a tool for selecting datasets, characterizing a type of event, for training the AI module (see paragraphs 51 and 54, discloses receiving audio data of sounds in an environment and used for training machine learning object classifier with audio frames labeled based on a type of objects. Further disclosing augmentation selection unit that selects frames of the audio data to augment. In addition, paragraphs 31 and 47, discloses characterization of environmental and/or vehicle events);
ii) a tool for enhancing the datasets, the tool for enhancing the datasets being configured to alter the datasets to match parameters of the datasets to sounds, as received at the AI module while operating in a field of operation (see paragraphs 53 and 56, discloses expanding audio data into larger diverse audio data set via selectively augmenting frames of the audio data 401. Further describing multiple augmentation techniques such as temporal modification augmentation, distance adjustment augmentation and environmental augmentation technique that includes inserting noise. Also disclosing simulating the sound being emitted from the object in different environments and operational states while matching field conditions through environmental noise simulation); and
iii) a tool for building the AI model based on the datasets that were enhanced (see paragraphs 7, 51 & 53, discloses generating augmented audio dataset 402 for training ML object classifier. Further describing enhanced datasets via allowing the ML classifier to be trained to classify sound measurements corresponding to object types such as with a lower generalization error and increased robustness).
Regarding Dependent claim 2, with dependency of claim 1, Sallem discloses the tool for enhancing the datasets including a tool for adding sounds to the datasets, the sounds characterizing a background noise (see paragraphs 6 and 56, including the explanation provided in the Independent claim).
Regarding Dependent claim 4, with dependency of claim 2, Sallem discloses the tool for adding the sounds including a selection for setting a gain that determines the amplitude of the background noise (see paragraphs 6 and 56, including the explanation provided in the Independent claim).
Regarding Dependent claim 5, with dependency of claim 1, Sallem discloses a tool for filtering the datasets the tool for the filtering including a tool for selecting a range of mean values of a parameter characterizing an event to accept in a set of filtered data (see paragraphs 29 & 38, including the explanation provided in the Independent claim).
Regarding Dependent claim 6, with dependency of claim 5, Sallem discloses the tool for the filtering including a tool for selecting a range of sample rates to accept in a set of data (see paragraphs 29 & 38, including the explanation provided in the Independent claim).
Regarding Dependent claim 7, with dependency of claim 5, Sallem discloses the tool for the filtering including a tool for selecting a range of precisions of data to accept in a set of the data (see paragraphs 29 & 38, including the explanation provided in the Independent claim).
Regarding Dependent claim 8, with dependency of claim 5, Sallem discloses the tool for the filtering including a tool for selecting a range of alignments accepted a set of data (see paragraphs 29 & 38, including the explanation provided in the Independent claim).
Regarding Dependent claim 9, with dependency of claim 1, Sallem discloses AI modules located in the vehicle (see paragraphs 7 & 31, including the explanation provided in the Independent claim).
Regarding Dependent claim 10, with dependency of claim 9, Sallem discloses one of the AI modules being placed on a bell housing of an engine of the vehicle (see paragraphs 23-25, including the explanation provided in the Independent claim).
Regarding Dependent claim 11, with dependency of claim 9, Sallem discloses one of the AI modules being placed in a wheel well of the vehicle (see paragraphs 29 & 38, including the explanation provided in the Independent claim).
Regarding Dependent claim 12, with dependency of claim 1, Sallem discloses the machine instructions include instructions, which, when implemented by the processor system, cause the system to determine a range of the events that are detectable by the datasets (see paragraphs 51 & 53, including the explanation provided in the Independent claim).
Regarding Dependent claim 13, with dependency of claim 1, Sallem discloses an interrupt that communicates with the AI module, which is configured to send an alert to another device, when a specified event is detected (see paragraphs 7 & 31, including the explanation provided in the Independent claim).
Regarding Dependent claim 16, with dependency of claim 1, Sallem discloses an optical sensor, and the datasets include a combination of optical data and acoustic data (see paragraphs 22-24, 32 & 43, including the explanation provided in the Independent claim).
Regarding Independent claim 18, Sallem discloses A method comprising:
building, by a machine, an Artificial Intelligence (AI) module, the machine including a processor system and a memory system, the memory system storing machine instructions, which, when implemented by the processor system, causes the machine to implement the method, the building of the AI module including b) selecting datasets, by a data selection tool, the datasets characterizing a type of event, for training the AI module (see paragraphs 51 and 54, discloses receiving audio data of sounds in an environment and used for training machine learning object classifier with audio frames labeled based on a type of objects. Further disclosing augmentation selection unit that selects frames of the audio data to augment. In addition, paragraphs 31 and 47, discloses characterization of environmental and/or vehicle events); and
d) enhancing the datasets by a toolset for enhancing data, the enhancing including altering the data to match parameters of the datasets to sounds received at the AI module during a field operation (see paragraphs 53 and 56, discloses expanding audio data into larger diverse audio data set via selectively augmenting frames of the audio data 401. Further describing multiple augmentation techniques such as temporal modification augmentation, distance adjustment augmentation and environmental augmentation technique that includes inserting noise. Also disclosing simulating the sound being emitted from the object in different environments and operational states while matching field conditions through environmental noise simulation); and
the building of an AI model, the AI model being based on the datasets that were enhanced (see paragraphs 7, 51 & 53, discloses generating augmented audio dataset 402 for training ML object classifier. Further describing enhanced datasets via allowing the ML classifier to be trained to classify sound measurements corresponding to object types such as with a lower generalization error and increased robustness).
Regarding Dependent claim 19, with dependency of claim 18, Sallem discloses the building of the AI module further including extracting features from the datasets (see paragraph 52, including the explanation provided in the Independent claim).
Regarding Dependent claim 20, with dependency of claim 18, Sallem discloses the datasets including prior recorded data and publicly available data (see paragraph 51, including the explanation provided in the Independent claim).
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 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.
12. Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Sallem (U.S. Pub 2021/0065733, filed Aug. 29, 2019) in view of Sharma (U.S. Pub 2021/0287661, filed Mar. 10, 2021).
Regarding Dependent claim 3, with dependency of claim 2, Sallem discloses augmentation selection unit for augmenting and altering the audio data using different augmentation techniques (see paragraph 55). Sallem fails to teach modifying a signal-to-noise-ratio. Sharma discloses the tool for adding the sounds including a selection for setting a signal-to-noise ratio (see paragraph 139, discloses an augmentation process that adds a target signal-to-noise ratio via gain factor to the audio data). It would have been obvious for one of ordinary skill in the art before the effective filing date of the application to have implemented known augmentation techniques that include signal-to-noise ratio adjustments which supports augmentation across different domains has disclosed by Sharma in paragraph 3.
13. Claims 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Sallem (U.S. Pub 2021/0065733, filed Aug. 29, 2019) in view of DeLuca (U.S. Pub 2021/0174140, filed Dec. 5, 2019).
Regarding Dependent claim 14, with dependency of claim 13, Sallem discloses a modem and GPS system has part of the vehicle audio training system (see paragraphs 38 & 68). Sallem however fails to teach IoT devices. DeLuca discloses a ML audio training system that support implementation in IoT devices (see abstract & paragraphs 20-21). It would have been obvious for one of ordinary skill in the art before the effective filing date of the application to have supported use of IoT modem has it improves connectivity and analytics has disclosed by DeLuca in paragraph 2.
Regarding Dependent claim 15, with dependency of claim 1, Sallem fails to teach support for a mobile device for communicating with the AT module. DeLuca discloses a) the AI model being stored in the AT module; b) the AT model also being stored in a memory unit on a device deployed remotely from the AT module in a network; and c) an application stored on a mobile device for communicating with the AT module and the AT module being deployed remotely from the mobile device (see paragraph 17, 19 & 32). It would have been obvious for one of ordinary skill in the art before the effective filing date of the application to have supported use of IoT devices along with mobile phone support has it improves connectivity and analytics has disclosed by DeLuca in paragraph 2.
14. Claims 17 is rejected under 35 U.S.C. 103 as being unpatentable over Sallem (U.S. Pub 2021/0065733, filed Aug. 29, 2019) in view of Brahma (U.S. Pub 2022/0261590, filed Feb. 18, 2021).
Regarding Dependent claim 17, with dependency of claim 1, Sallem fails to teach fusion of sensors that include temperature measurements. Brahma discloses a temperature sensor, and the datasets include a combination of temperature data and acoustic data (see paragraph 29, discloses sensor fusion that includes temperature and audio data for training a machine learning model). It would have been obvious for one of ordinary skill in the art to have supported sensor fusion system that comprises temperature measurements with sound has it helps to improve classification using multiple attributes in detecting vehicle faults.
It is noted that any citation [[s]] to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. [[See, MPEP 2123]]
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MANGLESH M PATEL whose telephone number is (571)272-5937. The examiner can normally be reached on M-F from 10:30 am to 7:30 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ramya P. Burgess, can be reached at telephone number 571-272-6011. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Manglesh M Patel/
Primary Examiner, Art Unit 3661
9/26/2025