DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-7, 9-16, 18-20 have been presented for examination based on the amendment filed on 6/3/2026.
Claims 1, 10 and 19 are amended.
Rejection for claims 1-7, 9-16, 18-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph is WITHDRAWN in view amendment.
Claim(s) 1-6, 9-15, 18-20 are newly rejected under 35 U.S.C. 103 as being unpatentable over US 9824453 B1 by Collins; Stephen M. et al., in view of US 20080052134 A1 by Nowak; Vikki et al., in view of US 20240303745 A1 by Fields; Brian et al., further in view of US PGPUB No. US 20260023765 A1 by Madisetti; Vijay et al (Priority to July 7, 2023), further in view of US PGPUB No. US 20250045491 A1 by RESCHKA; Andreas.
Claim(s) 7 & 16 are newly rejected under 35 U.S.C. 103 as being unpatentable over US 9824453 B1 by Collins; Stephen M. et al., in view of US 20080052134 A1 by Nowak; Vikki et al., in view Fields, in view of Madisetti, in view of RESCHKA, further in view of US 20230116639 A1 by Patt; Theo et al.
This action is made Final.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Response to Arguments
(Argument 1) Applicant has argued in Remarks Pg.8-10:
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(Response 1) In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
New prior art Madisetti teaches receive, over the one or more networks, an LLM summary of the vehicle incident from the LLM engine or service (Madisetti: Fig.9-13 showing LLM as h-LLMs producing relevant summarization as output; [0153] "... [0153] Aspects of a LASER system according to an embodiment of the invention include splitting long inputs into multiple blocks/chunks (e.g. 10 k-50 k tokens each) and processing the blocks/chunks separately, ranking and/or scoring the outputs from each block/chunk to determine most relevant content, constructing new condensed blocks/chunks from the highest scoring outputs for next round and/or delete irrelevant information, iteratively processing the blocks/chunks through an LLM, selecting the best outputs, and condensing or summarizing the best outputs, and gradually concentrating the context into fewer but more focused subsets of documents or chunks or summaries....", Madisetti [0143] "... An example use case can be a network of Vision LLMs (one for each car on a road or in a platoon) which exchange information as to unusual traffic incidents that the autonomous car may not have been trained with to handle...."; Also see [0110]-[0112]).
Madisetti can process text, image, audio and video to generate the output (summary) using LLM (h-LLMs) as shown in Fig.4, [0163]-[0164], [0241] & [0248] at least.
Further, applicant has summarily argued that Reschka does not teach based on the information corpus and the LLM summary, generate, without further user input. Examiner has mapped this limitation Reschka teaches based on the information corpus and the LLM summary, generate, without further user input (Reschka: [0046] "... Additionally or alternatively, the logic 113 may generate a media representation, such as an image or a video, of the scenario. ..." ; Fig.9 element 921 & [0067] "...[0067] In some implementations, captured or obtained media from both an interior and an exterior of a vehicle may also enhance a generated scenario. For example, in FIG. 9, an image or video 921 (hereinafter “image”) may show a representation of both an interior and an exterior of one or more vehicles during an accident. The logic 113 may augment the raw data 221 with the image 921 to generate a scenario 931, which depicts information of participants and their characteristics or behaviors. ...", Fig. 1 & [0043] "... [0043] FIG. 1 illustrates an example implementation or scenario (hereinafter “implementation”), of a computing system 102 that automatically generates seed scenarios from a corpus of data which may encompass accident reconstruction reports, accident reports, incident reports, potential or suspicious activity reports, traffic reports, and/or other reports of actual events, accidents, near-miss events, or disengagements (hereinafter “events”)...." - the scenario also vehicle incidents like accident reconstruction. The scenarios are generated based on LLM report as shown in [0038] "... [0038] In order to improve accuracy and effectiveness of testing scenarios for autonomous and semi-autonomous vehicles, a computing system, which may include or be associated with machine learning components such as Large Language Model (LLM), may generate testing scenarios based on reports, logs, and/or other data from external databases. ..." [0006]-[0007]).
Emphasis is shown as LLM shows the representation as animation based on plurality of user input. See annotated Fig.9 on the next page in context of Reschka [0046] and [0067] cited.
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(Argument 2) Applicant has argued in Remarks Pg.10:
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(Response 2) Madisetti teaches optimized AI prompt is generated by an AI prompt generator trained, based on outputs of the LLM engine or service (Madisetti: Fig.9-10 showing generation of optimized AI prompt as derived prompts for different categories, which are based on output of h-LLMs – See [0110] "... The AI Input Broker 810 and Output Broker 814 update a local AI Broker Database 820 with the results of the request's path through its hierarchy and create an index of “derived requests” that may be used in future to select which set of “derived requests” [optimized derived prompt/request 822/924] an incoming request may fall into for further processing...."
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; [0010]-[0111]), to generate AI prompts that are optimized to increase a relevance and a brevity of the outputs of the LLM engine or service (Madisetti : Fig.10 context aware prompts 922; [0111] "... The prompt is sent to AI Input Broker 810 which generates multiple derived prompts for different categories 924. The prompts are converted into embeddings using multiple embedding models 926. The prompt embeddings 928 are sent to a vector database 930 which returns a list of knowledge documents 934 that are relevant to the prompt based on the similarity of their embeddings to the user's prompt. The knowledge documents 934 are sent to the AI Input Broker 810 which creates new context-aware prompts based on the user's initial prompt 916, derived prompts 924 and the retrieved knowledge documents 934 as context and sends it to multiple h-LLMs 912....") by avoiding language that causes the LLM engine or service to focus on details that are of less relevance (Madisetti: Fig.11, [0112] showing output is ranked and the AI-Broker database from which derived prompts/requests are generated are updated based on the LLM output - "... [0112] Referring now to FIG. 11 is an illustration of an AI Broker for processing results from multiple h-LLMs, is described in more detail. Results produced by multiple h-LLMs 1000 are sent to an AI Output Broker 1002 which performs tasks such as assigning priorities 1004 and weights 1006 to the results, filtering 1010, ranking 1012 and caching 1014. The AI Output Broker 1002 provides an API interface 1016 for configuring and managing various aspects of the broker. An AI Broker Database 1020 stores the results along with the meta-data information such as the request path. AI Broker Database 1020 creates an index of “derived requests” that may be used in future to select which set of “derived requests” an incoming request may fall into for further processing....") to the vehicle incident (Madisetti [0143] "... An example use case can be a network of Vision LLMs (one for each car on a road or in a platoon) which exchange information as to unusual traffic incidents that the autonomous car may not have been trained with to handle....").
No new arguments are made for the remaining limitations or dependent claims. Examiner respectfully maintains the rejection.
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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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-6, 9-15, 18-20 are newly rejected under 35 U.S.C. 103 as being unpatentable over US 9824453 B1 by Collins; Stephen M. et al., in view of US 20080052134 A1 by Nowak; Vikki et al., in view of US 20240303745 A1 by Fields; Brian et al., further in view of US PGPUB No. US 20260023765 A1 by Madisetti; Vijay et al (Priority to July 7, 2023), further in view of US PGPUB No. US 20250045491 A1 by RESCHKA; Andreas.
Regarding Claims 1, 10 and 19 (Updated 2/27/2026)
Collins teaches (Claim 1) A computing system (Collins: Fig.1 Col.6 Lines 34-Col.8 line 20)/ (Claim 10). A non-transitory computer readable medium storing instructions that, when executed by one or more processors of a computing system (Collins: Fig.1 Showing elements 103, 105, 107, 115) , cause the computing system to \ (Claim 19). A computer-implemented method of collision reconstruction, the method being performed by one or more processors (Collins: Fig.3) : comprising: a network communication interface (Collins: Fig.1 element 127, 123) ; one or more processors (Collins: Fig.1 element 103) ; and a memory storing instructions that, when executed by the one or more processors (Collins: Fig.1 element 105, 107, 115) , cause the computing system to: obtain an information corpus (Collins : Fig.3 Col.8 Lines 33-46) corresponding to a vehicle incident involving a vehicle over one or more sessions with a user (Collins: Fig.3 Col.9 Lines 30-Col.10 Line 49) ; (Collins: Col.56 Lines 52-Col.58 Line 22 "... In step 5406, the computing device may generate a 3D image of the vehicle based on the captured images...." where the 3D CAD image from captured images generation and comparison can be understood as vehicle incident simulation).
Alternate Interpretation of “a vehicle incident simulation of the vehicle incident”: If this interpreted as actual vehicle movement simulation (like animation) due to which the vehicle damaged rejection is further made below:
Collins does not explicitly teach based on the information corpus, generate a vehicle incident simulation of the vehicle incident, under the alternate interpretation.
Nowak teaches based on the information corpus, generate a vehicle incident simulation of the vehicle incident (Nowak: Fig.2A-2B [0030]-[0036] as "... Using an interactive help utility, such as a help wizard, a user may create and revise the animation to create a precise and accurate recreation....").
Fields teaches generate an optimized artificial intelligence (AI) prompt based on the information corpus (Fields: [0055] "... [0055] The ML chatbot may include and/or derive functionality from a Large Language Model (LLM). The ML chatbot may be trained on a server [hence optimized], such as server 105, using large training datasets of text which may provide sophisticated capability for natural-language tasks, such as answering questions and/or holding conversations. The ML chatbot may include a general-purpose pretrained LLM which, when provided with a starting set of words (prompt) as an input, may attempt to provide an output (response) of the most likely set of words that follow from the input....") ; transmit, over one or more networks, the optimized AI prompt to a large language model (LLM) engine or service executing on a remote computing system (Fields: [0055]-[0057]; [0057] "... [0057] The system and methods to generate and/or train an ML chatbot model (e.g., via the ML module 140 of the server 105) which may be used the an ML chatbot, may consists of three steps: (1) a Supervised Fine-Tuning (SFT) step where a pretrained language model (e.g., an LLM) may be fine-tuned on a relatively small amount of demonstration data curated by human labelers to learn a supervised policy (SFT ML model) which may generate responses/outputs from a selected list of prompts/inputs....") ; as of AI/ML chatbot to for generating images (Fields: [0072]-[0074]; [0073] "... Other types of generative AI/ML may use the GAN, the transformer model, and/or other types of models and/or algorithms to generate: (i) realistic images from sketches, which may include the sketch and object category as input to output a synthesized image;... With the appropriate algorithms and/or training, generative AI/ML may produce various types of multimedia output and/or content which may be incorporated into a customized presentation, e.g., via an AI and/or ML chatbot (or voice bot). "); [0074] "... The trained ML chatbot may generate output such as images, video, slides (e.g., a PowerPoint slide), virtual reality, augmented reality, mixed reality, multimedia, blockchain entries, metaverse content, or any other suitable components which may be used in the customized presentation....")..
Collins and Fields and/or Collins, Novak and Fields combination do not explicitly teach based on the information corpus and the LLM summary, generate a vehicle incident simulation of the vehicle incident (Emphasis on bold and underlined as additional limitation not being taught: corpus+LLM summary -> simulation).
Madisetti teaches optimized AI prompt is generated by an AI prompt generator trained, based on outputs of the LLM engine or service (Madisetti: Fig.9-10 showing generation of optimized AI prompt as derived prompts for different categories, which are based on output of h-LLMs – See [0110] "... The AI Input Broker 810 and Output Broker 814 update a local AI Broker Database 820 with the results of the request's path through its hierarchy and create an index of “derived requests” that may be used in future to select which set of “derived requests” [optimized derived prompt/request 822/924] an incoming request may fall into for further processing...."
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; [0010]-[0111]), to generate AI prompts that are optimized to increase a relevance and a brevity of the outputs of the LLM engine or service (Madisetti : Fig.10 context aware prompts 922; [0111] "... The prompt is sent to AI Input Broker 810 which generates multiple derived prompts for different categories 924. The prompts are converted into embeddings using multiple embedding models 926. The prompt embeddings 928 are sent to a vector database 930 which returns a list of knowledge documents 934 that are relevant to the prompt based on the similarity of their embeddings to the user's prompt. The knowledge documents 934 are sent to the AI Input Broker 810 which creates new context-aware prompts based on the user's initial prompt 916, derived prompts 924 and the retrieved knowledge documents 934 as context and sends it to multiple h-LLMs 912....") by avoiding language that causes the LLM engine or service to focus on details that are of less relevance (Madisetti: Fig.11, [0112] showing output is ranked and the AI-Broker database from which derived prompts/requests are generated are updated based on the LLM output - "... [0112] Referring now to FIG. 11 is an illustration of an AI Broker for processing results from multiple h-LLMs, is described in more detail. Results produced by multiple h-LLMs 1000 are sent to an AI Output Broker 1002 which performs tasks such as assigning priorities 1004 and weights 1006 to the results, filtering 1010, ranking 1012 and caching 1014. The AI Output Broker 1002 provides an API interface 1016 for configuring and managing various aspects of the broker. An AI Broker Database 1020 stores the results along with the meta-data information such as the request path. AI Broker Database 1020 creates an index of “derived requests” that may be used in future to select which set of “derived requests” an incoming request may fall into for further processing....") to the vehicle incident (Madisetti [0143] "... An example use case can be a network of Vision LLMs (one for each car on a road or in a platoon) which exchange information as to unusual traffic incidents that the autonomous car may not have been trained with to handle....").
Madisetti further teaches receive, over the one or more networks, an LLM summary of the vehicle incident from the LLM engine or service (Madisetti: Fig.9-13 showing LLM as h-LLMs producing relevant summarization as output; [0153] "... [0153] Aspects of a LASER system according to an embodiment of the invention include splitting long inputs into multiple blocks/chunks (e.g. 10 k-50 k tokens each) and processing the blocks/chunks separately, ranking and/or scoring the outputs from each block/chunk to determine most relevant content, constructing new condensed blocks/chunks from the highest scoring outputs for next round and/or delete irrelevant information, iteratively processing the blocks/chunks through an LLM, selecting the best outputs, and condensing or summarizing the best outputs, and gradually concentrating the context into fewer but more focused subsets of documents or chunks or summaries....", Madisetti [0143] "... An example use case can be a network of Vision LLMs (one for each car on a road or in a platoon) which exchange information as to unusual traffic incidents that the autonomous car may not have been trained with to handle...."; Also see [0110]-[0112]) ;
Reschka teaches based on the information corpus and the LLM summary, generate, without further user input (Reschka: [0071] shows no user input to filter out data and further in [0067] & [0043] as mapped below there is no mention of user intervention as by use of word “automatically”) , a vehicle incident simulation of the vehicle incident (Reschka: [0046] "... Additionally or alternatively, the logic 113 may generate a media representation, such as an image or a video, of the scenario. ..." ; Fig.9 element 921 & [0067] "...[0067] In some implementations, captured or obtained media from both an interior and an exterior of a vehicle may also enhance a generated scenario. For example, in FIG. 9, an image or video 921 (hereinafter “image”) may show a representation of both an interior and an exterior of one or more vehicles during an accident. The logic 113 may augment the raw data 221 with the image 921 to generate a scenario 931, which depicts information of participants and their characteristics or behaviors. ...", Fig. 1 & [0043] "... [0043] FIG. 1 illustrates an example implementation or scenario (hereinafter “implementation”), of a computing system 102 that automatically generates seed scenarios from a corpus of data which may encompass accident reconstruction reports, accident reports, incident reports, potential or suspicious activity reports, traffic reports, and/or other reports of actual events, accidents, near-miss events, or disengagements (hereinafter “events”)...." - the scenario also vehicle incidents like accident reconstruction. The scenarios are generated based on LLM report as shown in [0038] "... [0038] In order to improve accuracy and effectiveness of testing scenarios for autonomous and semi-autonomous vehicles, a computing system, which may include or be associated with machine learning components such as Large Language Model (LLM), may generate testing scenarios based on reports, logs, and/or other data from external databases. ..." [0006]-[0007]).
It would have been obvious to one (e.g. a designer) of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Nowak (2008) to Collins (2017) to generate vehicle simulation of the vehicle incidence to complement the teachings of Collins to address additional features taught in Collins like gathering speed data (Nowak : [0034]-[0036]) to determine bodily injuries (Collins: See Fig.54A elements 5422-5428). Further motivation to combine would have been that both Collins and Novak are analogous art in to the instant claim in the field of automotive damage assessment which enables a remote user to visually illustrate damage to an item through a rich-media application (Nowak: Abstract; Collins: Abstract).
It would have been obvious to one (e.g. a designer) of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Fields to Nowak (2008) to generate vehicle simulation animation in a more relalisti c manner from user input data (Nowak : [0034]-[0036] Fields [0055]-[0057]; [0072]-[0074]). Further motivation to combine would have been that both Fields, Novak & Collins are analogous art in to the instant claim in the field of automotive damage assessment collection which enables a remote user to visually illustrate damage to an item through a rich-media application (Fields: Fig.4-5; Nowak: Abstract; Collins: Abstract).
It would have been obvious to one (e.g. a designer) of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Madisetti (2023 filing) to Fields to generate improved prompts (Madisetti : [0110]-[0112]) to enhance retrieval speed and accuracy (Madisetti: [0156] [0160] [0202]) . Additional motivation to combine would be Fields and Madisetti are analogous art to the instant claim limitation pertaining to optimizing the chatbot with LLM (Madisetti: Fields: [0056] to keep the prompt relevant Fields [0056] "... [0056] Multi-turn (i.e., back-and-forth) conversations may require LLMs to maintain context and coherence across multiple user utterances, which may require the ML chatbot to keep track of an entire conversation history as well as the current state of the conversation...."; Madisetti: [0110]-[0112]).
It would have been obvious to one (e.g. a designer) of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Reschka (2023 filing) to Collins (2017) & Fields to generate vehicle simulation/ implementation/scenario such as accident reconstruction (Reschka : [0043][0038]) to also use the LLM based processing to gather raw data with motivation "... to improve accuracy and effectiveness of testing scenarios for autonomous and semi-autonomous vehicles, ..." (Reschka : [0038]). Additional motivation to combine would be Collins, Fields, and Reschka are analogous art to the instant claim in the field of accident reconstruction and use of LLM (Reschka: abstract, [0038][0043]; Fields: [0041][0055]-[0057], Abstract; Collins: Col.14 Lines 48-53 – use of machine learning).
Regarding Claims 2, 11 and 20
Collins teaches The computing system of claim 1, wherein the information corpus includes damage inputs from the user on a damage input interface comprising a three-dimension representation of the vehicle of the user, the damage inputs identifying damage to the vehicle (Collins: Fig.4, 16-17, 33-35 showing damage input in captured images, and 3D based processing in Col.56 Lines 52-Col.58 Line 22). Motivation to combine is incorporated from the parent claim.
Regarding Claims 3 & 12
Collins teaches The computing system of claim 2, wherein the information corpus further includes damage inputs from one or more additional users on the damage input interface that identifies damage to the vehicle (Collins: Col.10 Lines 41-49 – data captured by user/customer; Col.28 Lines 6-10 damage data captured by agent ). Motivation to combine is incorporated from the parent claim.
Regarding Claims 4 & 13
Nowak teaches The computing system of claim 1, wherein the information corpus includes collision inputs from the user on a collision input interface that enables the user to provide one or more vehicle trajectories and an estimated travel speed of each vehicle corresponding to the one or more vehicle trajectories (Nowak: [0031]-[0033] Fig.2A-2B) . Motivation to combine is incorporated from the parent claim. Motivation to combine is incorporated from the parent claim.
Regarding Claims 5 & 14
Nowak teaches The computing system of claim 4, wherein the information corpus further includes collision inputs from one or more additional users on the collision input interface that indicates respective vehicle trajectories and estimate travels speed of one or more vehicle corresponding to the respective vehicle trajectories (Nowak: [0031]-[0033] Fig.2A-2B from user; additional user inputs from additional vehicles as in [0036]"... Some incident animator tools accept information from measuring instruments input devices, or vehicle controllers or vehicle computers internal or external to one or more vehicles automatically...." ). Motivation to combine is incorporated from the parent claim.
Regarding Claims 6 & 15
Collins teaches The computing system of claim 1, wherein the information corpus includes images of damage to the vehicle of the user captured via a guided content capture process (Collins: at least in Fig.4-7 shows the guided damage capture process and Fig.9 shows the flow; more details may be available in additional figures 15-20 and associated disclosure) . Motivation to combine is incorporated from the parent claim.
Regarding Claims 9 & 18
Fields teaches wherein the executed instructions further cause the computing system to: generate a collision reconstruction interface presenting at least the LLM summary and vehicle incident simulation (Fields: [0072]-[0074]; [0073] "... Other types of generative AI/ML may use the GAN, the transformer model, and/or other types of models and/or algorithms to generate: (i) realistic images from sketches, which may include the sketch and object category as input to output a synthesized image;... With the appropriate algorithms and/or training, generative AI/ML may produce various types of multimedia output and/or content which may be incorporated into a customized presentation, e.g., via an AI and/or ML chatbot (or voice bot). "); [0074] "... The trained ML chatbot may generate output such as images, video, slides (e.g., a PowerPoint slide), virtual reality, augmented reality, mixed reality, multimedia, blockchain entries, metaverse content, or any other suitable components which may be used in the customized presentation....").
Motivation to combine is incorporated from the parent claim.
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Claim(s) 7 & 16 are newly rejected under 35 U.S.C. 103 as being unpatentable over US 9824453 B1 by Collins; Stephen M. et al., in view of US 20080052134 A1 by Nowak; Vikki et al., in view Fields, in view of Madisetti, in view of RESCHKA, further in view of US 20230116639 A1 by Patt; Theo et al.
Regarding Claims 7 & 16
Teachings of Collins, Nowak, Fields, Madisetti and RESCHKA are shown in the parent claim 1. Nowak teaches collision path animation creation, but does teach wherein the vehicle incident simulation is overlaid on satellite image data of a location of the vehicle incident.
Patt teaches The computing system of claim 1, wherein the vehicle incident simulation is overlaid on satellite image data of a location of the vehicle incident (Patt: Figs.9R through 9X "... input can be in the form of a pin drop, with respect to highly specific geographic imagery such as provided through a satellite view of the incident location...."; [0133], [0157]) .
Motivation to combine Collins, Nowak, Fields, Madisetti and RESCHKA is incorporated from parent respective claim.
It would have been obvious to one (e.g. a designer) of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Patt to Nowak (&Collins) to more accurately pinpoint the accident location based on satellite view (Patt:[0057], [0068] accuracy in data gathering and simulation of incidence; Figs.9R through 9X & [0133], [0157]). Further motivation to combine would have been that Patt, Nowak & Collins are analogous art to the instant claim in the field of accurate vehicle incidence data gathering (Patt: Figs.9R through 9X [0133], [0157]; Nowak: Fig.2A-2B [0030]-[0036] & Collins: Abstract).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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Communication
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AKASH SAXENA
Primary Examiner
Art Unit 2188
/AKASH SAXENA/Primary Examiner, Art Unit 2188 Saturday, August 8, 2026
Prosecution Insights