DETAILED ACTION
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/13/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretation
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.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
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.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
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 limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: ‘input unit’ and ‘computation unit’ in claims 1-5 and 8.
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 does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claim 8 is 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 8 recites the limitation "the structured query language”. There is insufficient antecedent basis for this limitation in the claim. This is being interpreted as a new element meaning any database language.
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 and 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cardona (US 20220237963 A1) in view of Yae (US 20220075816 A1).
Regarding claim 1, Cardona teaches “A traffic accident scene analysis system comprising: a camera unit for capturing and outputting scene images of an accident involving at least one accident-causing vehicle;” (Cardona, Paragraphs 40 and 11, and Figure 4 element 402, “The VFA computing device may further retrieve photographic data. As used herein, “photographic data” includes photographs and any data associated with the photographs (e.g., time stamps or geographic coordinates). For example, photographic data may include photographs taken of the scene of the collision and of damage to the vehicles. Photographs of the scene of the collision may include, for example, the final resting positions of the vehicles following the collision. Photographic data may further include video taken by cameras onboard the vehicle or cameras having a view of the scene of the collision (e.g. surveillance cameras).”; “AV 400 may include a plurality of sensors 402, a computing device 404, and a vehicle controller 408. Sensors 402 may be, for example, sensors 136 (shown in FIG. 2). Further, sensors 402 may include, but are not limited to, temperature sensors, terrain sensors, weather sensors, accelerometers, gyroscopes, radar, LIDAR, Global Positioning System (GPS), video devices, imaging devices, cameras (e.g., 2D and 3D cameras), audio recorders, and computer vision. Sensors 402 may be used to collect, for example, vehicle telematics data, as described above. Such telematics data may be transmitted by VFA computing device 102 (shown in FIG. 1).”)
While Cardona teaches an input unit (Cardona, Paragraph 69, “In some example embodiments, VFA computing device 102 may retrieve the time and location from user device 116, which may be, for example, a GPS-equipped mobile phone device. For example, an individual involved in the collision may contact his or her insurer to make an insurance claim, for example, by calling an insurance representative with user device 116 and/or by submitting the claim using a mobile application installed on user device 116. Based upon the call and/or the submission through the mobile application, VFA computing device 102 may determine that an accident has occurred and record a time the call and/or submission was made (e.g., a timestamp). VFA computing device 102 may further retrieve the geographic coordinates of user device 116. VFA computing device 102 may identify the timestamp and retrieved geographic coordinates as the time and location of the collision.”) and the receipt of record information (Cardona, Paragraph 41, “The VFA computing device may further retrieve text data. As used herein, “text data” includes text (e.g., text documents). For example, text data may include documents describing the collision (e.g., a police report). The VFA computing device may parse text data for terms and phrases indicative of the behavior of the vehicles in the collision.”), Cardona does not expressly disclose that record information is received via input from the input unit. Rather, Cardona describes a generic retrieval of the text data from an unspecified source (see above).
Yae teaches input, via an input device, of accident information (Yae, Paragraph 57, “The input device 115 may receive an input signal corresponding to a user's manipulation, operation, or voice. According to various exemplary embodiments of the present invention, the input device 115 may input accident information by a user when an accident occurs. To the present end, the input device 115 may be implemented with a scroll wheel, a button, a knob, a touch screen, a touch pad, a lever, a track ball, and the like that a user manipulates, at least one of a motion sensor for detecting motion or voice of an occupant and a voice recognition sensor, or a combination thereof.”)
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the input unit (user device) of Cardona to enable input of the record information of Cardona, in accordance with the input unit for accident information inputting taught by Yae.
The motivation for doing so would have been to improve the functionality of the user interface already taught by Cardona, and also enable the system of Cardona to use police reports/text data that otherwise have not been published or uploaded to a database from which the system typically retrieves the text data. Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Cardona with the above teaching from Yae to fully disclose, “an input unit for inputting a record information;”.
Cardona in view of Yae further disclose, “a computation unit electrically connected to the camera unit and the input unit for receiving the scene images and the record information,” (Cardona, Figure 1 element 102; see VFA computing device in the above excerpts, which is a computation unit that receives the images (i.e. via connection to the camera unit) and record information (i.e. via connection to user device as per the above combination).)
“the computation unit performing image segmentation on the scene images to generate information of the at least one accident-causing vehicle and a road information,” (Cardona, Paragraph 59, “The VFA computing device may further determine the position and orientation of the vehicle based upon photographic data. For example, photographic data may include photographs taken of the scene of the collision and of damage to the vehicles. Photographs of the scene of the collision may include, for example, the final resting positions of the vehicles following the collision. Photographic data may further include video taken by cameras onboard the vehicle or cameras having a view of the scene of the collision (e.g. surveillance cameras). The VFA computing device may generate the simulation so that the position and orientation of representations of the vehicles corresponds to the positions of the vehicles in photographs and/or of the scene at the moment depicted by the photograph and/or video. Photographs of damage to the vehicles may indicate points of contact (e.g., points of damage indicating contact between two vehicles or between vehicles and objects) during the collision. The VFA computing device may generate the simulation so that the points of contact in the simulated collision correspond to points of contact indicated by the photographs.” Note that the automated identification of discrete vehicles, position and orientation of the vehicles, point of contact, etc. requires a semantic segmentation for labelling pixels as belonging to car, point of contact, etc. Further, “road information” is mapped to either the other vehicles or points of contact between collided vehicles.)
“and the computation unit performing word segmentation on the record information to generate plural keyword information;” (Cardona, Paragraphs 31 and 41, and last sentence of Paragraph 147, “In some example embodiments, the VFA computing device may retrieve the time and location of the collision from a third party. For example, a police report may include a time and geographic coordinates of a collision. The VFA computing device may retrieve a third party document such as a police report and parse the document for data regarding the time and the location of the collision (e.g., the geographic coordinates). The VFA computing device may identify the time and the location based upon the parsed data.”; “The VFA computing device may further retrieve text data. As used herein, “text data” includes text (e.g., text documents). For example, text data may include documents describing the collision (e.g., a police report). The VFA computing device may parse text data for terms and phrases indicative of the behavior of the vehicles in the collision.”; “The machine learning programs may also include natural language processing, semantic analysis, automatic reasoning, and/or other types of machine learning or artificial intelligence.”)
“the computation unit executing program data of a generative artificial intelligence model that generates a preliminary analysis report of the accident based on the information of the at least one accident-causing vehicle, the road information, and the plural keyword information, the preliminary analysis report comprising an animation of the accident;” (Cardona, Paragraphs 54 and 5, and the last sentence of Paragraph 147, “To simulate the collision, the VFA computing device may determine the position and orientation of one or more vehicles involved in the collision for a plurality of moments in time during the collision. The VFA computing device may simulate the collision based upon, for example, speech data and/or vehicle telematics data. The VFA computing device may further use additional data to simulate the collision (e.g., vehicle specification data, photographic data, text data, and/or physics data).”; “Systems and methods are configured to gather and analyze collision data, and make determinations in a vehicle forensics process using geolocation data, speech data, analytics data, and/or other vehicle telematics data. The collision data and artificial intelligence are leveraged to model and simulate a collision for a claims process.”; “The machine learning programs may also include natural language processing, semantic analysis, automatic reasoning, and/or other types of machine learning or artificial intelligence.” Note that the photographic data amounts to ‘the information of the at least one accident-causing vehicle and the road information’ (as they are derived from the photographs as outlined above), and the text data amounts to the plural keyword information.)
“and a user interface electrically connected to the computation unit, and the user interface receiving and displaying the preliminary analysis report.” (Cardona, Paragraph 98, “Stored in memory area 210 may be, for example, computer readable instructions for providing a user interface to user 201 via media output component 215 and, optionally, receiving and processing input from input device 220. A user interface may include, among other possibilities, a web browser and client application. Web browsers may enable users, such as user 201, to display and interact with media and other information typically embedded on a web page or a website. A client application may allow user 201 to interact with a server application from VFA computing device 102 (shown in FIG. 1), for example, to view a simulation generated by VFA computing device 102.”)
Regarding claim 5, Cardona in view of Yae teaches “The traffic accident scene analysis system as claimed in claim 1,”
“further comprising an auxiliary camera unit which is electrically connected to the computation unit and capturing an occurrence process of the accident to produce images of the accident, wherein the generative artificial intelligence model of the computation unit receives the images of the accident, and the generative artificial intelligence model generates the preliminary analysis report of the accident based on the information of the at least one accident-causing vehicle, the road information, the plural keyword information, and the images of the accident.” (Cardona, Paragraphs 40, 54, and 5, “The VFA computing device may further retrieve photographic data. As used herein, “photographic data” includes photographs and any data associated with the photographs (e.g., time stamps or geographic coordinates). For example, photographic data may include photographs taken of the scene of the collision and of damage to the vehicles. Photographs of the scene of the collision may include, for example, the final resting positions of the vehicles following the collision. Photographic data may further include video taken by cameras onboard the vehicle or cameras having a view of the scene of the collision (e.g. surveillance cameras).” “To simulate the collision, the VFA computing device may determine the position and orientation of one or more vehicles involved in the collision for a plurality of moments in time during the collision. The VFA computing device may simulate the collision based upon, for example, speech data and/or vehicle telematics data. The VFA computing device may further use additional data to simulate the collision (e.g., vehicle specification data, photographic data, text data, and/or physics data).”; “Systems and methods are configured to gather and analyze collision data, and make determinations in a vehicle forensics process using geolocation data, speech data, analytics data, and/or other vehicle telematics data. The collision data and artificial intelligence are leveraged to model and simulate a collision for a claims process.” Note that the photographic data retrieved by the VFA computing device (which operates the generative artificial intelligence model) maps to the images of the accident captured by the auxiliary camera device (surveillance camera), as these images are used, in addition to the other elements, for collision simulation generation.)
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cardona in view of Yae further in view of Rousson (US 20070260135 A1).
Regarding claim 4, Cardona in view of Yae teaches “The traffic accident scene analysis system as claimed in claim 1,”
“wherein the computation unit executes an image segmentation model,” (Cardona, Paragraphs 59 and 160, “The VFA computing device may further determine the position and orientation of the vehicle based upon photographic data. For example, photographic data may include photographs taken of the scene of the collision and of damage to the vehicles. Photographs of the scene of the collision may include, for example, the final resting positions of the vehicles following the collision. Photographic data may further include video taken by cameras onboard the vehicle or cameras having a view of the scene of the collision (e.g. surveillance cameras). The VFA computing device may generate the simulation so that the position and orientation of representations of the vehicles corresponds to the positions of the vehicles in photographs and/or of the scene at the moment depicted by the photograph and/or video. Photographs of damage to the vehicles may indicate points of contact (e.g., points of damage indicating contact between two vehicles or between vehicles and objects) during the collision. The VFA computing device may generate the simulation so that the points of contact in the simulated collision correspond to points of contact indicated by the photographs.”; “A further enhancement of the VFA computing device includes a processor, wherein to determine the position and the orientation of the vehicle at each of the plurality of moments in time, the processor is configured to determine the position and orientation based upon one or more of vehicle telematics data, vehicle specification data, photographic data, or physics data.” Note that the automated identification of discrete vehicles, position and orientation of the vehicles, point of contact, etc. requires a segmentation model implemented by the VFA computing device for labelling pixels as belonging to each object. Further, note that Cardona expressly discloses the use of machine learning models by the VFA computing device (see, for example, Paragraph 149.))
While Cardona in view of Yae discloses user inputs to the input unit (user device) (Cardona, Paragraph 69, “In some example embodiments, VFA computing device 102 may retrieve the time and location from user device 116, which may be, for example, a GPS-equipped mobile phone device. For example, an individual involved in the collision may contact his or her insurer to make an insurance claim, for example, by calling an insurance representative with user device 116 and/or by submitting the claim using a mobile application installed on user device 116. Based upon the call and/or the submission through the mobile application, VFA computing device 102 may determine that an accident has occurred and record a time the call and/or submission was made (e.g., a timestamp). VFA computing device 102 may further retrieve the geographic coordinates of user device 116. VFA computing device 102 may identify the timestamp and retrieved geographic coordinates as the time and location of the collision.”; Yae, Paragraph 57, “The input device 115 may receive an input signal corresponding to a user's manipulation, operation, or voice. According to various exemplary embodiments of the present invention, the input device 115 may input accident information by a user when an accident occurs. To the present end, the input device 115 may be implemented with a scroll wheel, a button, a knob, a touch screen, a touch pad, a lever, a track ball, and the like that a user manipulates, at least one of a motion sensor for detecting motion or voice of an occupant and a voice recognition sensor, or a combination thereof.” See combination statement recited with rationale and motivation in the rejection of claim 1.), Cardona in view of Yae do not expressly disclose “the image segmentation model receives at least one recognition instruction by the input unit,”.
Rousson discloses the initiation of a segmentation process based on a recognition instruction by an input unit (Rousson, Paragraph 51, “FIG. 14 illustrates a computer system that can be used in accordance with one aspect of the present invention. The system is provided with data 1201 representing the to be displayed image. It may also include the prior learning data. An instruction set or program 1202 comprising the methods of the present invention is provided and combined with the data in a processor 1203, which can process the instructions of 1202 applied to the data 1201 and show the resulting image on a display 1204. The processor can be dedicated hardware, a GPU, a CPU or any other computing device that can execute the instructions of 1202. An input device 1205 like a mouse, or track-ball or other input device allows a user to initiate the segmentation process. Consequently the system as shown in FIG. 14 provides an interactive system for image segmentation. Of course, any type of computer system can be used, although it is preferred to use a computer system having sufficient processing power. By way of example only, a stand alone PC, a multiprocessor PC, a main frame computer, a parallel processing computer or any other type of computer can be used. In summary, according to one aspect of the present invention a method has been presented which mimics in certain aspects the physician approach in establishing the position of the esophagus. The method of segmentation that is one aspect of the present invention comprises three phases: (1) shape and appearance modeling of the esophagus from a training set in a reference basis defined by the left atrium and the aorta; (2) automatic extraction of the esophagus centerline by integrating each prior knowledge; and (3) extension of the center line to inner and outer boundaries.”)
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the input unit of Cardona in view of Yae to enable providing a recognition instruction to the image segmentation model, as taught by Rousson.
The motivation for doing so would have been to enhance the functionality of the user interface already taught by Cardona in view of Yae, and improve the overall user experience. Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Cardona in view of Yae with the above teaching from Rousson to fully disclose, “the image segmentation model receives at least one recognition instruction by the input unit,”.
Cardona in view of Yae with the above teaching from Rousson further disclose, “the image segmentation model performs semantic segmentation on the scene images, and generates the information of the at least one accident-causing vehicle based on the at least one recognition instruction.” (Cardona, Paragraph 59, “The VFA computing device may further determine the position and orientation of the vehicle based upon photographic data. For example, photographic data may include photographs taken of the scene of the collision and of damage to the vehicles. Photographs of the scene of the collision may include, for example, the final resting positions of the vehicles following the collision. Photographic data may further include video taken by cameras onboard the vehicle or cameras having a view of the scene of the collision (e.g. surveillance cameras). The VFA computing device may generate the simulation so that the position and orientation of representations of the vehicles corresponds to the positions of the vehicles in photographs and/or of the scene at the moment depicted by the photograph and/or video. Photographs of damage to the vehicles may indicate points of contact (e.g., points of damage indicating contact between two vehicles or between vehicles and objects) during the collision. The VFA computing device may generate the simulation so that the points of contact in the simulated collision correspond to points of contact indicated by the photographs.” Note that in view of the combination above, this segmentation is initiated by the recognition instruction, therefore the segmentation and associated information generation is “based on the at least one recognition instruction”.)
Claim(s) 6-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cardona in view of Yae further in view of Han (US20220163346A1).
Regarding claim 6, Cardona in view of Yae teaches “The traffic accident scene analysis system as claimed in claim 1,”
“wherein the generative artificial intelligence model generates at least one reference point coordinate for each accident-causing vehicle based on the plural keyword information,” (Cardona teaches that the collision simulation contains coordinates (embodied in position information) for each vehicle (including the accident-causing vehicle), and that the collision simulation itself is, in part, based on the plural keyword information. See Paragraph 54, “To simulate the collision, the VFA computing device may determine the position and orientation of one or more vehicles involved in the collision for a plurality of moments in time during the collision. The VFA computing device may simulate the collision based upon, for example, speech data and/or vehicle telematics data. The VFA computing device may further use additional data to simulate the collision (e.g., vehicle specification data, photographic data, text data, and/or physics data).” Cardona additionally describes the extraction of coordinate data directly from the plural keyword information in Paragraph 31: “In some example embodiments, the VFA computing device may retrieve the time and location of the collision from a third party. For example, a police report may include a time and geographic coordinates of a collision. The VFA computing device may retrieve a third party document such as a police report and parse the document for data regarding the time and the location of the collision (e.g., the geographic coordinates). The VFA computing device may identify the time and the location based upon the parsed data.”)
While Cardona in view of Yae discloses the identification of road marking locations/coordinates based on map data (see Paragraph 34), Cardona in view of Yae does not expressly disclose that “the road information” (data specifically extracted from the images taken/photographic data according to Claim 1) comprises at least one road marking coordinate.
Han discloses the identification of road marking coordinates from acquired image data (Han, Paragraphs 96 and 94, and Figures 8B-8D, “FIG. 8A to FIG. 8D are exemplary diagrams illustrating a result of performing semantic segmentation according to an embodiment of the present disclosure.” Figures 8B-8D show road an image segmented wherein the segmentations include road marking coordinates.; “By classifying each pixel in the image into units of information that it means, for example, moving object information and road information, a semantic segmented image is obtained (S220). Here, the moving object information indicates a moving object such as a vehicle or a pedestrian, and the road information indicates a road, a lane marking, a road surface indication, and the like. Moving object information is used for obstacle processing during autonomous driving, and other road information is used as texture information.”)
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to perform the image segmentation for road/lane marking coordinates of Han on the photographic data of Cardona in view of Yae such that the road information includes road/lane marking coordinates at the collision scene.
The motivation for doing so would have been to generate the collision simulations with more up-to-date lane marking locations. Currently, Cardona in view of Yae extracts lane marking positions for the simulation from map data. While the map data may be continuously updated, it may not capture changes to lane markings within an hour, day, week, etc. of the collision, resulting in a simulation with outdated lane markings. Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Cardona in view of Yae with the above teaching from Han to fully disclose, “and the road information comprises at least one road marking coordinate;”.
Cardona in view of Yae further in view of Han further disclose, “the preliminary analysis report comprises a scene schematic, the scene schematic comprising plural reference distances, the plural reference distances comprising distances between the at least one reference point coordinate and the at least one road marking coordinate.” (Cardona describes collision scene schematic comprising positions (i.e. coordinates) of each scene element (including cars and road markings). Distances between each point of the scene are inherent: any two points on an image have a relative distance to each other, amounting to ‘plural reference distances’ between all coordinates (including ‘the at least one reference point coordinate and the at least one road marking coordinate’). See Paragraphs 71, 81, and 34: “VFA computing device 102 may be further configured to retrieve data to generate the simulation. VFA computing device 102 may retrieve, for example, map data, contextual data, vehicle specification data, photographic data, text data, and physics data.”; “VFA computing device 102 may generate a simulation including a scene model depicting the scene of the collision. The scene model may be based upon, for example, map data and contextual data and enables individuals not present at the scene of the collision (e.g., insurance claims employees) to visualize the scene.”; “As used herein, “map data” may refer to any data identifying geographic features associated with the scene of the collision. Examples of map data include, but are not limited to, the locations of vehicle thoroughfares (e.g., streets, roads, or highways), sidewalks, railroads, water features, structures (e.g., buildings, fences, guard rails, or walls), terrain and/or topographical features (e.g., hills), and/or objects (e.g., foliage, road signs, or utility poles). Map data may also include information regarding specific vehicle thoroughfares, for example, a number of lanes, a direction of travel of each lane, road-specific traffic regulations (e.g., speed limits, school zones, or weight limits), presence of regulatory signs and signals (e.g., lane markings, railroad crossing gates, stop signs, or traffic signals), road dimensions (e.g., width of the lanes), road features (e.g., the presence and type of medians or barriers, the material and condition of road surfaces, or the presence of bridges and tunnels), and/or road topography (e.g., inclines in the road).”)
Regarding claim 7, Cardona in view of Yae teaches “The traffic accident scene analysis system as claimed in claim 1,”
While Cardona in view of Yae discloses the identification of road range based on map data (see Paragraph 34), Cardona in view of Yae does not expressly disclose that “the road information” (data specifically extracted from the images taken/photographic data according to Claim 1) comprises a range of road.
Han discloses the identification of a range of a road from acquired image data (Han, Paragraphs 96 and 94, and Figures 8B-8D, “FIG. 8A to FIG. 8D are exemplary diagrams illustrating a result of performing semantic segmentation according to an embodiment of the present disclosure.” Figures 8B-8D show road an image segmented wherein the segmentations include road ranges.; “By classifying each pixel in the image into units of information that it means, for example, moving object information and road information, a semantic segmented image is obtained (S220). Here, the moving object information indicates a moving object such as a vehicle or a pedestrian, and the road information indicates a road, a lane marking, a road surface indication, and the like. Moving object information is used for obstacle processing during autonomous driving, and other road information is used as texture information.”)
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to perform the image segmentation for road ranges of Han on the photographic data of Cardona in view of Yae such that the road information includes the road ranges at the collision scene.
The motivation for doing so would have been to generate the collision simulations with more up-to-date road range regions. Currently, Cardona in view of Yae extracts road ranges for the simulation from map data. While the map data may be continuously updated, it may not capture changes to road ranges made within an hour, day, week, etc. of the collision, resulting in a simulation with outdated road ranges. Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Cardona in view of Yae with the above teaching from Han to fully disclose, “wherein the road information comprises a range of road,”
Cardona in view of Yae further in view of Han further disclose, “the generative artificial intelligence model generates a simulated track of the at least one accident-causing vehicle based on the plural keyword information, and the generative artificial intelligence model determines whether the simulated track is within the range of road;” (Cardona teaches that the simulation includes a position and orientation of the vehicles for each of a plurality of moments of time, amounting to a simulated track. The simulation is generated, in part, by the plural keyword information. Further, the simulation depicts both the simulated track and the road, amounting to an inherent depiction of whether or not the simulated track is within the range of the road. See Paragraphs 103 and 34: “In exemplary embodiments, processor 305 may include and/or be communicatively coupled to one or more modules for implementing the systems and methods described herein. Processor 305 may contain a location determining module 330 configured to determine a time and a location of a traffic collision. Processor 305 may also include a speech analysis module 332 configured to parse speech data corresponding to a statement given by a witness of the traffic collision and parse the speech data for phrases describing the traffic collision. Processor 305 may also include a simulation module 334 configured to determine based upon the speech data, for each of a plurality of moments in time during the traffic collision, a position and an orientation of a vehicle involved in the traffic collision and generate a simulation including a representation of the vehicle involved in the traffic collision, the simulation based upon map data, contextual data, and the position and orientation of the vehicle involved in the traffic collision at each of the plurality of moments in time. Additionally or alternatively, simulation module 334 may be configured to determine, based upon vehicle telematics data, for each of a plurality of moments in time during the traffic collision, a position and an orientation of the vehicle.”; “As used herein, “map data” may refer to any data identifying geographic features associated with the scene of the collision. Examples of map data include, but are not limited to, the locations of vehicle thoroughfares (e.g., streets, roads, or highways), sidewalks, railroads, water features, structures (e.g., buildings, fences, guard rails, or walls), terrain and/or topographical features (e.g., hills), and/or objects (e.g., foliage, road signs, or utility poles). Map data may also include information regarding specific vehicle thoroughfares, for example, a number of lanes, a direction of travel of each lane, road-specific traffic regulations (e.g., speed limits, school zones, or weight limits), presence of regulatory signs and signals (e.g., lane markings, railroad crossing gates, stop signs, or traffic signals), road dimensions (e.g., width of the lanes), road features (e.g., the presence and type of medians or barriers, the material and condition of road surfaces, or the presence of bridges and tunnels), and/or road topography (e.g., inclines in the road).”
“when the generative artificial intelligence model determines that the simulated track is within the range of road, the generative artificial intelligence model generates the animation of the accident based on the information of the at least one accident-causing vehicle, the road information and the simulated track.” (Cardona, see Paragraphs 103 and 34 (cited above) and Paragraph 54: “To simulate the collision, the VFA computing device may determine the position and orientation of one or more vehicles involved in the collision for a plurality of moments in time during the collision. The VFA computing device may simulate the collision based upon, for example, speech data and/or vehicle telematics data. The VFA computing device may further use additional data to simulate the collision (e.g., vehicle specification data, photographic data, text data, and/or physics data).” Taken together, these paragraphs disclose that the collision simulation includes an animation (simulation) of the collision (accident) based on ‘the information of the at least one accident-causing vehicle, the road information and the simulated track.’ Note that, as the references are combined above and in view of the rejection of claim 1, the photographic data (used for simulation generation) embodies the information of the at least one accident-causing vehicle and the road information. Further, regarding: “when the generative artificial intelligence model determines that the simulated track is within the range of road”: the references teach that limitation that follows this ‘when’ condition occurs regardless of whether or not this condition is met. Still, this means that the limitation that follows is performed by the references when the ‘when’ condition is met (in addition to when it is not met).)
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cardona in view of Yae further in view of OFFICIAL NOTICE.
Regarding claim 8, Cardona in view of Yae teaches “The traffic accident scene analysis system as claimed in claim 1,”
“wherein the computation unit is electrically connected to a database constructed on the structured query language,” (Cardona, Paragraph 66, “FIG. 1 depicts an exemplary vehicle forensics system 100. In the exemplary embodiment, vehicle forensics system 100 includes a vehicle forensics analytics (VFA) computing device 102. VFA computing device may include a database server 104 and be in communication with a database 106 and/or other memory devices. VFA computing device 102 may also be in communication with, for example, a map data computing device 108, a contextual data computing device 110, a speech data computing device 112, an autonomous vehicle (AV) 114, a user device 116, and/or a telematics device 118.”)
While Cardona in view of Yae teaches the database directly connected to the VFA computing device (Figure 1), wherein the VFA computing device generates “the information of the at least one accident-causing vehicle, the road information, the plural keyword information, and the preliminary analysis report of various accidents” (see claim 1 rejection), and database storage of map data and contextual data (Paragraphs 72-72), Cardona in view of Yae does not expressly disclose that the database itself stores these VFA-generated elements (the information of the at least one accident-causing vehicle, the road information, the plural keyword information, and the preliminary analysis report of various accidents).
The Examiner takes OFFICIAL NOTICE that the above-described storage of AI/machine learning inputs and outputs is well known and routine in the field of machine learning.
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to store the information of the at least one accident-causing vehicle, the road information, the plural keyword information, and the preliminary analysis report of various accidents in the database of Cardona in view of Yae, as taught by the above teaching of OFFICIAL NOTICE.
The motivation for doing so would have been for future use in applications such as output validation by experts/users, and iterative continuous training. In fact, Cardona describes training using previously acquired data (Cardona, Paragraph 146, “A processor or a processing element may be trained using supervised or unsupervised machine learning, and the machine learning program may employ a neural network, which may be a convolutional neural network, a deep learning neural network, or a combined learning module or program that learns in two or more fields or areas of interest. Machine learning may involve identifying and recognizing patterns in existing data in order to facilitate making predictions for subsequent data. Models may be created based upon example inputs in order to make valid and reliable predictions for novel inputs.”) Therefore, the above combination effectively achieves the goals already expressly disclosed in Cardona in view of Yae. Additionally, the database is already connected directly to the VFA computing device (see Figure 1), making the above combination technically straightforward. Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Cardona in view of Yae with the above teaching from OFFICIAL NOTICE to fully disclose, “the database storing the information of the at least one accident-causing vehicle, the road information, the plural keyword information, and the preliminary analysis report of various accidents.”
Allowable Subject Matter
Claims 2 and 3 are objected to as being dependent upon a rejected base claim but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: With respect to claim 2 (and claim 3 through dependence), in addition to other limitations in the claims the Prior Art of Record fails to teach, disclose or render obvious the applicant' s invention as claimed, in particular:
Claim 2 recites: “The traffic accident scene analysis system as claimed in claim 1, wherein the computation unit executes program data of an image segmentation model and a word segmentation model, the image segmentation model is connected to the word segmentation model, the word segmentation model generates the plural keyword information based on the record information, and the image segmentation model performs instance segmentation of the scene images based on the plural keyword information to generate the information of the at least one accident-causing vehicle.”
The closest reference found to the above limitations, Cardona, discloses the extraction of various collision-related data, including imaging data and text data, for use in collision reconstruction through simulation generation. Han discloses a map generation for autonomous driving applications including segmenting road images. Yae teaches a system for sharing vehicle accident information, including transmission of accident-related images to other vehicles for cause determination. Rousson teaches a method for image segmentation of an esophagus according to a shortest path algorithm. Sambo (US 20220044024 A1) teaches vehicle accident reconstruction method that includes generation of dense semantic point clouds based on video data. Guo (US 10984293 B2) teaches artificial intelligence-based image analysis of a video stream of a vehicle, wherein frames are segmented to identify vehicle parts and damages for display to the user. Zhang (CN 104463842 A) teaches the recreation of a traffic accident in 3D based on image analysis used to track vehicle motion dynamics of the vehicle involved in the collision. However, none of these references expressly disclose the bolded limitations above. In particular, Cardona does appear to perform instance segmentation by identifying discrete vehicles during image segmentation of the photographic data (see Paragraph 59, for example). However, this instance segmentation is in no way ‘based on the plural keyword information’. The image segmentation and keyword information extraction occur alongside each other to gather data used to simulate the collision. However, they are not linked in such a way that one is ‘based on’ the other. It also does not appear to be obvious to modify the invention of Cardona in this way.
Conclusion
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/AARON JOSEPH SORRIN/
Examiner, Art Unit 2672
/SUMATI LEFKOWITZ/Supervisory Patent Examiner, Art Unit 2672
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