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 .
Disposition of the Claims
In response to applicant’s amendment received on 09/02/2026, all requested changes to the claims and specification have been entered.
Claim(s) 1-16 were previously pending.
No Claim(s) have been added.
Claim(s) 2 & 9 have been cancelled.
Claim(s) 1, 3-8, 10-16 are currently pending.
Response to Amendment
Specification Objections
Applicant has amended ¶0001 of the specification to correct the cross-reference to the priority application to now recite the correct Application No. Applicant amended ¶0160 of the specification to correct the figure referenced when discussing the heat map and accumulated time tab buttons.
The objections to the specification have been withdrawn.
Claim Objections
Applicant has amended claim 10 to clarify what the pre-trained AI model is trained on and to further specify that the selectable information is matched with either the region-of-interest icon or the one or more regions of interest to determine matched selectable information.
The objection to claim 10 has been withdrawn.
Claim Interpretation
Applicant acknowledges the interpretation under 35 U.S.C. § 112(f) of the “communication module” of claim 16.
Claim Rejections under 35 U.S.C. § 112(b)
Applicant has amended claims 1, 15 & 16 to now clarify two distinct types of regions of interest, with one or more regions of interest being set to first “to obtain data related to movement of an object on the basis of an input of a user” and later, a selection UI allows a user to then designate a distinct “target region of interest, from among the one or more regions of interest”.
The rejection of claims 1, 15 & 16 under 35 U.S.C. § 112(b) have been withdrawn.
Applicant has amended claims 1, 15 & 16 to now specify a distinct “setting UI” unique from the “selection UI” to address the previously indicated lack of antecedent basis for claim 12. This is change is further reflected in the amendments to claims 4 & 7 which now specifically refer to “the setting UI”.
The rejection of claim 12 under 35 U.S.C. § 112(b) has been withdrawn.
Claim Rejections under 35 U.S.C. § 103
Applicant’s arguments, see pgs. 10-12, filed 09/02/2026, with respect to the rejection(s) of claim(s) 1-16 under 35 U.S.C. § 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Ozeki; Shinichi (US 2023/0409004 A1).
Applicant’s amendment to incorporate previously indicated allowable subject matter changes the scope of the independent claim. While conducting a new and updated search, the examiner found prior art that renders the amended claim language obvious, which are further detailed in claim rejections under 35 U.S.C. § 103 below.
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, 3-8 & 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Bosch IVA 4.0 Intelligent Video Analysis Operating Manual (publ. June 2009), hereinafter referred as “IVA”, in view of Li et al (CN 112818935 A), hereinafter referred to as “Li”, further in view of Ozeki; Shinichi (US 2023/0409004 A1), hereinafter referred to as “Ozeki”.
Considering claim 1, IVA disclose a surveillance video annotation and analysis wizard analyzing object movement. More specifically, IVA teach A method of setting a region of interest in an image for collecting road traffic indexes using an electronic device (the intelligent video analysis (IVA) program is an algorithm that can be used to detect properties and behaviors of moving object in a [Sec 1.4 – Intelligent Video Analysis], objects are typically people or vehicles moving in a field [Sec 4.1 – The Basics – Objects]), the method comprising:
setting one or more regions of interest to obtain data related to movement of an object on the basis of an input of a user (a user is able to create polygonal “fields” that detect objects moving within them [Sec 4.1 – The Basics – Field]);
outputting an indicator for obtaining additional data on the basis of the input of the user (a variety of indicators can be displayed based on a user selected “task”, such as “Object in Field” which prompts a user to select a field and then displays a colored outline indicator indicating detected objects entering the field [Sec 4.5.2 – Object in Field; Fig. on pg. 26]);
outputting a selection user interface (UI) for selecting a target region of interest, from among the one or more regions of interest, related to the indicator, (when a user selects the task “Object in Field”, a user can either select an existing field or creating a new field, and when an object passes through, a colored outline indicator will be overlayed onto the object [Sec 4.5.2 – Object in Field; Fig. on pg. 26 & Sec 4.2 – Object Outlines and Other Image Information]); and
linking the target region of interest selected through the selection UI to the indicator (after selecting the task “Object in Field”, a user is directed to select a field to perform the task, thereby linking any colored outline indicator indicating a detected object to that particular field [Sec 4.5.2 – Object in Field; Fig. on pg. 26 & Sec 4.2 – Object Outlines and Other Image Information]),
wherein the one or more regions of interest includes a vehicle region-of-interest for measuring movement of a vehicle (IVA: objects can be filtered according to certain properties to distinguish vehicles and pedestrians [Sec 4.1 – The Basics – Objects], when selecting the task “Object in Field”, a user can configure detection conditions for objects entering a field with the properties of a vehicle to obtain movement information such as speed [Sec 4.5.2 – Object in Field]) and a pedestrian region-of-interest for measuring movement of a pedestrian (IVA: when selecting the task “Object in Field”, a user can configure detection conditions for objects entering a field with the properties of a pedestrian to obtain movement information such as speed [Sec 4.5.2 – Object in Field], particularly when “Head Detection” is configured to identify heads of pedestrians passing through a field [4.7.2 – Global Settings & Sec 4.2 – Object Outlines and Other Image Information])
wherein a setting UI is displayed together with the surveillance images (IVA: a popup menu is displayed alongside the surveillance video [Sec 4.3.1 – Popup Menu in the Camera Image; Fig. on pg. 19]), and the setting UI includes a region-of-interest icon for setting the region of interest (IVA: a “Create Field” icon is provided for designated a field for analysis [Sec 4.3.1 – Popup Menu in the Camera Image; Fig. on pg. 19]),
wherein the method further comprises displaying an information selection window after the region-of interest icon is selected (IVA: the “Create Field” icon allows a user to designate a field for analysis [Sec 4.3.1 – Popup Menu in the Camera Image; Fig. on pg. 19], and for each field, a set of selectable statistics information (in the form of histograms) is presented under the displayed “Statistics” tab for Object Area, Speed, and Direction [Sec 4.3 – IVA 4.0 User Interface; Fig. on pg. 17; Sec 4.6 – Statistics]),
IVA fails to disclose the surveillance video used in their system is obtained in real-time or an information selection window for selecting information according to the type of the region of interest. Li, however, is analogous art pertinent to the field of endeavor of the present application and disclose a multi-lane congestion detection method for monitoring real-time traffic data. More specifically, Li teach displaying surveillance images collected in real time; (Li: obtaining a real-time video stream of road traffic in step 1 of Fig. 1 [¶33-34; exemplified in Fig. 4]).
Furthermore, Li describe that their invention enables real-time monitoring to obtain live data of traffic flow which facilitates accurate congestion detection and prediction of congestion duration [¶23]. Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the present application to utilize the teachings real-time monitoring of Li with the video analytics system outlined in IVA to arrive at the invention of the present application. The motivation for doing so would be to accurately obtain congestion information of a road segment in real-time.
Neither IVA nor Li, however, disclose an information selection window for selecting information according to the type of the region of interest. Ozeki, per contra, is analogous art pertinent to the field of endeavor and describes a controller that displays an information selection screen with selectable information depending on a selected region. Ozeki teach and selectable information of the information selection window is determined according to types of the one or more regions of interest (Ozeki: a display-info-type selection screen 300 presents different selectable information in the second displaying 320 based on which of the first displaying regions 310 (i.e., a region of interest) is selected – the examiner notes that the positioning of a region falls into a category of a “type” of region [¶0027-30, 34; Figs. 3 & 4]).
Ozeki explains that, by displaying different information types, dependent on a split region selection by the operator, their controller facilitates overall selection information visibility to a user, reducing operation burden and time [¶0009]. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to utilize the region-specific information selection provided by Ozeki with the base traffic analysis GUI taught by IVA in view of Li to improve overall operability when toggling between regions and selecting information for a particular type of region.
Turning to claim 3, IVA in view of Li, further in view of Ozeki teach The method of claim 1 (as described above), more specifically, IVA teach wherein the indicator includes a vehicle indicator that is linkable to the vehicle region-of-interest (IVA: a field for detecting vehicles can be defined over a street, which can be restricted to only detect vehicles based on defined conditions (object size, aspect ratio, speed, direction), which, when met, display uniquely colored outline around cars associated to the field [Sec 4.5.2 – Object in Field & Sec 4.2 – Object Outlines and Other Image Information]) and a pedestrian indicator that is linkable to the pedestrian region-of-interest (IVA: a field for detecting pedestrians can be defined over a sidewalk, which can be restricted to only detect pedestrians via “Head Detection”, which when met, display blue head indicators linked to the field [Sec 4.5.2 – Object in Field & Sec 4.2 – Object Outlines and Other Image Information]).
Regarding claim 4, IVA in view of Li, further in view of Ozeki teach The method of claim 3, (as described above), more specifically, IVA teach wherein the setting UI includes a a vehicle indicator icon for setting the vehicle indicator (IVA: the “Object in Field” icon can be clicked to allow a user to either select or create a field configured for vehicles via user or pre-defined conditions to show a colored outline indicator on identified vehicles in the field [Sec 4.5.2 – Object in Field & Sec 4.2 – Object Outlines and Other Image Information]), and a pedestrian indicator icon for setting the pedestrian indicator (IVA: the “Object in Field” icon can be clicked to allow a user to either select or create a field configured for pedestrians via user or pre-defined conditions to show a colored outline indicator on identified pedestrians in the field [Sec 4.5.2 – Object in Field & Sec 4.2 – Object Outlines and Other Image Information], furthermore, with “Head Detection” configured, heads of pedestrians passing through a field are indicated via a blue head mark [4.7.2 – Global Settings & Sec 4.2 – Object Outlines and Other Image Information]),
when the user selects the vehicle indicator icon, the selection UI displays a preset vehicle region-of-interest (IVA: a user is must define an associated field when “Object in Field” is selected [Sec 4.5.2 – Object in Field], with a predefined field already placed on the image view which a user is able to edit and adjust [Sec 4.3.1 – Popup Menu in the Camera Image]), and
when the user selects the pedestrian indicator icon, the selection UI displays a preset pedestrian region-of-interest (IVA: a user can define an associated field when “Object in Field” is selected [Sec 4.5.2 – Object in Field], again, “Head Detection” can be configured to strictly identify heads of pedestrians passing through a field [4.7.2 – Global Settings & Sec 4.2 – Object Outlines and Other Image Information], with a predefined field already placed on the image view which a user is able to edit and adjust [Sec 4.3.1 – Popup Menu in the Camera Image]).
When considering claim 5, IVA in view of Li, further in view of Ozeki teach The method of claim 4 (as described above), IVA, particularly, teach wherein the vehicle indicator includes a loop detector for detecting an occupancy rate within the vehicle region-of-interest (IVA: a “Crossing line” task can be designated to count a number of vehicles passing through a designated field [Sec 4.5.3 – Crossing Line]) and a direction vector for measuring an exit direction of the vehicle (IVA: a direction filter can be used to measure the direction of vehicles in a designated field [Sec 4.5.2 – Object in Field], furthermore a crossing line can measure vehicles passing forward or backwards through the line [Sec 4.5.3 – Crossing Line], while object motion flow arrows can illustrate the speed and direction of objects in a field [Sec 5.1 – IVA 4.0 Flow Basics and Image Information]) and
when the direction vector is set as a first direction and a second direction, a number of vehicles exiting from the vehicle region-of-interest in the first direction and the second direction is measured (IVA: the crossing line can count the number of times a vehicle passing through a forward or backward direction [Sec 4.5.3 – Crossing Line]).
As for claim 6, IVA in view of Li, further in view of Ozeki teach The method of claim 5 (as described above), IVA further teaches wherein the direction vector is displayed as an arrow indicating the exit direction (IVA: object motion flow arrows can illustrate the direction of vehicles as they enter and exit a field [Sec 5.1 – IVA 4.0 Flow Basics and Image Information]), and
when the direction vector is linked to the vehicle region-of-interest, the link between the direction vector and the vehicle region-of-interest is displayed through a linker (IVA: arrows indicating flow direction and speed are displayed in an analysis field, with the color of the arrow linking objects that trigger detection to the designated field conditions [Sec 5.1 – IVA 4.0 Flow Basics and Image Information]).
Regarding claim 7, IVA in view of Li, further in view of Ozeki teach The method of claim 5 (as described previously), IVA further teach wherein the pedestrian indicator includes a pedestrian measurer for counting an amount of traffic for pedestrians moving in the pedestrian region-of-interest (IVA: a crossing line function configured to only detect persons can count the number of pedestrians passing through a field of interest, which can be configured to only trigger by pedestrians [Sec 4.5.3 – Crossing line]),
the setting UI further includes a vector icon (IVA: Crossing line icon [Sec 4.5.3 – Crossing line] or object in field icon [Sec 4.5.2 – Object in Field]),
when a setting input of a straight line is received in the pedestrian region-of-interest after the vector icon is selected, the straight line is set to the pedestrian measurer (IVA: a crossing line function configured to only detect persons can count the number of pedestrians passing through a field of interest, which can be configured to only trigger by pedestrians via “Head Detection” [Sec 4.5.3 – Crossing line & Sec 4.2 – Object Outlines and Other Image Information]), and
when a setting input of a straight line is received in a region adjacent to the vehicle region-of-interest after the vector icon is selected, the straight line is set to the direction vector (IVA: a direction arrow can be set for fields configured to trigger by vehicles [Sec 4.5.2 – Object in Field]).
As for claim 8, IVA in view of Li, further in view of Ozeki teach The method of claim 1 (as described previously), with IVA further teaching wherein the one or more regions of interest further include a speed measurement region for measuring a moving speed of a vehicle in a specific region in the surveillance image (IVA: speed of an vehicle is measured via the “flow in field” task [Sec 5.4.3 – Flow in Field]), and when one of the one or more regions of interest and the indicator overlap, the indicator is displayed preferentially (IVA: flow indicators (yellow circles) are shown preferentially with full opacity while the field is displayed translucently [Sec 5.4.3 – Flow in Field; Fig. on pg. 55]).
Turning to claim 11, IVA in view of Li teach The method of claim 1 (as described previously), IVA fail to explicitly recite a heatmap overlayed surveillance footage. Li, however, teach further comprising displaying a heat map on the surveillance image based on the input of the user (Li: step S613 describe overlaying a vehicle density heat map over the road traffic interest area [¶21-22]),
wherein the heat map is generated by analyzing the surveillance images for an accumulated time (Li: the accumulated time of the surveillance footage informs the current vehicle density heat map and future predictions of how long congestion will last [¶21-22]).
Li further describe that their invention enables real-time monitoring to obtain live data of traffic flow which facilitates accurate congestion detection and prediction of congestion duration [¶23]. Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the present application to utilize the teachings real-time monitoring of Li with the video analytics system outlined in IVA to arrive at the invention of the present application. The motivation for doing so would be to accurately obtain congestion information of a road segment in real-time.
As for claim 12, IVA in view of Li, further in view of Ozeki teach The method of claim 11 (as described above), with IVA further teaching wherein the setting UI further includes an accumulated time input region for inputting the accumulated time (Li: at step S613, a time period can be set [¶21-22]).
Li further describe that their invention enables real-time monitoring to obtain live data of traffic flow which facilitates accurate congestion detection and prediction of congestion duration [¶23]. Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the present application to utilize the teachings real-time monitoring of Li with the video analytics system outlined in IVA to arrive at the invention of the present application. The motivation for doing so would be to accurately obtain congestion information of a road segment in real-time.
Claim(s) 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Bosch IVA 4.0 Intelligent Video Analysis Operating Manual (publ. June 2009), hereinafter referred as “IVA” in view of Li et al (CN 112818935 A), hereinafter referred to as “Li”, further in view of Ozeki; Shinichi (US 2023/0409004 A1), hereinafter referred to as “Ozeki”, further in view of Hranac et al (US 2014/0136089 Al), hereinafter referred to as “Hranac”.
Regarding claim 13, IVA in view of Li, further in view of Ozeki teach The method of claim 11 (as described previously), and while IVA teach further comprising, when a selection one region of interest among the one or more regions of interest is received, (IVA: a field is either selected from an existing one or created [Sec 4.1 – The Basics – Field]).
IVA, however, fails to teach the limitation of a time series chart. Hranac, on the other hand, is analogous art pertinent to the field of endeavor and disclose traffic visualization dashboard for obtaining roadway incident analytics. Hranac specifically teach displaying a time series chart related to the object measured in the selected one region of interest (Hranac: a timeline section 240 displays time-series chart 242 which can display either the current day’s congestion data, a past years congestion distribution by the hour, and an incidence area showing the day’s hourly incidents and the difference between past years [¶0047; Fig. 2]).
Hranac further recite that their dashboard enable a user to select a variety of information categories (anomalies, speeds, incidents, work zones) to be displayed on their interface [¶0038], and enable real-time congestion analysis for a roadway [¶0011]. Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the present application to implement the dashboard of Hranac to provide a congestion analytics time-series feature to the video analysis wizard described by IVA in view of Li, further in view of Ozeki to arrive at the invention of the instant application. The motivation for doing so would be to obtain more information about the traffic conditions of a road segment in real-time.
As for claim 14, IVA in view of Li, further in view of Ozeki, further in view of Hranac teach The method of claim 13 (as described above), with Li disclosing wherein information on the object displayed through the heat map, (Li: vehicle density heat map [¶21-22]).
Li further describe that their invention enables real-time monitoring to obtain live data of traffic flow which facilitates accurate congestion detection and prediction of congestion duration [¶23]. Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the present application to utilize the teachings real-time monitoring of Li with the video analytics system outlined in IVA to arrive at the invention of the present application. The motivation for doing so would be to accurately obtain congestion information of a road segment in real-time. Li, however, fail to teach different information in the form of a time-series chart.
Hranac, on the other hand, teach is different from information on the object displayed through the time series chart (Hranac: a timeline section 240 displays time-series chart 242 with an incidence area showing the day’s hourly incidents and the difference between past years [¶0047; Fig. 2]).
Hranac further recite that their dashboard enable a user to select a variety of information categories (anomalies, speeds, incidents, work zones) to be displayed on their interface [¶0038], and enable real-time congestion analysis for a roadway [¶0011]. Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the present application to implement the dashboard of Hranac to provide a congestion analytics time-series feature to the video analysis wizard described by IVA in view of Li, further in view of Ozeki to arrive at the invention of the instant application. The motivation for doing so would be to obtain more information about the traffic conditions of a road segment in real-time.
Considering claim 15, IVA teach the computer program comprising:
setting one or more regions of interest to obtain data related to movement of an object on the basis of an input of a user (IVA: the intelligent video analysis (IVA) program is an algorithm that can be used to detect properties and behaviors of moving object [Sec 1.4 – Intelligent Video Analysis], objects are typically people or vehicles moving in field [Sec 4.1 – The Basics – Objects]);
outputting an indicator for obtaining additional data on the basis of the input of the user (IVA: a variety of indicators can be displayed based on a user selected “task”, such as “Object in Field” which prompts a user to select a field and then displays a colored outline indicator indicating detected objects entering the field [Sec 4.5.2 – Object in Field; Fig. on pg. 26 & Sec 4.2 – Object Outlines and Other Image Information]);
outputting a selection user interface (UI) for selecting a target region of interest from among the one or more regions of interest, related to the indicator (IVA: when a user selects the task “Object in Field”, a user can either select an existing field or creating a new field, and when an object passes through, a colored outline indicator will be overlayed onto the object [Sec 4.5.2 – Object in Field; Fig. on pg. 26 & Sec 4.2 – Object Outlines and Other Image Information]); and
linking the target region of interest selected through the selection UI to the indicator (IVA: after selecting the task “Object in Field”, a user is directed to select a field to perform the task, thereby linking any colored outline indicator indicating a detected object to that particular field [Sec 4.5.2 – Object in Field; Fig. on pg. 26] & Sec 4.2 – Object Outlines and Other Image Information),
wherein the one or more regions of interest includes a vehicle region-of-interest for measuring movement of a vehicle (IVA: objects can be filtered according to certain properties to distinguish vehicles and pedestrians [Sec 4.1 – The Basics – Objects], when selecting the task “Object in Field”, a user can configure detection conditions for objects entering a field with the properties of a vehicle to obtain movement information such as speed [Sec 4.5.2 – Object in Field]) and a pedestrian region-of-interest for measuring movement of a pedestrian (IVA: when selecting the task “Object in Field”, a user can configure detection conditions for objects entering a field with the properties of a pedestrian to obtain movement information such as speed [Sec 4.5.2 – Object in Field], particularly when “Head Detection” is configured to identify heads of pedestrians passing through a field [4.7.2 – Global Settings & Sec 4.2 – Object Outlines and Other Image Information])
wherein a setting UI is displayed together with the surveillance images (IVA: a popup menu is displayed alongside the surveillance video [Sec 4.3.1 – Popup Menu in the Camera Image; Fig. on pg. 19]), and the setting UI includes a region-of-interest icon for setting the region of interest (IVA: a “Create Field” icon is provided for designated a field for analysis [Sec 4.3.1 – Popup Menu in the Camera Image; Fig. on pg. 19]),
wherein the computer program further comprises displaying an information selection window after the region-of interest icon is selected (IVA: the “Create Field” icon allows a user to designate a field for analysis [Sec 4.3.1 – Popup Menu in the Camera Image; Fig. on pg. 19], and for each field, a set of selectable statistics information (in the form of histograms) is presented under the displayed “Statistics” tab for Object Area, Speed, and Direction [Sec 4.3 – IVA 4.0 User Interface; Fig. on pg. 17; Sec 4.6 – Statistics]),
IVA fails to disclose the surveillance video used in their system is obtained in real-time or an information selection window for selecting information according to the type of the region of interest. Li, however, is analogous art pertinent to the field of endeavor of the present application and disclose a multi-lane congestion detection method for monitoring real-time traffic data. More specifically, Li teach displaying surveillance images collected in real time; (Li: obtaining a real-time video stream of road traffic in step 1 of Fig. 1 [¶33-34; exemplified in Fig. 4]).
Furthermore, Li describe that their invention enables real-time monitoring to obtain live data of traffic flow which facilitates accurate congestion detection and prediction of congestion duration [¶23]. Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the present application to utilize the teachings real-time monitoring of Li with the video analytics system outlined in IVA to arrive at the invention of the present application. The motivation for doing so would be to accurately obtain congestion information of a road segment in real-time.
Neither IVA nor Li, however, disclose an information selection window for selecting information according to the type of the region of interest. Ozeki, per contra, is analogous art pertinent to the field of endeavor and describes a controller that displays an information selection screen with selectable information depending on a selected region. Ozeki teach and selectable information of the information selection window is determined according to types of the one or more regions of interest (Ozeki: a display-info-type selection screen 300 presents different selectable information in the second displaying 320 based on which of the first displaying regions 310 (i.e., a region of interest) is selected – the examiner notes that the positioning of a region falls into a category of a “type” of region [¶0027-30, 34; Figs. 3 & 4]).
Ozeki explains that, by displaying different information types, dependent on a split region selection by the operator, their controller facilitates overall selection information visibility to a user, reducing operation burden and time [¶0009]. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to utilize the region-specific information selection provided by Ozeki with the base traffic analysis GUI taught by IVA in view of Li to improve overall operability when toggling between regions and selecting information for a particular type of region.
Hranac, on the other hand, teach A non-transitory computer-readable recording medium on which a computer program executed by a computer is recorded, (Hranac: computer environment 160 includes a processors 162 and memory modules 164 [¶0029; Fig. 1]).
Hranac is combinable with IVA because it is from the related field of endeavor. It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine the memory of Hranac with the invention of IVA, Li and Ozeki. It is well known in the art to store instruction on a computer-readable medium, and allowing a computer-implemented method to be performed by a processor. One of ordinary skill in the art would know that, in combination, each element (the method of IVA in view of Li, further in view of Ozeki, and the memory of Hranac), merely performs the same function together as they do separately to yield predictable results. Therefore, it would have been obvious to combine Hranac with IVA in view of Li, further in view of Ozeki to obtain the invention as specified in claim 15.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Bosch IVA 4.0 Intelligent Video Analysis Operating Manual (publ. June 2009), hereinafter referred as “IVA” in view of Li et al (CN 112818935 A), hereinafter referred to as “Li”, further in view of Ozeki; Shinichi (US 2023/0409004 A1), further in view of O’Gorman et al (US 2015/0287214 A1), hereinafter referred to as “O’Gorman”.
Regarding claim 16, IVA teach being configured to set one or more regions of interest for collecting road traffic indexes in the image data on the basis of the image data (IVA: the intelligent video analysis (IVA) program is an algorithm that can be used to detect properties and behaviors of moving object [Sec 1.4 – Intelligent Video Analysis], objects are typically people or vehicles moving in a field [Sec 4.1 – The Basics – Objects]),
set the one or more regions to obtain data related to movement of an object on the basis of an input of a user (IVA: a user is able to create polygonal “fields” that detect objects moving within them [Sec 4.1 – The Basics – Field]), output an indicator for obtaining additional data on the basis of the input of the user (IVA: a variety of indicators can be displayed based on a user selected “task”, such as “Object in Field” which prompts a user to select a field and then displays a colored outline indicator indicating detected objects entering the field [Sec 4.5.2 – Object in Field; Fig. on pg. 26 & Sec 4.2 – Object Outlines and Other Image Information]), output a selection user interface (UI) for selecting a target region of interest, from among the one or more regions of interest, related to the indicator (when a user selects the task “Object in Field”, a user can either select an existing field or creating a new field, and when an object passes through, a colored outline indicator will be overlayed onto the object [Sec 4.5.2 – Object in Field; Fig. on pg. 26& Sec 4.2 – Object Outlines and Other Image Information]), and link the target region of interest selected through the selection UI to the indicator (IVA: after selecting the task “Object in Field”, a user is directed to select a field to perform the task, thereby linking any colored outline indicator indicating a detected object to that particular field [Sec 4.5.2 – Object in Field; Fig. on pg. 26 & Sec 4.2 – Object Outlines and Other Image Information]),
wherein the one or more regions of interest includes a vehicle region-of-interest for measuring movement of a vehicle (IVA: objects can be filtered according to certain properties to distinguish vehicles and pedestrians [Sec 4.1 – The Basics – Objects], when selecting the task “Object in Field”, a user can configure detection conditions for objects entering a field with the properties of a vehicle to obtain movement information such as speed [Sec 4.5.2 – Object in Field]) and a pedestrian region-of-interest for measuring movement of a pedestrian (IVA: when selecting the task “Object in Field”, a user can configure detection conditions for objects entering a field with the properties of a pedestrian to obtain movement information such as speed [Sec 4.5.2 – Object in Field], particularly when “Head Detection” is configured to identify heads of pedestrians passing through a field [4.7.2 – Global Settings & Sec 4.2 – Object Outlines and Other Image Information])
wherein (IVA: a popup menu is displayed alongside the surveillance video [Sec 4.3.1 – Popup Menu in the Camera Image; Fig. on pg. 19]), and the setting UI includes a region-of-interest icon for setting the region of interest (IVA: a “Create Field” icon is provided for designated a field for analysis [Sec 4.3.1 – Popup Menu in the Camera Image; Fig. on pg. 19]),
wherein (IVA: the “Create Field” icon allows a user to designate a field for analysis [Sec 4.3.1 – Popup Menu in the Camera Image; Fig. on pg. 19], and for each field, a set of selectable statistics information (in the form of histograms) is presented under the displayed “Statistics” tab for Object Area, Speed, and Direction [Sec 4.3 – IVA 4.0 User Interface; Fig. on pg. 17; Sec 4.6 – Statistics]).
IVA fails to disclose the surveillance video used in their system is obtained in real-time or an information selection window for selecting information according to the type of the region of interest, or a particular electronic device with a communication module and processor. Li, however, is analogous art pertinent to the field of endeavor of the present application and disclose a multi-lane congestion detection method for monitoring real-time traffic data. More specifically, Li teach displaying surveillance images collected in real time; (Li: obtaining a real-time video stream of road traffic in step 1 of Fig. 1 [¶33-34; exemplified in Fig. 4]).
Furthermore, Li describe that their invention enables real-time monitoring to obtain live data of traffic flow which facilitates accurate congestion detection and prediction of congestion duration [¶23]. Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the present application to utilize the teachings real-time monitoring of Li with the video analytics system outlined in IVA to arrive at the invention of the present application. The motivation for doing so would be to accurately obtain congestion information of a road segment in real-time.
Neither IVA nor Li, however, disclose an information selection window for selecting information according to the type of the region of interest. Ozeki, per contra, is analogous art pertinent to the field of endeavor and describes a controller that displays an information selection screen with selectable information depending on a selected region. Ozeki teach and selectable information of the information selection window is determined according to types of the one or more regions of interest (Ozeki: a display-info-type selection screen 300 presents different selectable information in the second displaying 320 based on which of the first displaying regions 310 (i.e., a region of interest) is selected – the examiner notes that the positioning of a region falls into a category of a “type” of region [¶0027-30, 34; Figs. 3 & 4]).
Ozeki explains that, by displaying different information types, dependent on a split region selection by the operator, their controller facilitates overall selection information visibility to a user, reducing operation burden and time [¶0009]. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to utilize the region-specific information selection provided by Ozeki with the base traffic analysis GUI taught by IVA in view of Li to improve overall operability when toggling between regions and selecting information for a particular type of region.
IVA, Li and Ozeki, however, are silent on disclosing an electronic device with a communication module and a processor. O’Gorman, on the other hand, is analogous art pertinent to the field of endeavor of the present application and disclose a monitoring apparatus for tracking object movements and generating activity maps. O’Gorman teach An electronic device (O’Gorman: a unified activity map generation circuit 104, which can include a CPU [¶0080; Fig. 1]) comprising:
a communication module (Examiner notes that this limitation is being interpreted under 35 U.S.C. § 112(f) – O’Gorman: the network can 110 may be the internet, intranet, LAN [¶0079]) configured to receive image data captured by a camera (O’Gorman: the network receives video data from cameras 10-1 … 10-N [¶0079-81; Fig. 1]); and
a processor (O’Gorman: a unified activity map generation circuit 104, which can include a CPU comprised of one or more processors [¶0080; Fig. 1]). Thus, in accordance with the KSR rationales (see MPEP § 2143), the prior art includes all the claimed elements in the present application, with the only difference being the lack of combination. Furthermore, one of ordinary skill in the art could have easily combined the elements by known methods and that each element would merely perform the same function as it does separately. Furthermore, one of ordinary skill would recognize that the combination would be predictable, as utilizing the base device and disclosed LAN of O’Gorman would allow one to execute the video analytics program disclosed by IVA in view of Li to observe and analyze a traffic scene in real-time.
Allowable Subject Matter
Claim 10 is 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.
Regarding claim 10, the primary reason for indication of allowable subject matter is that the currently cited prior art or prior art made of record fails to teach or reasonably suggest “selectable information is determine through a pre-trained AI model, and the pre0trained AI model is trained on a dataset in which the region-of-interest icon or the one or more regions of interest are matched with the selectable information, so as to determine the matched selectable information when a selection for the region-of-interest icon or the one or more regions of interest is received”. The closest cited prior art, Ozeki, disclose the selectable information determined according to a type of region of interest, however, this information is pre-determined and is not informed from the training of a machine-learning model. For at least this reason, no prior art of record teaches towards the invention as described in claim 10 as a whole.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael M. Sofroniou whose telephone number is (571)272-0287. The examiner can normally be reached M-F: 8:30 AM - 5:00 PM.
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/MICHAEL M SOFRONIOU/Examiner, Art Unit 2661
/JOHN VILLECCO/Supervisory Patent Examiner, Art Unit 2661
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