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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/23/2025 has been entered.
Response to Amendment
The Amendment filed 08/28/2025 has been entered. Claims 12, 19, 20 and 29 are canceled and claims 32-35 are new. Claims 1-11, 13-18 and 21-28 and 30-35 are now pending in the application.
Claim Objections
Claims 1-11, 13-18 and 21-31 are objected to because of the following informalities:
Claim 28, line 1 recites the phrase “Method of claim 35” which should be “Method of claim 34”
For the informalities above and wherever else they may occur appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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.
Claims 1, 3, 5-7, 9-11, 15-18, 21-23 and 25-28 and 30-35 are rejected under 35 U.S.C. 103 as being unpatentable over Chandra et al. (US 20020138582 A1 hereinafter Chandra) in view of Boykin et al. (US 20180050800 A1 hereinafter Boykin)
As to independent claim 1, Chandra teaches method of identifying desired information comprising:
determining a coarse filter based on a rule; [a rule has a coarse filter ¶583-584 "Each rule comprises an association with one event through a coarse-grain filter, a fine-grain filter that has one or more conditions, zero or more constants, one or more actions or handler"]
transmitting the coarse filter to a filter module configured for coarse filtering data with the coarse filter [event handler ¶545 and broker ¶573 use a coarse filter to filter based on event header Fig. 17B 1712 ¶584 "coarse-grain filters carry out filtering only on a header portion of an event message."]
determining a fine filter based on the rule; [rule includes a fine filter ¶583-585 "Rule conditions may be created as coarse-grain filters or fine-grain filters"]
and
fine filtering the course-filtered sensor data with the fine filter; [The coarse filter first selects event messages based on basic criteria in the message header. Then, the fine filter further examines these pre-selected messages in detail, applying more specific conditions that must all be met for the associated action to trigger ¶602 "If the event message matches one of the coarse-grain filters, then in block 1742, one or more rules with fine-grain filters are retrieved. Rule constants are extracted from the rules in block 1746. In block 1748, the fine-grain filters are applied to the event message"]
Chandra does not specifically teach coarse filtered sensor data; wherein said coarse filtering is remote from said fine filtering.
However, Boykin teaches coarse filtered sensor data; [bounding box of a camera frame (sensor data) is coarse filter ¶72 "detect various shapes and objects according to embodiments of this disclosure. FIG. 2 depicts the recognition of multiple vehicle shapes (shown in and above bounding boxes) 21 in a video frame 20 of the information captured by a camera device 16"]
wherein said coarse filtering is remote from said fine filtering. [remotely a collecting device such as Fig.1 vehicle computer 12 and server 48 device do filtering for tiered or second level filtering (multiple object recognition with different neural nets) ¶77-78 "the analytics for recognition and detection of the designated content is distributed among the vehicle 10 computer 12 and one or more remote computers (e.g. the server 15 in the police station 14)."…"use a separate neural network to instantly achieve multiple object recognition as described herein"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the rule filters by Chandra by incorporating the coarse filtered sensor data; wherein said coarse filtering is remote from said fine filtering disclosed by Boykin because both techniques address the same field of business analytics and by incorporating Boykin into Chandra saves the time of operators of sensors by automating tasks in simple ways [Boykin ¶8-9]
As to dependent claim 3, the rejection of claim 1 is incorporated Chandra and Boykin further teach wherein the coarse filter comprises a feature of interest. [Boykin shapes of interest Fig. 2 boxes ¶72 "recognition of multiple vehicle shapes (shown in and above bounding boxes) 21 in a video frame 20"]
As to dependent claim 5, the rejection of claim 3 is incorporated Chandra and Boykin further teach wherein the feature comprises a model of a vehicle. [Boykin features for vehicle model ¶67 "vehicle identification parameters/characteristics (makes, models, colors, etc.)"]
As to dependent claim 6, the rejection of claim 3 is incorporated Chandra and Boykin further teach wherein the feature comprises a color of a vehicle. [features for vehicle color ¶67 "vehicle identification parameters/characteristics (makes, models, colors, etc.)"]
As to dependent claim 7, the rejection of claim 28 is incorporated Chandra and Boykin further teach wherein the actor comprises a law enforcement agency. [Boykin law enforcement agencies, applications and offices ¶67-68 ]
As to dependent claim 9, the rejection of claim 1 is incorporated Chandra and Boykin further teach wherein the desired information comprises an identity of a subject. [Boykin recognition of people ¶72 including facial recognition ¶93]
As to dependent claim 10, the rejection of claim 1 is incorporated Chandra and Boykin further teach wherein the desired information comprises a license plate number. [Boykin ¶174 "analyzing the captured visual data, processor may detect the presence of (suspect's) vehicle 3407 and various characteristics thereof (e.g., vehicle type, make, model, color, license plate number, etc.)"]
As to dependent claim 11, the rejection of claim 1 is incorporated Chandra and Boykin further teach deleting all filtered sensor data. [Chanda delete actions ¶590]
As to dependent claim 15, the rejection of claim 1 is incorporated Chandra and Boykin further teach encoding the desired information. [Boykin compression and formats are encoding ¶58]
As to dependent claim 16, the rejection of claim 1 is incorporated Chandra and Boykin further teach wherein said coarse filtering is configured for determining human subjects. [Boykin people Fig. 4 ¶72 " FIG. 4 depicts the recognition of multiple “people” shapes (shown in bounding boxes) 41 "]
As to dependent claim 17, the rejection of claim 1 is incorporated Chandra and Boykin further teach wherein said coarse filtering is configured for determining license plate numbers. [Boykin filters images for license plate ¶59/ ¶69]
As to independent claim 18, the rejection of claim 1 is incorporated Chandra and Boykin further teach System for identifying desired information comprising a processor configured for the method of claim 1. [Chandra system and processor ¶191-192]
As to dependent claim 21, the rejection of claim 1 is incorporated Chandra and Boykin further teach transmitting the desired information. [Boykin remote storage and analysis transmits data ¶66, ¶70]
As to dependent claim 22, the rejection of claim 28 is incorporated Chandra and Boykin further teach confirming an authorization of the actor. [Boykin authorized users ¶94]
As to dependent claim 23, the rejection of claim 1 is incorporated Chandra and Boykin further teach securing said transmitting. [Boykin authorized users and RTSP has security ¶93]
As to dependent claim 25, the rejection of claim 1 is incorporated Chandra and Boykin further teach storing the desired information. [Boykin remote storage ¶66, ¶70]
As to dependent claim 26, the rejection of claim 1 is incorporated Chandra and Boykin further teach wherein said coarse filtering occurs on an autonomous vehicle and said fine filtering occurs in a cloud. [Boykin cloud ¶66, autonomous ¶90]
As to dependent claim 27, the rejection of claim 1 is incorporated Chandra and Boykin further teach wherein the data comprise a location of the collection device. [Boykin GPS metadata (location) ¶59]
As to dependent claim 28, the rejection of claim 34 is incorporated Chandra and Boykin further teach wherein said receiving comprises an actor. [Chandra user defined rules ¶536]
As to dependent claim 30, the rejection of claim 1 is incorporated Chandra and Boykin further teach wherein the collection device is fixed relative to an autonomous vehicle. [Boykin a collection device, such as a camera or microphone mounted on the docking station (which can be considered an autonomous vehicle when referring to police vehicles equipped with advanced technological capabilities), is fixed relative to the autonomous vehicle it is attached to ¶10-12]
As to independent claim 31, the rejection of claim 1 is incorporated Chandra and Boykin further teach computer-readable medium configured for storing instructions configured for the method of claim 1. [Chandra medium and computer program ¶815]
As to independent claim 32, the rejection of claim 1 is incorporated Chandra and Boykin further teach further comprising, prior to said fine filtering, receiving the coarse-filtered sensor data. Boykin remotely a collecting device such as Fig.1 vehicle computer 12 and server 48 device do filtering for tiered or second level filtering (multiple object recognition with different neural nets) ¶77-78 "the analytics for recognition and detection of the designated content is distributed among the vehicle 10 computer 12 and one or more remote computers (e.g. the server 15 in the police station 14)."…"use a separate neural network to instantly achieve multiple object recognition as described herein"]
As to independent claim 33, the rejection of claim 32 is incorporated Chandra and Boykin further teach further comprising securing said receiving. [Boykin authorized users and RTSP has security ¶93]
As to independent claim 34, the rejection of claim 1 is incorporated Chandra and Boykin further teach further comprising, prior to said determining, receiving the rule. [Chandra user defined rules ¶536]
As to independent claim 35, the rejection of claim 1 is incorporated Chandra and Boykin further teach further comprising tuning said coarse filtering and said fine filtering. [Chandra modify the rules that control filtering (tune) ¶581]
Claims 2, 13-14 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Chandra in view of Boykin as applied to the rejection of claim 1 above, and further in view of Darche et al. (US 10587483 B1 hereinafter Darche)
As to dependent claim 2, the combination of Chandra and Boykin teach all the limitations of claim 1 that is incorporated.
Chandra and Boykin do not specifically teach encrypting in place the filtered sensor data and defining encrypted sensor data.
However, Darche teaches encrypting in place the filtered sensor data and defining encrypted sensor data. [encrypting Col. 7 ln. 20-27 "The sensor computer 106 compresses the current file, performs any other operations such as encrypting the current file"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the sensor systems disclosed by Chandra and Boykin by incorporating the encrypting in place the filtered sensor data and defining encrypted sensor data disclosed by Darche because all techniques address the same field of data analysis and by incorporating Darche into Chandra and Boykin provide more effective collection of data and filters deployed [Darche Col. 3 ln. 1-15]
As to dependent claim 13, the combination of Chandra and Boykin teach all the limitations of claim 1 that is incorporated.
Chandra and Boykin do not specifically teach compressing the desired information.
However, Darche teaches compressing the desired information. [compresses Col. 7 ln. 20-27 "The sensor computer 106 compresses the current file, performs any other operations such as encrypting the current file"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the sensor systems disclosed by Chandra and Boykin by incorporating the compressing the desired information disclosed by Darche because all techniques address the same field of data analysis and by incorporating Darche into Chandra and Boykin provide more effective collection of data and filters deployed [Darche Col. 3 ln. 1-15]
As to dependent claim 14, the combination of Chandra and Boykin teach all the limitations of claim 1 that is incorporated.
Chandra and Boykin do not specifically teach encrypting the desired information.
However, Darche teaches encrypting the desired information. [encrypting Col. 7 ln. 20-27 "The sensor computer 106 compresses the current file, performs any other operations such as encrypting the current file"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the sensor systems disclosed by Chandra and Boykin by incorporating the encrypting the desired information disclosed by Darche because all techniques address the same field of data analysis and by incorporating Darche into Chandra and Boykin provide more effective collection of data and filters deployed [Darche Col. 3 ln. 1-15]
As to dependent claim 24, the combination of Chandra, Boykin and Darche teach all the limitations of claim 2 that is incorporated. Chandra, Boykin and Darche further teach storing the encrypted sensor data. [Darche encrypting Col. 7 ln. 20-27 "The sensor computer 106 compresses the current file, performs any other operations such as encrypting the current file"]
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Chandra in view of Boykin as applied to the rejection of claim 3 above, and further in view of Izenson et al. (US 20210191926 A1 hereinafter Izenson)
As to dependent claim 4, the combination of Chandra and Boykin teach all the limitations of claim 3 that is incorporated.
Chandra and Boykin do not specifically teach wherein the feature comprises a height of a subject.
However, Izenson teaches wherein the feature comprises a height of a subject. [Izenson height ¶23]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the sensor systems disclosed by Chandra and Boykin by incorporating the wherein the feature comprises a height of a subject disclosed by Izenson because all techniques address the same field of data analysis and by incorporating Izenson into Chandra and Boykin provides user with more effective or consistent results preventing wastes of time [Izenson ¶2-3]
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Chandra in view of Boykin as applied to the rejection of claim 1 above, and further in view of Muetzel et al. (US 20140376778 A1 hereinafter Muetzel)
As to dependent claim 8, the combination of Chandra and Boykin teach all the limitations of claim 1 that is incorporated.
Chandra and Boykin do not specifically teach
However, Muetzel teaches wherein the rule is based on a warrant [analysis server processes data by doing extractions (filtering) based on a warrant ¶35 “Based on the extracted license plate string, analysis server 150 may determine if the mini-van 320 is, for example, a stolen vehicle, subject to a search warrant, subject to emergency recall, registered to a person or company subject to a search warrant or police investigation, or more”]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the sensor systems disclosed by Chandra and Boykin by incorporating the disclosed by Muetzel because all techniques address the same field of data analysis and by incorporating Muetzel into Chandra and Boykin helps users better identify objects and locations without intervention or automatically [Muetzel ¶33]
Response to Arguments
Applicant's arguments filed 12/23/2025. In the remark, applicant argues that:
Tarantola and Boykin fail to teach “[1] filtering data with the coarse filter and defining coarse-filtered sensor data; and fine filtering the course-filtered sensor data with the fine filter; [3] wherein said coarse filtering is remote from said fine filtering.” As recited by amended claim 1.
As to point (1) Applicant’s arguments with respect to claims have been considered but are moot in view of a new ground of rejection made under 35 U.S.C. 103 as being unpatentable over Chandra in view of Boykin as set forth above. Further to coarse filtering both Boykin and the specification discuss bounding boxes (see spec PGPUB ¶47 and Boykin ¶72)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
Ahmad et al. (US 20180018869 A1) teaches autonomous vehicles with detections of vehicles and their speed (see ¶11)
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
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/BEAU D SPRATT/Primary Examiner, Art Unit 2143