DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
2. Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
3. The information disclosure statement (IDS) submitted on 11/20/2024 and 06/08/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 103
4. 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.
5. 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.
6. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
7. Claim(s) 1-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Titus et al. (US 2010/0026802 A1) in view of Qin (US 2024/0346256 A1).
8. With reference to claim 1, Titus teaches An intelligent security system (“The automatic video surveillance system of the invention is for monitoring a location for, for example, market research or security purposes.” [0073] “Block 241 shows an intelligence-enabled network of one or more video cameras within a facility or across multiple facilities.” [0096]) Titus also teaches an artificial intelligence (AI) video analyzer configured to process a video signal input from a camera and generate object description information that describes an object included in the video; (“Block 221 represents a raw (uncompressed) digital video input. This can be obtained, for example, through analog to digital capture of an analog video signal or decoding of a digital video signal. Block 222 represents a hardware platform housing the main components of the video surveillance system (video content analysis--block 225--and activity inference--block 226).” [0077] “FIG. 25 shows a network (block 251) with a number of potential intelligence-enabled devices connected to it. Block 252 is an IP camera with content analysis components on board that can stream primitives over a network. Block 253 is an IP camera with both content analysis and activity inference components on board that can be programmed directly with rules and will generate network alerts directly. In an exemplary embodiment, the IP cameras in block 253 and 254 may detect rules directly from video, for example, while in a configuration mode. … Block 257 is a handheld PDA enabled with wireless network communications that has activity inference algorithms on board and is capable of accepting video primitives from the network and displaying alerts. Block 258 is complete intelligent video analysis system capable of accepting analog or digital video streams, performing content analysis and activity inference and displaying alerts on a series of alert consoles.” [0101] “main DSP application 2606 may take an alert and send it to another algorithm running on hardware platform 2601. This may, for example, be a facial recognition algorithm to be executed upon a person-based rule being triggered. In such a case, the handoff may be made if the alert contains an object field that indicates that the object type is a person.” [0107] “An event discriminator refers to one or more objects optionally interacting with one or more spatial attributes and/or one or more temporal attributes. An event discriminator is described in terms of video primitives (also called activity description meta-data). Some of the video primitive design criteria include the following: capability of being extracted from the video stream in real-time; inclusion of all relevant information from the video; and conciseness of representation.” [0148] “FIG. 16a shows an exemplary video analysis portion of a video surveillance system according to an embodiment of the invention. In FIG. 16a, a video sensor (for example, but not limited to, a video camera) 1601 may provide a video stream 1602 to a video analysis subsystem 1603. Video analysis subsystem 1603 may then perform analysis of the video stream 1602 to derive video primitives, which may be stored in primitive storage 1605.” [0170]) Titus further teaches an intelligent query generator configured to generate a query including a description of the video from the object description information output by the AI video analyzer; (“FIG. 25 shows a network (block 251) with a number of potential intelligence-enabled devices connected to it. Block 252 is an IP camera with content analysis components on board that can stream primitives over a network. Block 253 is an IP camera with both content analysis and activity inference components on board that can be programmed directly with rules and will generate network alerts directly. In an exemplary embodiment, the IP cameras in block 253 and 254 may detect rules directly from video, for example, while in a configuration mode. … Block 257 is a handheld PDA enabled with wireless network communications that has activity inference algorithms on board and is capable of accepting video primitives from the network and displaying alerts. Block 258 is complete intelligent video analysis system capable of accepting analog or digital video streams, performing content analysis and activity inference and displaying alerts on a series of alert consoles.” [0101] “An event discriminator refers to one or more objects optionally interacting with one or more spatial attributes and/or one or more temporal attributes. An event discriminator is described in terms of video primitives (also called activity description meta-data). Some of the video primitive design criteria include the following: capability of being extracted from the video stream in real-time; inclusion of all relevant information from the video; and conciseness of representation.” [0148] “in case of a video surveillance application, the properties may be features of the object detected in the video stream, such as size, speed, color, classification (human, vehicle), or the properties may be scene change properties. FIG. 17 gives examples of using such queries. In FIG. 17a, the query, "Show me any red vehicle," 171 is posed. This may be decomposed into two "property relationship value" (or simply "property") queries, testing whether the classification of an object is vehicle 173 and whether its color is predominantly red 174. These two sub-queries can combined with the Boolean operator "and" 172.” [0174] “the required activity query is to "find a red vehicle making an illegal left turn" 201. The illegal left turn may be captured through a combination of activity descriptors and modified Boolean operators. One virtual tripwire may be used to detect objects coming out of the side street 193, and another virtual tripwire may be used to detect objects traveling to the left along the road 205. These may be combined by a modified "and" operator 202. The standard Boolean "and" operator guarantees that both activities 193 and 205 have to be detected. The object modifier 203 checks that the same object crossed both tripwires, while the temporal modifier 204 checks that the bottom-to-top tripwire 193 is crossed first, followed by the crossing of the right-to-left tripwire 205 no more than 10 seconds later.” [0183]) Titus teaches a security event processor configured to input the query generated by the intelligent query generator, and identify a security event. (“FIG. 25 shows a network (block 251) with a number of potential intelligence-enabled devices connected to it. Block 252 is an IP camera with content analysis components on board that can stream primitives over a network. Block 253 is an IP camera with both content analysis and activity inference components on board that can be programmed directly with rules and will generate network alerts directly. In an exemplary embodiment, the IP cameras in block 253 and 254 may detect rules directly from video, for example, while in a configuration mode. … Block 257 is a handheld PDA enabled with wireless network communications that has activity inference algorithms on board and is capable of accepting video primitives from the network and displaying alerts. Block 258 is complete intelligent video analysis system capable of accepting analog or digital video streams, performing content analysis and activity inference and displaying alerts on a series of alert consoles.” [0101] “once the video, and, if there are other sensors, the non-video primitives 161 are available, the system may detect events. The user tasks the system by defining rules 163 and corresponding responses 164 using the rule and response definition interface 162. In an exemplary embodiment, the rule response and definition interface 162 may receive rules detected directly from incoming video, as described above with reference to FIGS. 29-31. The areas of interest, tripwires, direction, speed, etc. detected rules may be available to the user in tasking the system. The rules are translated into event discriminators, and the system extracts corresponding event occurrences 165. The detected event occurrences 166 trigger user defined responses 167. A response may include a snapshot of a video of the detected event from video storage 168 (which may or may not be the same as video storage 1604 in FIG. 16a). The video storage 168 may be part of the video surveillance system, or it may be a separate recording device 15. Examples of a response may include, but are not necessarily limited to, the following: activating a visual and/or audio alert on a system display; activating a visual and/or audio alarm system at the location; activating a silent alarm; activating a rapid response mechanism; locking a door; contacting a security service; forwarding or streaming data (e.g., image data, video data, video primitives; and/or analyzed data) to another computer system via a network, such as, but not limited to, the Internet;” [0171] “the required activity query is to "find a red vehicle making an illegal left turn" 201. The illegal left turn may be captured through a combination of activity descriptors and modified Boolean operators. One virtual tripwire may be used to detect objects coming out of the side street 193, and another virtual tripwire may be used to detect objects traveling to the left along the road 205. These may be combined by a modified "and" operator 202. The standard Boolean "and" operator guarantees that both activities 193 and 205 have to be detected. The object modifier 203 checks that the same object crossed both tripwires, while the temporal modifier 204 checks that the bottom-to-top tripwire 193 is crossed first, followed by the crossing of the right-to-left tripwire 205 no more than 10 seconds later.” [0183] “The video surveillance system of the invention operates automatically, detects and archives video primitives of objects in the scene, and detects event occurrences in real time using event discriminators. In addition, action is taken in real time, as appropriate, such as activating alarms, generating reports, and generating output.” [0188])
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Titus does not explicitly teach generative AI, process a response by the generative AI. This is what Qin teaches (“an LLM may be augmented with augmentation information (e.g., domain-specific information; entity-specific information; product-specific information; recent information unavailable at generation of the large language model; or information changed after generation of the large language model). A retrieval augmented generation (RAG) approach is disclosed herein that adds an information retrieval component to create augmented prompts to feed into the generative language model for generating the final answer/prediction.” [0018] “prompt generator 212 may employ natural language processing (NLP) techniques to generate an augmented prompt 236 that requests LLM 214 to respond to query 216 based on contextual information using augmentation information 232.” [0046]) Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Qin into Titus, in order to improve the scope and accuracy of responses generated by an LLM.
9. With reference to claim 2, Titus teaches the AI video analyzer comprises: an object recognition unit configured to process the video signal input from the camera and extract an object included in the video; (“Block 221 represents a raw (uncompressed) digital video input. This can be obtained, for example, through analog to digital capture of an analog video signal or decoding of a digital video signal. Block 222 represents a hardware platform housing the main components of the video surveillance system (video content analysis--block 225--and activity inference--block 226).” [0077] “FIG. 25 shows a network (block 251) with a number of potential intelligence-enabled devices connected to it. Block 252 is an IP camera with content analysis components on board that can stream primitives over a network. Block 253 is an IP camera with both content analysis and activity inference components on board that can be programmed directly with rules and will generate network alerts directly. In an exemplary embodiment, the IP cameras in block 253 and 254 may detect rules directly from video, for example, while in a configuration mode. … Block 257 is a handheld PDA enabled with wireless network communications that has activity inference algorithms on board and is capable of accepting video primitives from the network and displaying alerts. Block 258 is complete intelligent video analysis system capable of accepting analog or digital video streams, performing content analysis and activity inference and displaying alerts on a series of alert consoles.” [0101] “An event discriminator refers to one or more objects optionally interacting with one or more spatial attributes and/or one or more temporal attributes. An event discriminator is described in terms of video primitives (also called activity description meta-data). Some of the video primitive design criteria include the following: capability of being extracted from the video stream in real-time; inclusion of all relevant information from the video; and conciseness of representation.” [0148]) Titus also teaches an object description text generation unit configured to generate object description text information describing the object extracted by the object recognition unit; (“An event discriminator refers to one or more objects optionally interacting with one or more spatial attributes and/or one or more temporal attributes. An event discriminator is described in terms of video primitives (also called activity description meta-data). Some of the video primitive design criteria include the following: capability of being extracted from the video stream in real-time; inclusion of all relevant information from the video; and conciseness of representation.” [0148] “Exemplary object descriptors may include generic properties including, but not limited to, size, shape, perimeter, position, trajectory, speed and direction of motion, motion salience and its features, color, rigidity, texture, and/or classification.” [0154] “The video surveillance system of the invention operates automatically, detects and archives video primitives of objects in the scene, and detects event occurrences in real time using event discriminators. In addition, action is taken in real time, as appropriate, such as activating alarms, generating reports, and generating output.” [0188] “The exemplary report includes an image from a video marked-up to include labels and trajectory indications and text describing the marked-up image. The system of the example is tasked with searching for a number of areas: length, position, and time of a trajectory of an object; time and location an object was immobile; correlation of trajectories with areas, as specified by the operator; and classification of an object as not a person, one person, two people, and three or more people.” [0213]) Titus further teaches an object description graphic generation unit configured to generate object description graphic information describing the object extracted by the object recognition unit; an object description graphic editing unit configured to display an area of the object extracted by the object recognition unit as a bounding box in at least one piece of still video extracted from the video signal and to process a graphic annotation by adding the object description graphic information generated by the object description graphic generation unit to a region around the area of the object displayed as the bounding box in the still video; (“The illegal left turn may be captured through a combination of activity descriptors and modified Boolean operators. One virtual tripwire may be used to detect objects coming out of the side street 193, and another virtual tripwire may be used to detect objects traveling to the left along the road 205. These may be combined by a modified "and" operator 202. The standard Boolean "and" operator guarantees that both activities 193 and 205 have to be detected. The object modifier 203 checks that the same object crossed both tripwires, while the temporal modifier 204 checks that the bottom-to-top tripwire 193 is crossed first, followed by the crossing of the right-to-left tripwire 205 no more than 10 seconds later.” [0183] “The left turn may be expressed as the tripwire activity detectors 2112 and 2113, corresponding to tripwires 2102 and 2103, respectively, joined with the "and" combinator 2111 with the object modifier "same" 2117 and temporal modifier "2112 before 2113" 2118. Similarly, the right turn may be expressed as the tripwire activity detectors 2115 and 2116, corresponding to tripwires 2105 and 2106, respectively, joined with the "and" combinator 2114 with the object modifier "same" 2119 and temporal modifier "2115 before 2116" 2120. To detect that the same object turned first left then right, the left turn detector 2111 and the right turn detector 2114 are joined with the "and" combinator 2121 with the object modifier "same" 2122 and temporal modifier "2111 before 2114" 2123. Finally, to ensure that the detected object is a vehicle, a Boolean "and" operator 2125 is used to combine the left-and-right-turn detector 2121 and the property query 2124.” [0186] “The activity record includes, for example: details of a trajectory of an object; a time of detection of an object; a position of detection of an object, and a description or definition of the event discriminator that was employed. The activity record can include information, such as video primitives, needed by the event discriminator. The activity record can also include representative video or still imagery of the object(s) and/or area(s) involved in the event occurrence.” [0205] “the exemplary report is an image from a video marked-up to include labels, graphics, statistical information, and an analysis of the statistical information. For example, the area identified as coffee has statistical information of an average number of customers in the area of 2/hour and an average dwell time in the area as 5 seconds. The system determined this area to be a "cold" region, which means there is not much commercial activity through this region. … the exemplary report is an image from a video marked-up to include labels, graphics, statistical information, and an analysis of the statistical information. For example, the area at the back of the aisle has average number of customers of 14/hour and is determined to have low traffic. … if the operator desires more information about any particular area or any particular area, a point-and-click interface allows the operator to navigate through representative still and video imagery of regions and/or activities that the system has detected and archived.” [0210-0212] “the operator can mark up a video image from the sensor with a graphic representing the appearance of a known-sized object, such as a person.” [0217]) Titus teaches an object description information output unit configured to output the object description text information generated by the object description text generation unit and the object description graphic information graphically annotated by the object description graphic editing unit to the intelligent query generator. (“the required activity query is to "find a red vehicle making an illegal left turn" 201. The illegal left turn may be captured through a combination of activity descriptors and modified Boolean operators. One virtual tripwire may be used to detect objects coming out of the side street 193, and another virtual tripwire may be used to detect objects traveling to the left along the road 205. These may be combined by a modified "and" operator 202.” [0183] “output is generated. The output is based on the event occurrences extracted in block 44 and a direct feed of the source video from block 41. The output is stored on a computer-readable medium, displayed on the computer system 11 or another computer system, or forwarded to another computer system. As the system operates, information regarding event occurrences is collected, and the information can be viewed by the operator at any time, including real time. … The output can include a display from the direct feed of the source video from block 41 transmitted either via analog video transmission means or via network video streaming.” [0206-0207] “the exemplary report is an image from a video marked-up to include labels, graphics, statistical information, and an analysis of the statistical information. For example, the area identified as coffee has statistical information of an average number of customers in the area of 2/hour and an average dwell time in the area as 5 seconds. The system determined this area to be a "cold" region, which means there is not much commercial activity through this region.” [0210] “The exemplary report includes an image from a video marked-up to include labels and trajectory indications and text describing the marked-up image. The system of the example is tasked with searching for a number of areas: length, position, and time of a trajectory of an object; time and location an object was immobile; correlation of trajectories with areas, as specified by the operator; and classification of an object as not a person, one person, two people, and three or more people.” [0213])
10. With reference to claim 3, Titus teaches the object description graphic information comprises a graphic that indicates a movement line of the object. (“objects are detected via movement. Any motion detection algorithm for detecting movement between frames at the pixel level can be used for this block.” [0191] “the exemplary report is an image from a video marked-up to include labels, graphics, statistical information, and an analysis of the statistical information. For example, the area identified as coffee has statistical information of an average number of customers in the area of 2/hour and an average dwell time in the area as 5 seconds. The system determined this area to be a "cold" region, which means there is not much commercial activity through this region.” [0210] “Block 71 is the same as block 41, except that a typical object moves through the scene at various trajectories. The typical object can have various velocities and be stationary at various positions. For example, the typical object moves as close to the video sensor as possible and then moves as far away from the video sensor as possible. This motion by the typical object can be repeated as necessary.” [0220])
11. With reference to claim 4, Titus teaches the security event processor comprises: a parsing unit configured to extract security-related keywords; a security event identification unit configured to analyze the security-related keywords extracted by the parsing unit to determine whether a specific security event has occurred; and a security event response unit configured to output response information for the security event that has occurred when the security event identification unit determines that the specific security event has occurred. (“FIG. 25 shows a network (block 251) with a number of potential intelligence-enabled devices connected to it. Block 252 is an IP camera with content analysis components on board that can stream primitives over a network. Block 253 is an IP camera with both content analysis and activity inference components on board that can be programmed directly with rules and will generate network alerts directly. In an exemplary embodiment, the IP cameras in block 253 and 254 may detect rules directly from video, for example, while in a configuration mode. … Block 257 is a handheld PDA enabled with wireless network communications that has activity inference algorithms on board and is capable of accepting video primitives from the network and displaying alerts. Block 258 is complete intelligent video analysis system capable of accepting analog or digital video streams, performing content analysis and activity inference and displaying alerts on a series of alert consoles.” [0101] “once the video, and, if there are other sensors, the non-video primitives 161 are available, the system may detect events. The user tasks the system by defining rules 163 and corresponding responses 164 using the rule and response definition interface 162. In an exemplary embodiment, the rule response and definition interface 162 may receive rules detected directly from incoming video, as described above with reference to FIGS. 29-31. The areas of interest, tripwires, direction, speed, etc. detected rules may be available to the user in tasking the system. The rules are translated into event discriminators, and the system extracts corresponding event occurrences 165. The detected event occurrences 166 trigger user defined responses 167. A response may include a snapshot of a video of the detected event from video storage 168 (which may or may not be the same as video storage 1604 in FIG. 16a). The video storage 168 may be part of the video surveillance system, or it may be a separate recording device 15. Examples of a response may include, but are not necessarily limited to, the following: activating a visual and/or audio alert on a system display; activating a visual and/or audio alarm system at the location; activating a silent alarm; activating a rapid response mechanism; locking a door; contacting a security service; forwarding or streaming data (e.g., image data, video data, video primitives; and/or analyzed data) to another computer system via a network, such as, but not limited to, the Internet;” [0171] “the required activity query is to "find a red vehicle making an illegal left turn" 201. The illegal left turn may be captured through a combination of activity descriptors and modified Boolean operators. One virtual tripwire may be used to detect objects coming out of the side street 193, and another virtual tripwire may be used to detect objects traveling to the left along the road 205. These may be combined by a modified "and" operator 202. The standard Boolean "and" operator guarantees that both activities 193 and 205 have to be detected. The object modifier 203 checks that the same object crossed both tripwires, while the temporal modifier 204 checks that the bottom-to-top tripwire 193 is crossed first, followed by the crossing of the right-to-left tripwire 205 no more than 10 seconds later.” [0183] “The video surveillance system of the invention operates automatically, detects and archives video primitives of objects in the scene, and detects event occurrences in real time using event discriminators. In addition, action is taken in real time, as appropriate, such as activating alarms, generating reports, and generating output.” [0188] “output is generated. The output is based on the event occurrences extracted in block 44 and a direct feed of the source video from block 41. The output is stored on a computer-readable medium, displayed on the computer system 11 or another computer system, or forwarded to another computer system. As the system operates, information regarding event occurrences is collected, and the information can be viewed by the operator at any time, including real time. … The output can include a display from the direct feed of the source video from block 41 transmitted either via analog video transmission means or via network video streaming.” [0206-0207])
12. With reference to claim 5, Titus teaches a multimodal AI analyzer configured to process sensing information input from at least one sensor node, generate multimodal information, and output the multimodal information to the intelligent query generator. (“FIG. 25 shows a network (block 251) with a number of potential intelligence-enabled devices connected to it. Block 252 is an IP camera with content analysis components on board that can stream primitives over a network. Block 253 is an IP camera with both content analysis and activity inference components on board that can be programmed directly with rules and will generate network alerts directly. In an exemplary embodiment, the IP cameras in block 253 and 254 may detect rules directly from video, for example, while in a configuration mode. … Block 257 is a handheld PDA enabled with wireless network communications that has activity inference algorithms on board and is capable of accepting video primitives from the network and displaying alerts. Block 258 is complete intelligent video analysis system capable of accepting analog or digital video streams, performing content analysis and activity inference and displaying alerts on a series of alert consoles.” [0101] “The other sensors 17 provide additional input to the computer system 11. … Examples of another sensor 17 include, but are not limited to: a motion sensor; an optical tripwire; a biometric sensor; an RFID sensor; and a card-based or keypad-based authorization system. The outputs of the other sensors 17 can be recorded by the computer system 11, recording devices, and/or recording systems.” [0133] “Primitives may also come from non-video sources, such as audio sensors, heat sensors, pressure sensors, card readers, RFID tags, biometric sensors, etc.” [0156] “once the video, and, if there are other sensors, the non-video primitives 161 are available, the system may detect events. The user tasks the system by defining rules 163 and corresponding responses 164 using the rule and response definition interface 162. In an exemplary embodiment, the rule response and definition interface 162 may receive rules detected directly from incoming video, as described above with reference to FIGS. 29-31. The areas of interest, tripwires, direction, speed, etc. detected rules may be available to the user in tasking the system. The rules are translated into event discriminators, and the system extracts corresponding event occurrences 165. The detected event occurrences 166 trigger user defined responses 167. A response may include a snapshot of a video of the detected event from video storage 168 (which may or may not be the same as video storage 1604 in FIG. 16a). The video storage 168 may be part of the video surveillance system, or it may be a separate recording device 15. Examples of a response may include, but are not necessarily limited to, the following: activating a visual and/or audio alert on a system display; activating a visual and/or audio alarm system at the location; activating a silent alarm; activating a rapid response mechanism; locking a door; contacting a security service; forwarding or streaming data (e.g., image data, video data, video primitives; and/or analyzed data) to another computer system via a network, such as, but not limited to, the Internet;” [0171] “the required activity query is to "find a red vehicle making an illegal left turn" 201. The illegal left turn may be captured through a combination of activity descriptors and modified Boolean operators. One virtual tripwire may be used to detect objects coming out of the side street 193, and another virtual tripwire may be used to detect objects traveling to the left along the road 205. These may be combined by a modified "and" operator 202. The standard Boolean "and" operator guarantees that both activities 193 and 205 have to be detected. The object modifier 203 checks that the same object crossed both tripwires, while the temporal modifier 204 checks that the bottom-to-top tripwire 193 is crossed first, followed by the crossing of the right-to-left tripwire 205 no more than 10 seconds later.” [0183] “output is generated. The output is based on the event occurrences extracted in block 44 and a direct feed of the source video from block 41. The output is stored on a computer-readable medium, displayed on the computer system 11 or another computer system, or forwarded to another computer system. As the system operates, information regarding event occurrences is collected, and the information can be viewed by the operator at any time, including real time. … The output can include a display from the direct feed of the source video from block 41 transmitted either via analog video transmission means or via network video streaming.” [0206-0207])
13. With reference to claim 6, Titus teaches the sensor node comprises at least one of an acoustic sensor for detecting sound, an olfactory sensor for detecting smell, a distance sensor for detecting distance, a temperature sensor for detecting temperature, a humidity sensor for detecting humidity, an illuminance sensor for detecting illuminance, and a concentration sensor for detecting concentration. (“FIG. 25 shows a network (block 251) with a number of potential intelligence-enabled devices connected to it. Block 252 is an IP camera with content analysis components on board that can stream primitives over a network. Block 253 is an IP camera with both content analysis and activity inference components on board that can be programmed directly with rules and will generate network alerts directly. In an exemplary embodiment, the IP cameras in block 253 and 254 may detect rules directly from video, for example, while in a configuration mode. … Block 257 is a handheld PDA enabled with wireless network communications that has activity inference algorithms on board and is capable of accepting video primitives from the network and displaying alerts. Block 258 is complete intelligent video analysis system capable of accepting analog or digital video streams, performing content analysis and activity inference and displaying alerts on a series of alert consoles.” [0101] “The other sensors 17 provide additional input to the computer system 11. … Examples of another sensor 17 include, but are not limited to: a motion sensor; an optical tripwire; a biometric sensor; an RFID sensor; and a card-based or keypad-based authorization system. The outputs of the other sensors 17 can be recorded by the computer system 11, recording devices, and/or recording systems.” [0133] “Primitives may also come from non-video sources, such as audio sensors, heat sensors, pressure sensors, card readers, RFID tags, biometric sensors, etc.” [0156])
14. With reference to claim 7, Titus teaches the multimodal AI analyzer generates multimodal information comprising at least one of sound description information obtained by analyzing an acoustic signal input from the acoustic sensor, smell description information obtained by analyzing a smell signal input from the olfactory sensor, distance description information obtained by analyzing a distance signal input from the distance sensor, temperature description information obtained by analyzing a temperature signal input from the temperature sensor, humidity description information obtained by analyzing a humidity signal input from the humidity sensor, illuminance description information obtained by analyzing a humidity signal input from the illuminance sensor, or concentration description information obtained by analyzing a concentration signal input from the concentration sensor. (“FIG. 25 shows a network (block 251) with a number of potential intelligence-enabled devices connected to it. Block 252 is an IP camera with content analysis components on board that can stream primitives over a network. Block 253 is an IP camera with both content analysis and activity inference components on board that can be programmed directly with rules and will generate network alerts directly. In an exemplary embodiment, the IP cameras in block 253 and 254 may detect rules directly from video, for example, while in a configuration mode. … Block 257 is a handheld PDA enabled with wireless network communications that has activity inference algorithms on board and is capable of accepting video primitives from the network and displaying alerts. Block 258 is complete intelligent video analysis system capable of accepting analog or digital video streams, performing content analysis and activity inference and displaying alerts on a series of alert consoles.” [0101] “The other sensors 17 provide additional input to the computer system 11. … Examples of another sensor 17 include, but are not limited to: a motion sensor; an optical tripwire; a biometric sensor; an RFID sensor; and a card-based or keypad-based authorization system. The outputs of the other sensors 17 can be recorded by the computer system 11, recording devices, and/or recording systems.” [0133] “Primitives may also come from non-video sources, such as audio sensors, heat sensors, pressure sensors, card readers, RFID tags, biometric sensors, etc.” [0156] “once the video, and, if there are other sensors, the non-video primitives 161 are available, the system may detect events. The user tasks the system by defining rules 163 and corresponding responses 164 using the rule and response definition interface 162. In an exemplary embodiment, the rule response and definition interface 162 may receive rules detected directly from incoming video, as described above with reference to FIGS. 29-31. The areas of interest, tripwires, direction, speed, etc. detected rules may be available to the user in tasking the system. The rules are translated into event discriminators, and the system extracts corresponding event occurrences 165. The detected event occurrences 166 trigger user defined responses 167. A response may include a snapshot of a video of the detected event from video storage 168 (which may or may not be the same as video storage 1604 in FIG. 16a). The video storage 168 may be part of the video surveillance system, or it may be a separate recording device 15. Examples of a response may include, but are not necessarily limited to, the following: activating a visual and/or audio alert on a system display; activating a visual and/or audio alarm system at the location; activating a silent alarm; activating a rapid response mechanism; locking a door; contacting a security service; forwarding or streaming data (e.g., image data, video data, video primitives; and/or analyzed data) to another computer system via a network, such as, but not limited to, the Internet;” [0171])
Conclusion
15. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michelle Chin whose telephone number is (571)270-3697. The examiner can normally be reached on Monday-Friday 8:00 AM-4:30 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http:/Awww.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Kent Chang can be reached on (571)272-7667. The fax phone number for the organization where this application or proceeding is assigned is (571)273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https:/Awww.uspto.gov/patents/apply/patent- center for more information about Patent Center and https:/Awww.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MICHELLE CHIN/
Primary Examiner, Art Unit 2614
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