DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 11/13/2024, 11/26/2024, 02/10/2025, and 07/22/2025 have been considered by the examiner.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-7 are rejected under 35 U.S.C. 103 as being unpatentable over Albertson et a. (US Pub. No. 2012/0140042 A1) in view of Wang et al. (US Pub. No. 2020/0151440 A1).
Regarding claim 1, Albertson discloses, an information processing device comprising: at least one memory storing processing instructions; and at least one processor configured to execute the processing instructions to: (See Albertson ¶94, “Processor 512 may be a general-purpose processor such as IBM's POWERPC.RTM. processor that, during normal operation, processes data under the control of an operating system 560, application software 570, middleware (not depicted), and other code accessible from a dynamic storage device such as random-access memory (RAM) 514, a static storage device such as Read Only Memory (ROM) 516, a data storage device, such as mass storage device 518, or other data storage medium.”)
acquire scene information of a target place from another information processing device connected via a network, (See Albertson ¶111, “In addition, client profile service server 640 may monitor and provide additional information about a location of a user from monitored information such as the current location of the user, the current physical environment in which the user is located, the events currently scheduled for a user. In one example, client profile service provider 640 monitors a user's electronic calendar or a user's current GPS location, for example, from the user's personal, portable telephony device.”)
and on a basis of the scene information, acquire reference action information corresponding to the target place, from a storage device in which the reference action information is stored; (See Albertson ¶78, “In the example, behavior interpreter controller 404 accesses behavior definitions from behavior database 112, which includes general behavior definitions 412, environment specific behavior definitions 414, application specific behavior definitions 416, and user specific behavior definitions 418.” Further see Albertson ¶80 Environment specific behavior definitions 414 include behavior definitions and factors for determining whether behavior is adverse that are specific to the context in which the behavior is being detected. Examples of contexts may include, but are not limited to, the current location of a monitored user.”)
extract action information of a detection target in a captured image obtained by capturing an image of the target place; (See Albertson ¶149, “Block 1102 depicts capturing, via a stereoscopic image device, multiple image streams and via sensors, sensor data, within a focus area. Next, block 1104 illustrates tracking objects within the images and sensor data. Thereafter, block 1106 depicts generating a stream of 3D object properties for tracked objects. Thereafter, block 1108 depicts aggregating the 3D object properties for each of the tracked objects. In particular, the aggregated 3D object properties represent one or more objects tracked in association with at least one monitored user representative of behaviors of the at least one monitored user. … Next, block 1110 illustrates predicting at least one type of behavior from the aggregated stream of 3D object properties from one or more behavior definitions that match the aggregated stream of 3D object properties with a percentage of probability. In addition, next, block 1112 depicts predicting whether the behavior is potentially adverse with a percentage probability from the behavior definitions.”)
and detect a predetermined detection target in the captured image based on the reference action information and the action information; (See Albertson ¶118, “For example, a behavior record could also indicate the speed at which the monitored user is walking, the direction the monitored user is walking, and information that would identify the monitored user, such as a shirt color or hair color, if a monitored environment includes more than one monitored user.”)
wherein the reference action information and the action information include direction of face. (As alternatively required Albertson discloses direction of face, see ¶38, “In addition, behavior database 112 includes behavior definitions adjusted according to a corresponding facial expression or other corresponding behavior.”)
Albertson discloses the above limitations but he fails to disclose, wherein the reference action information and the action information include movement route
However, Wang discloses, wherein the reference action information and the action information include movement route. (See Wang ¶86, “For example, as described above with respect to the sensor data monitoring module 610, the facility deficiency server 220 receives and monitors live sensor data to detect deficiencies in facilities as is described herein. In embodiments, the sensor data includes data with respect to multiple different dimensions (e.g., walking speed of an individual, spatial dimensions of the individual’s walking path/trajectory.”
Further see Wang ¶68, “In embodiments, the facility deficiency server 220 detects the abnormalities by comparing the sensor data with training data in which the training data indicates sensor datasets that are considered to be “normal” with consideration to multiple different dimensions (e.g., space, time, weather, climate, special needs factors, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the walking path or trajectory of a monitoring user as monitored behavior as suggested by Wang to Albertson’s monitoring of abnormal behavior in a specific location. This can be done using known engineering techniques, with a reasonable expectation of success. The motivation for doing so is in order to detect specific malicious patterns defined by irregular movement such as pacing random walking or sudden stopping, which typically precedes criminal or suspicious activity.
Regarding claim 2, Albertson and Wang disclose, the information processing device according to Claim 1, wherein the at least one processor is configured to execute the processing instructions to generate the reference action information by learning the stored action information. (See Albertson¶110, “In addition, behavior learning controller 630 gathers other information that enables behavior learning controller 630 to learn and maintain behavior information in behavior database 112 that when accessed by behavior object detector services and behavior interpreter services, increases the accuracy of generation of 3D object properties and accuracy of prediction of behaviors and the potentially adversity of behaviors from 3D object properties by these services.”)
Regarding claim 3, Albertson and Wang disclose, the information processing device according to Claim 1, wherein the at least one processor is configured to execute the processing instructions to display detected the predetermined detection target on the captured image so as to be distinguished. (See Albertson ¶121, “In one example, a transparent colored overlay may be positioned as an image layer over a captured video image within "building B" to indicate a portion of a monitored user triggering an indicator of potentially adverse behavior.”)
Regarding claim 4, Albertson and Wang disclose, the information processing device according to Claim 1, wherein the at least one processor is configured to execute the processing instructions to track detected the predetermined detection target on the captured image (See Albertson ¶149, “Next, block 1104 illustrates tracking objects within the images and sensor data. Thereafter, block 1106 depicts generating a stream of 3D object properties for tracked objects. Thereafter, block 1108 depicts aggregating the 3D object properties for each of the tracked objects. In particular, the aggregated 3D object properties represent one or more objects tracked in association with at least one monitored user representative of behaviors of the at least one monitored user.”)
and display tracking result. (See Albertson ¶125, “In the example, warning controller 708 controls warning signals that turn on a monitoring device through infrared controller 730, that adjust a monitored video image through image overlay controller 740, and that alert an individual supervising user through tactile feedback controller 750 and audio feedback controller 760.”)
Regarding claim 5, Albertson and Wang disclose, the information processing device according to Claim 1, wherein the scene information includes environment information representing surrounding environment of the target place. (See Albertson ¶80, “Environment specific behavior definitions 414 include behavior definitions and factors for determining whether behavior is adverse that are specific to the context in which the behavior is being detected. Examples of contexts may include, but are not limited to, the current location of a monitored user, the time of day, the cultural meanings behind gestures and other behaviors within the context, the languages spoken within the context, and other factors that influence the context in which behavior could be interpreted. The current location of a monitored user may include the country or region in which the user is located and may include the actual physical environment, such as a traffic stop, an enclosed room, or a security checkpoint, for example.”)
Regarding claim 6, Albertson discloses, an information processing method performed by a computer and comprising: acquiring scene information of a target place from another information processing device connected via a network, (See Albertson ¶111, “In addition, client profile service server 640 may monitor and provide additional information about a location of a user from monitored information such as the current location of the user, the current physical environment in which the user is located, the events currently scheduled for a user. In one example, client profile service provider 640 monitors a user's electronic calendar or a user's current GPS location, for example, from the user's personal, portable telephony device.”)
and on a basis of the scene information, acquiring reference action information corresponding to the target place, from a storage device in which the reference action information is stored; (See Albertson ¶78, “In the example, behavior interpreter controller 404 accesses behavior definitions from behavior database 112, which includes general behavior definitions 412, environment specific behavior definitions 414, application specific behavior definitions 416, and user specific behavior definitions 418.” Further see Albertson ¶80 Environment specific behavior definitions 414 include behavior definitions and factors for determining whether behavior is adverse that are specific to the context in which the behavior is being detected. Examples of contexts may include, but are not limited to, the current location of a monitored user.”)
extracting action information of a detection target in a captured image obtained by capturing an image of the target place; (See Albertson ¶149, “Block 1102 depicts capturing, via a stereoscopic image device, multiple image streams and via sensors, sensor data, within a focus area. Next, block 1104 illustrates tracking objects within the images and sensor data. Thereafter, block 1106 depicts generating a stream of 3D object properties for tracked objects. Thereafter, block 1108 depicts aggregating the 3D object properties for each of the tracked objects. In particular, the aggregated 3D object properties represent one or more objects tracked in association with at least one monitored user representative of behaviors of the at least one monitored user. … Next, block 1110 illustrates predicting at least one type of behavior from the aggregated stream of 3D object properties from one or more behavior definitions that match the aggregated stream of 3D object properties with a percentage of probability. In addition, next, block 1112 depicts predicting whether the behavior is potentially adverse with a percentage probability from the behavior definitions.”)
and detecting a predetermined detection target in the captured image based on the reference action information and the action information; (See Albertson ¶118, “For example, a behavior record could also indicate the speed at which the monitored user is walking, the direction the monitored user is walking, and information that would identify the monitored user, such as a shirt color or hair color, if a monitored environment includes more than one monitored user.”)
wherein the reference action information and the action information include direction of face. (As alternatively required Albertson discloses direction of face, see ¶38, “In addition, behavior database 112 includes behavior definitions adjusted according to a corresponding facial expression or other corresponding behavior.”)
Albertson discloses the above limitations but he fails to disclose, wherein the reference action information and the action information include movement route
However, Wang discloses, wherein the reference action information and the action information include movement route. (See Wang ¶86, “For example, as described above with respect to the sensor data monitoring module 610, the facility deficiency server 220 receives and monitors live sensor data to detect deficiencies in facilities as is described herein. In embodiments, the sensor data includes data with respect to multiple different dimensions (e.g., walking speed of an individual, spatial dimensions of the individual’s walking path/trajectory.”
Further see Wang ¶68, “In embodiments, the facility deficiency server 220 detects the abnormalities by comparing the sensor data with training data in which the training data indicates sensor datasets that are considered to be “normal” with consideration to multiple different dimensions (e.g., space, time, weather, climate, special needs factors, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the walking path or trajectory of a monitoring user as monitored behavior as suggested by Wang to Albertson’s monitoring of abnormal behavior in a specific location. This can be done using known engineering techniques, with a reasonable expectation of success. The motivation for doing so is in order to detect specific malicious patterns defined by irregular movement such as pacing random walking or sudden stopping, which typically precedes criminal or suspicious activity.
Regarding claim 7, Albertson discloses, a non-transitory computer-readable storage medium storing a program, the program comprising instructions for causing a computer to execute processes to: (See Albertson ¶94, “Processor 512 may be a general-purpose processor such as IBM's POWERPC.RTM. processor that, during normal operation, processes data under the control of an operating system 560, application software 570, middleware (not depicted), and other code accessible from a dynamic storage device such as random-access memory (RAM) 514, a static storage device such as Read Only Memory (ROM) 516, a data storage device, such as mass storage device 518, or other data storage medium.”)
acquire scene information of a target place from another information processing device connected via a network, and on a basis of the scene information, (See Albertson ¶111, “In addition, client profile service server 640 may monitor and provide additional information about a location of a user from monitored information such as the current location of the user, the current physical environment in which the user is located, the events currently scheduled for a user. In one example, client profile service provider 640 monitors a user's electronic calendar or a user's current GPS location, for example, from the user's personal, portable telephony device.”)
acquire reference action information corresponding to the target place, from a storage device in which the reference action information is stored; (See Albertson ¶78, “In the example, behavior interpreter controller 404 accesses behavior definitions from behavior database 112, which includes general behavior definitions 412, environment specific behavior definitions 414, application specific behavior definitions 416, and user specific behavior definitions 418.” Further see Albertson ¶80 Environment specific behavior definitions 414 include behavior definitions and factors for determining whether behavior is adverse that are specific to the context in which the behavior is being detected. Examples of contexts may include, but are not limited to, the current location of a monitored user.”)
extract action information of a detection target in a captured image obtained by capturing an image of the target place; (See Albertson ¶149, “Block 1102 depicts capturing, via a stereoscopic image device, multiple image streams and via sensors, sensor data, within a focus area. Next, block 1104 illustrates tracking objects within the images and sensor data. Thereafter, block 1106 depicts generating a stream of 3D object properties for tracked objects. Thereafter, block 1108 depicts aggregating the 3D object properties for each of the tracked objects. In particular, the aggregated 3D object properties represent one or more objects tracked in association with at least one monitored user representative of behaviors of the at least one monitored user. … Next, block 1110 illustrates predicting at least one type of behavior from the aggregated stream of 3D object properties from one or more behavior definitions that match the aggregated stream of 3D object properties with a percentage of probability. In addition, next, block 1112 depicts predicting whether the behavior is potentially adverse with a percentage probability from the behavior definitions.”)
and detect a predetermined detection target in the captured image based on the reference action information and the action information; (See Albertson ¶118, “For example, a behavior record could also indicate the speed at which the monitored user is walking, the direction the monitored user is walking, and information that would identify the monitored user, such as a shirt color or hair color, if a monitored environment includes more than one monitored user.”)
wherein the reference action information and the action information include direction of face. (As alternatively required Albertson discloses direction of face, see ¶38, “In addition, behavior database 112 includes behavior definitions adjusted according to a corresponding facial expression or other corresponding behavior.”)
Albertson discloses the above limitations but he fails to disclose, wherein the reference action information and the action information include movement route
However, Wang discloses, wherein the reference action information and the action information include movement route. (See Wang ¶86, “For example, as described above with respect to the sensor data monitoring module 610, the facility deficiency server 220 receives and monitors live sensor data to detect deficiencies in facilities as is described herein. In embodiments, the sensor data includes data with respect to multiple different dimensions (e.g., walking speed of an individual, spatial dimensions of the individual’s walking path/trajectory.”
Further see Wang ¶68, “In embodiments, the facility deficiency server 220 detects the abnormalities by comparing the sensor data with training data in which the training data indicates sensor datasets that are considered to be “normal” with consideration to multiple different dimensions (e.g., space, time, weather, climate, special needs factors, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the walking path or trajectory of a monitoring user as monitored behavior as suggested by Wang to Albertson’s monitoring of abnormal behavior in a specific location. This can be done using known engineering techniques, with a reasonable expectation of success. The motivation for doing so is in order to detect specific malicious patterns defined by irregular movement such as pacing random walking or sudden stopping, which typically precedes criminal or suspicious activity.
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered pertinent to applicant’s disclosure.
Iwai (US Pub. No. 2015/0010204 A1) A behavior analysis/monitoring device includes: a person detection unit configured to detect a person(s) from image information obtained by capturing images covering an area around an item placement area; a part-of-interest detection unit configured to detect, for each person detected by the person detection unit, a part of interest set in a part of an upper body of the person excluding hands and arms; a position measurement unit configured to measure a position of the part of interest detected by the part-of-interest detection unit; and an item pick-up action determination unit configured to obtain a displacement of the part of interest based on the position of the part of interest obtained by the position measurement unit and to determine whether each person detected by the person detection unit performed an item pick-up action based on the displacement of the part of interest of the person.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID PERLMAN whose telephone number is (571) 270-1417.
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(571) 273-8300.
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/DAVID PERLMAN/Primary Examiner, Art Unit 2673
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