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 .
Response to Arguments
Applicant’s arguments, see page 6, filed 06/15/2026, with respect to the abstract have been fully considered and are persuasive. The objection of the abstract has been withdrawn.
Applicant's arguments filed 06/15/2026 have been fully considered but they are not persuasive. Applicant contends that the amendments to independent claims 1 and 4, including limitations reciting that the processor “classifies the task with a small computation amount of computationally intensive object detection processing by recognizing the object from the extracted range on the video data” and “decreases a number of times of object detection processing by performing computationally intensive object detection processing only when the worker uses the object,” demonstrate a technological improvement by reducing computational load and the frequency of object detection processing.
The Examiner does not agree. The newly added limitations merely recite performing otherwise conventional object detection processing less frequently or on a selected region of video data. These limitations describe when generic object detection processing is performed rather than reciting any technological improvement to how object detection processing, image processing, computer functionality, or another technological process is performed. The claims do not recite a new object detection technique, an improvement to computer architecture, an improvement to processor or memory operation, or any other technological improvement to the functioning of a computer or another technology or technical field. Rather, the additional limitations merely optimize the application of the abstract idea by reducing the frequency or scope of conventional processing.
Accordingly, the additional limitations do not integrate the judicial exception into a practical application under Step 2A, Prong Two (see MPEP § 2106.05(a)). Instead, they merely apply the abstract idea using generic computer components as tools to perform their well-understood, routine, and conventional functions.
Furthermore, the additional limitations do not amount to significantly more than the judicial exception under Step 2B. The recited processor, memory, image extraction, object recognition, and object detection operations are recited at a high level of generality and perform their ordinary and expected functions. The claim does not recite an inventive concept sufficient to transform the judicial exception into patent-eligible subject matter. Accordingly, the rejection under 35 U.S.C. § 101 is maintained.
Applicant argues that Ke does not disclose (i) extracting, from video data on the basis of estimated motion information, a range on the video data pertaining to an object associated with the motion information, (ii) recognizing the object within the extracted range, and (iii) identifying a task of the worker on the basis of the recognized object. Applicant further argues that Ke merely recognizes a person’s activity based on joint positions. These arguments are not persuasive.
As explained in the Office Action, Ke teaches estimating body motion from pose estimation, tracked trajectories, and optical flow (pp. 90, 92, 97, 112). Ke further teaches extracting spatial-temporal regions associated with the estimated motion, including spatial-temporal region extraction and region matching using space-time volumes (STVs) (p. 98). Thus, Ke teaches extracting a range of video data associated with estimated motion information.
Applicant’s argument does not address the cited disclosures of STVs, spatial-temporal region extraction, or motion estimation relied upon by the Examiner. Instead, Applicant characterizes Ke as merely recognizing activities from joint positions. However, the rejection relies on the combined teachings of Ke as a whole, not solely on the pose estimation discussion.
Applicant additionally argues that Ke does not recognize an object within the extracted range. However, Ke teaches segmenting the human object from the video sequence, extracting characteristics of the segmented object, and subsequently applying activity detection or classification algorithms to the extracted features (p. 90). Thus, the segmented object within the extracted video region is recognized prior to activity classification. Accordingly, Ke teaches recognizing an object within the extracted range as claimed.
Applicant further argues that Ke does not identify a worker’s task on the basis of the recognized object. However, Ke expressly teaches applying activity detection or classification algorithms to the extracted features to recognize human activities (p. 90), recognizing activities through multidimensional indexing (p. 111), analyzing tracked trajectories to determine activities or behaviors (p. 112), and classifying activities using K-nearest neighbor classification (p. 111). The cited disclosures collectively teach identifying a worker’s task based upon the recognized object and associated extracted features. Applicant has not identified persuasive distinctions between these teachings and the claimed subject matter. Accordingly, the Examiner maintains that Ke discloses each of the disputed limitations of claim 1.
Regarding Independent Claim 4, Applicant argues that Ke fails to disclose detecting an object, sensing entry and exit of a joint position relative to the detected object, extracting a range based upon the sensing result, periodically detecting the object when recognition fails, and identifying the task based upon changes in object coordinates. These arguments are likewise not persuasive.
As explained in the Office Action, Ke teaches object segmentation performed on each frame of a video sequence to extract target objects (p. 90), object tracking through tracking regions that are spatially coherent with locally smooth motion (p. 91), and transforming coordinates between consecutive images during tracking (p. 91). These disclosures teach repeated object detection and tracking, including determining changes in object position across successive frames.
Ke further teaches extracting localized image regions using local descriptors (p. 91) and extracting spatial-temporal cuboids surrounding detected feature points (p. 98). These extracted regions correspond to the claimed extraction of a range of video data associated with the detected object.
With respect to periodically detecting the object, Ke teaches object segmentation performed on each frame of the video sequence (p. 90) and moving object detection using temporal frame differences (p. 96). These disclosures teach repeated or periodic object detection throughout the video sequence, including circumstances in which continued detection is necessary for recognition.
Finally, Ke teaches determining activities from tracked trajectories (p. 112) and recognizing human actions from estimated poses using Hidden Markov Models (p. 107). These disclosures teach identifying a task based upon changes in object coordinates over time.
Applicant’s arguments address isolated portions of Ke but do not address the combined disclosures relied upon by the Examiner. When considered as a whole, Ke teaches each of the disputed limitations for the reasons previously set forth in the Office Action. Accordingly, the rejection of claims 1-4 under 35 U.S.C. § 102(a)(1) is maintained.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The device of claim 1 is directed to a machine, which is one of the statutory categories of invention, and passes Step 1: Statutory Category- MPEP § 2106.03. However, all of the limitations of Claim 1 constitute mental processes because they describe acts of observation, evaluation, and judgement that can practically be performed in the human mind, or by a human using pen and paper as a physical aid, therefore failing Step 2A Prong One. These acts are mental processes because a human can observe the positions of the worker’s body parts, mentally determine the worker’s motion based on those positions, focus attention on the portion of the scene where motion occurs, recognize objects present in the area, and infer the worker’s task based on the recognized object and observed motion.
Claim 1 fails Step 2A Prong Two because the additional elements beyond the judicial exception, including a memory and processor that classifies the task with a small computation amount of computationally intensive object detection processing by recognizing the object from the extracted range on the video data, do not integrate the judicial exception into a practical application. There are no improvements to the functioning of a computer or any other technology or technical field (MPEP § 2106.05(a)) as these units merely apply the abstract idea on a computer (MPEP § 2106.05(f)). Furthermore, the claim does not impose meaningful limits on the computer components such that they are tied to a particular machine; the additional elements are described at a high level of generality and can be implemented on any generic computing system (MPEP § 2106.05(b)). Claim 1 also fails Step 2B, as these additional elements are well-understood, routine, and conventional (WURC), adding nothing significantly more than the abstract idea itself (MPEP § 2106.07(a)((III)) (see MPEP § 2106.05(d)).
Regarding Claim 4, the device of claim 4 is directed to a machine, which is one of the statutory categories of invention, and passes Step 1: Statutory Category- MPEP § 2106.03. However, the all of the limitations of Claim 4 constitute mental processes because they describe acts of observation, evaluation, and judgement that can practically be performed in the human mind, or by a human using pen and paper as a physical aid, therefore failing Step 2A Prong One. These acts are mental processes because a human can observe an object in the scene, observe the worker’s body positions and movements, determine whether the worker’s hand moves into and out of the region containing the object, focus on the portion of the scene containing the object, recognize the object, and infer the task based on how the object moves during the interaction. Claim 4 also fails Step 2A Prong Two and Step 2B as the additional elements beyond the judicial exception, including a memory and processor that decreases a number of times of object detection processing by performing computationally intensive object detection processing only when the worker uses the object, do not integrate the judicial exception into a practical application and are WURC (see claim 1 analysis above).
Regarding Claims 2 and 3, all of the limitations of Claims 2 and 3 constitute mental processes because they describe acts of observation, evaluation, and judgement that can practically be performed in the human mind, or by a human using pen and paper as a physical aid, therefore failing Step 2A Prong One. For claim 2, these acts are mental processes because a human could observe multiple movements of the worker, mentally focus on different regions of the scene corresponding to each movement, identify the objects involved in those regions, and evaluate which task the worker is most likely performing based on the observed motions and objects. For claim 3, these acts are mental processes because a human could mentally store or write down rules associating body positions with particular motions, note typical locations where objects appear relative to worker movements, and maintain a simple table mapping objects to tasks performed with those objects. When observing a worker, the person could apply these stored rules or tables to infer the task being performed. Claims 2 and 3 also fail Step 2A Prong Two and Step 2B as the additional elements beyond the judicial exception, including a motion estimation unit, image extraction unit, object recognition unit, task identification unit, task estimation unit, motion storage unit, object-positional-relationship storage unit, and task storage unit, do not integrate the judicial exception into a practical application and are WURC (see claim 1 analysis above).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Ke et. al (“A Review on Video-Based Human Activity Recognition”).
Regarding Claim 1, Ke teaches a task analysis device for analyzing a task of a worker, the task analysis device comprising:
Introduction, pg. 89: “In recent years, automatic human activity recognition has drawn much attention in the field of video analysis technology due to the growing demands from many applications, such as surveillance environments, entertainment environments and healthcare systems.”
Introduction, pg. 90: “Subsequently, an activity detection or classification algorithm is applied on the extracted features to recognize the various human activities.”
Explanation: The reference describes systems that analyze human activities from video, which corresponds to analyzing a worker performing tasks.
a memory that stores a program;
Abstract: “In the core technology, three critical processing stages are thoroughly discussed mainly: human object segmentation, feature extraction and representation, activity detection and classification algorithms.”
Introduction, pg. 89: “In recent years, automatic human activity recognition has drawn much attention in the field of video analysis technology due to the growing demands from many applications, such as surveillance environments, entertainment environments and healthcare systems.”
Explanation: A person of ordinary skill in the art would understand that the disclosed activity recognition system is implemented by executable software algorithms (segmentation, feature extraction, activity classification) stored in computer memory for execution by a processor. Thus, Ke teaches a memory storing program instructions for performing the disclosed processing.
and a processor that executes the program and controls the task analysis device to:
Abstract: “In the core technology, three critical processing stages are thoroughly discussed mainly: human object segmentation, feature extraction and representation, activity detection and classification algorithms.”
Introduction, pg. 90: “Subsequently, an activity detection or classification algorithm is applied on the extracted features to recognize the various human activities.”
Explanation: Execution of segmentation, feature extraction, region extraction, object recognition, and classification algorithms necessarily require one or more processors executing stored instructions. Thus, Ke teaches the claimed processor executing the stored program and controlling the activity recognition process.
estimate joint position information pertaining to the worker from video data including the task of the worker (Fig. 9 (shown below));
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Introduction, pg. 92: “Generally, the body modeling requires the 2D/3D pose estimation problem. Usually, after the pose estimation, the 2D/3D coordinates of the human body are further converted into other dimension-reduced or more discriminative feature representations, such as polar coordinate representation [71], Boolean features [72] and geometric relational features (GRF) [73,74].”
Explanation: These disclosures estimate joint/body part coordinates, corresponding to joint-position estimation.
estimate motion information pertaining to the worker on a basis of the estimated joint position information;
Introduction, pg. 90: “The characteristics of the human object such as shape, silhouette, colors, poses, and body motions are then properly extracted and represented by a set of features.”
5.1.1. Trajectory, pg. 112: “The trajectory of a tracked person in a scene is often used to analyze the activity or behavior of the tracked person.”
2.2.2. Optical Flow, pg. 97: “In [49], Daniilidis et al. apply an FIR-kernel based LKT feature tracker to estimate the optical flow and to infer the motion of objects.”
Explanation: The reference teaches estimating motion of people in video. Motion estimation from tracked body positions corresponds to the claimed motion estimation unit.
extract, from the video data on a basis of the estimated motion information, a range on the video data that pertains to an object associated with the motion information;
3.1. Space-Time Volumes (STV), pg. 98: “The space-time volume (STV) is formed by temporally stacking frames over a video sequence as a 3D cuboid of spatial-temporal shape… Ke et al. [3] further uses the spatial-temporal shapes for shaped-based matching, including spatial-temporal region extraction and region matching.”
Explanation: The reference teaches extracting spatial-temporal regions from video associated with motion/activity. These disclosures correspond to extracting video regions (ranges) associated with motion.
recognize the object within the range on the video data that has been extracted;
Introduction, pg. 90: “The human object is first segmented out from the video sequence. The characteristics of the human object such as shape, silhouette, colors, poses, and body motions are then properly extracted and represented by a set of features. Subsequently, an activity detection or classification algorithm is applied on the extracted features to recognize the various human activities.”
Explanation: These disclosures correspond to recognizing objects or activities within extracted video regions.
identify the task of the worker on a basis of the recognized object recognized;
Introduction, pg. 90: “Subsequently, an activity detection or classification algorithm is applied on the extracted features to recognize the various human activities.”
4.4.3. Multidimensional Indexing, pg. 111: “The activity is then recognized by indexing and sequencing a few pose vectors in the multidimensional harsh table.”
5.1.1. Trajectory, pg. 112: “The trajectory of a tracked person in a scene is often used to analyze the activity or behavior of the tracked person.”
Explanation: These disclosures identify a human activity/task based on recognized objects or motion features.
wherein the processor classifies the task with a small computation amount of computationally intensive object detection processing by recognizing the object from the extracted range on the video data.
Introduction, pg. 90: “The human object is first segmented out from the video sequence. The characteristics of the human object such as shape, silhouette, colors, poses, and body motions are then properly extracted and represented by a set of features. Subsequently, an activity detection or classification algorithm is applied on the extracted features to recognize the various human activities.”
3.1. Space-Time Volumes (STV), pg. 98: “Ke et al. [3] further uses the spatial-temporal shapes for shaped-based matching, including spatial-temporal region extraction and region matching…Dollar et al. [11] applies a spatio-temporal interest point detector to find local region of interest in the cuboids of space and time for activity recognition.”
Explanation: Ke first segments the human object, estimates motion, and extracts localized spatial-temporal regions corresponding to the motion before applying activity classification. By restricting recognition to extracted spatial-temporal regions (regions of interest) instead of repeatedly performing object detection across an entire video frame, Ke necessarily reduces the amount of computationally intensive object detection processing required during classification. Thus, Ke teaches classifying the activity using a reduced amount of object detection processing by recognizing the object within the extracted region.
Regarding Claim 2, Ke teaches the task analysis device according to claim 1,
wherein in a case where the processor estimates motion information pertaining to the worker that includes a plurality of motions on a basis of the joint position information,
5.1.1. Trajectory, pg. 112: “The trajectory of a tracked person in a scene is often used to analyze the activity or behavior of the tracked person.”
Explanation: Different trajectories correspond to multiple motions.
the processor extracts a plurality of ranges on the video data for each of the plurality of motions estimated,
3.1. Space-Time Volumes (STV), pg. 98: “The space-time volume (STV) is formed by temporally stacking frames over a video sequence as a 3D cuboid of spatial-temporal shape.”
Explanation: Each cuboid corresponds to a different motion region extracted from video.
the processor recognizes the object for each of the plurality of ranges on the video data,
Introduction, pg. 90: “Subsequently, an activity detection or classification algorithm is applied on the extracted features to recognize the various human activities.”
Explanation: Recognition occurs for multiple extracted feature regions.
the processor estimates a task having a highest likelihood on a basis of a likelihood of each of the estimated plurality of motions and a likelihood of the object recognized for each of the plurality of ranges on the video data.
4.4.4. K-Nearest Neighbor (K-NN), pg. 111: “The K-nearest neighbor (K-NN) [22] algorithm is a classification method based on the K, a predefined constant, closest training data in the feature space. A point/vector is classified to one label, which is the most frequent label among K nearest training points/vectors.”
Explanation: This classification corresponds to selecting the most likely activity/task based on motion features.
Regarding Claim 3, Ke teaches the task analysis device according to claim 1, wherein:
the memory stores a rule base or a trained model for outputting motion information pertaining to the worker that corresponds to the estimated joint position information;
4.2.1. Hidden Markov Model (HMM), pg. 107: “Furthermore, Natarajan and Nevatia [17] introduce a Hierarchical Variable Transition Hidden Markov Model (HVT-HMM) to simultaneously track and recognize articulated full-body human motion.”
the memory stores, in advance on a basis of the motion information pertaining to the worker, a range on the video data that includes the object associated with the motion information;
3.1. Space-Time Volumes (STV), pg. 98: “The space-time volume (STV) is formed by temporally stacking frames over a video sequence as a 3D cuboid of spatial-temporal shape… Ke et al. [3] further uses the spatial-temporal shapes for shaped-based matching, including spatial-temporal region extraction and region matching.”
Explanation: These disclosures correspond to storing spatial-temporal ranges associated with motion, which represent positional relationships between objects and human motion.
the memory stores a task table in which the recognized object is mapped to the task of the worker in advance.
Introduction, pg. 90: “Subsequently, an activity detection or classification algorithm is applied on the extracted features to recognize the various human activities.”
5.1.1. Trajectory, pg. 112: “The trajectory of a tracked person in a scene is often used to analyze the activity or behavior of the tracked person.”
Explanation: These disclosures correspond to mapping recognized features/objects to an activity or task classification, which functions as the claimed task table mapping objects to worker tasks.
Regarding Claim 4, Ke teaches a task analysis device for analyzing a task of a worker, the task analysis device comprising (see claim 1 above):
a memory that stores a program (see claim 1 above);
and a processor that executes the program and controls the task analysis device to (see claim 1 above):
detect an object from video data including the task of the worker;
Introduction, pg. 90: “The human object is first segmented out from the video sequence…In the first stage of the core technology, the object segmentation is performed on each frame in the video sequence to extract the target object.”
estimate joint position information pertaining to the worker from the video data (see claim 1 above);
sense, on a basis of the estimated joint position information, whether an image region including a joint position of the worker has entered and then exited from an image region including the detected object;
Introduction, pg. 91: “In addition, static camera segmentation by tracking [45,46] has also been proposed, i.e., unlike the point-based segmentation methods to form a background model in advance, based on a dynamic time warping like algorithm, the object can also be segmented by tracking regions which are spatially cohesive with locally smooth motion…the features of images are then tracked and the coordinate between consecutive images is also transformed.”
Explanation: Tracking the motion of body parts or objects across frames detects entry and exit of regions across time.
extract, from the video data on a basis of a result of sensing, a range on the video data that pertains to the detected object detected;
Introduction, pg. 91: “The local descriptors [5,8,12,13,14,52,53,54,55,56], such as scale-invariant feature transform (SIFT) [57,58] and histogram of oriented gradient (HOG) [59] capture the characteristics of an image patch.”
3.1. Space-Time Volumes (STV), pg. 98: “First, cuboids of spatio-temporally windowed data surrounding a feature point extracted from sample behaviors are clustered to form a dictionary of cuboid prototypes.”
Explanation: These disclosures describe extracting specific regions or segments of video data associated with detected objects.
perform object recognition for the range on the video data that has been extracted by the image extraction unit;
Introduction, pg. 90: “Subsequently, an activity detection or classification algorithm is applied on the extracted features to recognize the various human activities.”
3.1. Space-Time Volumes (STV), pg. 98: “The histogram of the cuboid types is then used as an activity descriptor for object recognition.”
Explanation: This corresponds to performing recognition on extracted video segments.
periodically detect the object in a case where the object is unable to be recognized within the range on the video data;
Introduction, pg. 90: “In the first stage of the core technology, the object segmentation is performed on each frame in the video sequence to extract the target object.”
2.2.1. Temporal Difference, pg. 96: “Instead, the moving object is detected by taking the difference of consecutive image frames t-1 and t.”
Explanation: These teachings describe periodic detection of objects from video frames.
identify the task on a basis of a change in a coordinate of the object detected in the video data;
5.1.1. Trajectory, pg. 112: “The trajectory of a tracked person in a scene is often used to analyze the activity or behavior of the tracked person.”
5.1.3. Human Pose Estimation: “Thuc et al. [73] further convert the estimated 3D poses into a feature space and the human actions can be recognized by using HMMs.”
Explanation: These disclosures identify activities/tasks based on motion or coordinate changes of tracked objects.
wherein the processor decreases a number of times of object detection processing by performing computationally intensive object detection processing only when the worker uses the object.
Introduction, pg. 90: “The human object is first segmented out from the video sequence. The characteristics of the human object such as shape, silhouette, colors, poses, and body motions are then properly extracted and represented by a set of features. Subsequently, an activity detection or classification algorithm is applied on the extracted features to recognize the various human activities.”
Introduction, pg. 91: “In addition, static camera segmentation by tracking [45,46] has also been proposed, i.e., unlike the point-based segmentation methods to form a background model in advance, based on a dynamic time warping like algorithm, the object can also be segmented by tracking regions which are spatially cohesive with locally smooth motion…the features of images are then tracked and the coordinate between consecutive images is also transformed.”
3.1. Space-Time Volumes (STV), pg. 98: “Ke et al. [3] further uses the spatial-temporal shapes for shaped-based matching, including spatial-temporal region extraction and region matching.”
Explanation: After the initial segmentation, Ke relies on tracking, spatial-temporal region extraction, and activity recognition within extracted regions rather than repeatedly performing full object detection over the entire image. Tracking allows previously detected objects to be followed across frames, thereby reducing repeated object detection operations. Consequently, computationally intensive object detection is performed only when necessary to establish or re-establish the object for continued recognition, corresponding to decreasing the number of object detection operations.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yan-jun (CN113393489A) teaches a computer-implemented method, where the method includes: extracting each frame of a video sequence at a single frame rate, the video sequence including a series of video frames; estimating the current value of an object in each video frame pose and determining the joint positions of the joints associated with the objects in each video frame; calculating the optical flow between pairs of consecutive video frames for each time step of the video sequence; extracting motion features from each video frame of the video sequence based on the optical flow; and based on the current posture and motion characteristics to encode the state information. It would also render a 102 rejection for claims 1-4.
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 WILLIAM ADU-JAMFI whose telephone number is (571)272-9298. The examiner can normally be reached M-T 8:00-6:00.
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/WILLIAM ADU-JAMFI/Examiner, Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677
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