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 .
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 therefore, subject to the conditions and requirements of this title.
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.
Claims 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an
abstract idea without significantly more. The claim recites determining information on labeling of the labeling target sensor data with reference to a behavior of the object estimated from at least one of the labeling target sensor data and first reference data that corresponds to the labeling target sensor data and is of a type different from the labeling target sensor data. The limitation of specifying, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, specifying in the context of this claim encompasses the user manually determining if the object’s behavior such as walking is something that has been previously seen or not as "machine learning" is cited at a high level of generality. Similarly, the limitation of determining, as drafted, is a process that, under its broadest
reasonable interpretation, covers performance of the limitation in the mind but for the recitation
of a generic computer process. For example, but determining in the context of this claim
encompasses the user making a determination based on a frequency readout. The gathering of
the frequency data is not described and is therefore interpreted as insignificant pre-solution
activity. The "providing information" is also not described in detail and is interpreted as
significant post-solution activity. If a claim limitation, under its broadest reasonable
interpretation, covers performance of the limitation in the mind but for the recitation of generic
computer components, then it falls within the "Mental Processes" grouping of abstract ideas.
Accordingly, the claims recites an abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claim(s) 1-13 are rejected under 35 U.S.C. 103 as being unpatentable over Mazumder et al (US 2023/0052903) in view of Dalton et al. (US 20180018588 A1)
As to claim 1, Mazumder et al teaches a method for supporting labeling of sensor data, the method comprising the steps of:
acquiring labeling target sensor data measured by a sensor for an object(collect user behavior data, from the one or more sensors, of a first user during a current time interval in relation to a context of a surrounding environment, the collected user behavior data enriched with associated information; group the user behavior data by labels, each of the grouped user behavior data labeled with a corresponding task classification and the grouped user behavior data training a first machine learning model; paragraph [0013]);
and determining information on labeling of the labeling target sensor data with reference to a behavior of the object estimated from at least one of the labeling target sensor data (the collected user behavior data enriched with associated information; grouping the user behavior data by labels, each of the grouped user behavior data labeled with a corresponding task classification and the grouped user behavior data training a first machine learning model; proactively predicting an expected user behavior data during a future time interval by applying the trained first machine learning model to the collected user behavior data, and recommending a task to the first user based on the expected user behavior and a threshold associated with each task classification; obtaining feedback from the first user and continuously learning patterns in the collected user behavior data to refine the trained first machine learning model based on the feedback and changes to the user behavior data; and storing the trained first machine learning model into a knowledge base for continued and multi-task learning, paragraph [0014]). While Mazumder teaches the limitation above, Mazumder fails to teach” first reference data that corresponds to the labeling target sensor data and is of a type different from the labeling target sensor data. “
Dalton teaches the step of monitoring for unknown gestures or unknown behaviors, and automatically adding a definition of the unknown gestures or unknown behaviors to the respective set of gesture definitions or set of behavior definitions; a user may be alerted to classify unknown gestures or unknown behaviors; the method may further comprise the steps of monitoring for unclassified gestures and unclassified behaviors based on repeated patterns, and adding the unclassified gestures and unclassified behaviors to the respective set of gesture definitions and set of behavior definitions; the method may further comprise the step of comparing at least the operational data to one or more thresholds, and triggering an alarm if one or more thresholds have been exceeded; analyzing the operational data to identify data patterns may comprise comparing data values from the sensors to values in the definitions, convolving signals representative of the operational data processing signals representative of the operational data, using machine learning techniques to segment the operational data, or combinations thereof; the signals may be processed to obtain spatial information, frequency information, time domain information (paragraph [0045]).Dalton clearly teaches data values from the sensors, [0045], Sensors may include one or more: RFID reader that communicates with RFID tagged objects or locations, digital camera, GPS, weight or load sensor, CAN bus, etc., [0059]), and the use of machine learning and other techniques to automatically detect and elucidate the structure of actions of entities and/or associated entities in the system. This applies not only to the actions, but the relations between the actions.(paragraph [040])) It would have been obvious to one skilled in the art before filing of the claimed invention to use different label data in order to perceives its environment, learns, and adapts, changing its models to accurately anticipate the future in real or near-real time, and allowing for optimizing of organizational behavior accordingly. Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
As to claim 2, Dalton et al teaches the method of Claim 1, wherein in the acquiring step, the behavior of the object is estimated from at least one of sensor data measured by the sensor (data values from the sensors, [0045], Sensors may include one or more: RFID reader that communicates with RFID tagged objects or locations, digital camera, GPS, weight or load sensor, CAN bus, etc., [0059]),
and second reference data that corresponds to the sensor data and is of a type different from the sensor data, and the sensor data is acquired as the labeling target sensor data when the estimated behavior of the object is valid (Use of machine learning and other techniques to automatically detect and elucidate the structure of actions of entities and/or associated entities in the system. This applies not only to the actions, but the relations between the actions, [0040], monitoring for unknown gestures or unknown behaviors, and automatically adding a definition of the unknown gestures or unknown behaviors to the respective set of gesture definitions or set of behavior definitions; a user may be alerted to classify unknown gestures or unknown behaviors, using machine learning techniques to segment the operational data, or combinations thereof; the signals may be processed to obtain spatial information, frequency information, time domain information, or combinations thereof from the processed signals; data patterns may comprise first order data structures, gestures comprise second order data structures, and behaviors comprise third order data structures, [0045]).
As to claim 3, Mazumder et al teaches the method of Claim 1, wherein in the determining step, the behavior of the object is estimated using a machine learning-based behavior estimation model trained on the basis of sensor data measured by the sensor and second reference data that corresponds to the sensor data and is of a type different from the sensor data (The predicting process may employ, for example, a predictive model trained using machine learning to predict subsequent user behaviors based, at least in part, on current user behaviors. In one embodiment, the computing device 134 of the user can proactively send notifications and/or recommendations to users. The notification and recommendation can include, for example, a text, visual and/or audio notification, paragraph [0076][0084]).
As to claim 4, Mazumder et al teaches the method of Claim 1, wherein in the determining step, when the behavior of the object is not estimated from the first reference data, the information on the labeling of the labeling target sensor data is determined with reference to the behavior of the object estimated from the labeling target sensor data (The grouped user behavior data trains a first machine learning model. Expected user behavior data is then proactively predicted, at step 806, during a future time interval by applying the trained first machine learning model to the collected user behavior data. A task is recommended to the first user based on the expected user behavior and a threshold associated with each task classification. Changes to the user behavior data can be made at step 810 after obtaining feedback from the user and continuously learning patterns in the collected user behavior data to refine the trained machine learning model. At step 812, the trained machine learning model is stored into a knowledge base for continued and multi-task learning, paragraph [0100]).
As to claim 5, Mazumder et al teaches the method of Claim 4, wherein the first reference data includes video data for the object, and in the determining step, the behavior of the object is not estimated as the object is obscured by another object or the object changes its posture If the level of confidence is not satisfied, the model may require retraining to prevent false positive outcomes. In this case, the prediction 206c may use a previously observed label to provide a recommendation or may not provide any recommendation at all. In the case where the level of confidence is satisfied, the recommendation based on prediction 206c may be output to the user. In either case, the confidence may be measured against the threshold 206b. The threshold 206b, as explained below, may continuously change (i.e., adapt) as it learns the behavior of users, paragraph [0078][0100]).
As to claim 6, Mazumder et al teaches the method of Claim 4, further comprising the step of: when the behavior of the object is not estimated from the first reference data, providing the determined information on the labeling to a user to ensure the user is capable of labeling the labeling target sensor data (If the level of confidence is not satisfied, the model may require retraining to prevent false positive outcomes. In this case, the prediction 206c may use a previously observed label to provide a recommendation or may not provide any recommendation at all. In the case where the level of confidence is satisfied, the recommendation based on prediction 206c may be output to the user. In either case, the confidence may be measured against the threshold 206b. The threshold 206b, as explained below, may continuously change (i.e., adapt) as it learns the behavior of users, paragraph [0078]).
The limitation of claims 7-13 has been addressed above.
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NANCY . BITAR
Examiner
Art Unit 2664
/NANCY BITAR/Primary Examiner, Art Unit 2664
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