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 .
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 29-33 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because in the claims, the subject matter of " One or more computer storage media " while embodying functional descriptive material, is a sequence of instructions merely capable of being executed by a computer machine to realize its functionality, it is not a process, nor a device, cannot be embodied in a machine without a non-transitory computer-readable medium. Therefore it is suggested an amendment to the claims to recite, “One or more non- transitory computer storage media " to overcome the rejection.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 21, 27, 29, 34 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The limitations of " second sensor data encodes ground truth data ", " second sensor data does not encode ground truth data " in claims 21, 29, 34; " forth sensor data encodes ground truth data ", " forth sensor data does not encode ground truth data " in claim 27, as essential components in the invention either have no adequate support or description in the disclosure including specification and drawings, and enablement for the claimed subject matters, or after applying the broadest reasonable interpretation to the claim, the metes and bounds of the claimed invention still is clear and not indefinite. Nevertheless, the limitation of sensor data does or doesn’t encode ground truth indicates the inconsistence with the well known compression theory, in that a device/apparatus performs function of encoding on some kind of input data, herein the sensor data would not be a device/apparatus to perform encoding/compression.
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.
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 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.
Claim(s) 21-27, 29-33 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210271934 A1 White; Brian et al. (hereafter White), and further in view of US 20200210726 A1 Yang; Yilin et al. (hereafter Yang).
Regarding claim 21, White discloses A computer-implemented method (Fig.13), comprising: by one or more computing devices ([06]): determining, using first sensor data captured by one or more first sensors of a monitoring system as input to an event prediction model (Fig.3, [05]), a prediction result that indicates whether a predicted event involving an object will likely occur ([05], a wildfire is the object will likely occur); accessing second sensor data captured by one or more second sensors of the monitoring system after the capture of the first sensor data ([05], different sensors obtain associated data at different time).
White fails to disclose determining, using the second sensor data, whether the second sensor data encodes ground truth data that indicates implicit proof of an occurrence of the predicted event; in response to determining that the second sensor data does not encode ground truth data that indicates implicit proof of an occurrence of the predicted event, determining a discrepancy result that represents a discrepancy in the event prediction model determination of the first sensor data; and causing an update to the event prediction model using the discrepancy result.
However, Yang teaches determining, using the second sensor data, whether the second sensor data encodes ground truth data that indicates implicit proof of an occurrence of the predicted event ([07]-[08], [45], [52], among various sensors Lidar or Radar could be the second sensor used for encoding, ground truth represents the accurate prediction that is the proof of an occurrence of the predicted event); in response to determining that the second sensor data does not encode ground truth data that indicates implicit proof of an occurrence of the predicted event (Fig.1, [42], sensor data inputs into machine learning model 104 means the sensor data is not encoded), determining a discrepancy result that represents a discrepancy in the event prediction model determination of the first sensor data (Fig.1, [41], [88], images from camera input to machine learning model is the first sensor data and the error/loss in the predictions of the ML model is the discrepancy); and causing an update to the event prediction model using the discrepancy result ([59]).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the computer-implemented method disclosed by White to include the teaching in the same field of endeavor of Yang, in order to provide technologies that accurately and robustly predict distances to objects or obstacles in an environment using a deep neural network, as identified by Yang.
Regarding claims 22, 30, White discloses The computer-implemented method of claim 21, wherein causing the update comprises: adjusting, using the discrepancy result, at least one parameter of the one or more models ([31]).
Regarding claims 23, 31, White discloses The computer-implemented method of claim 22, comprising determining, by the one or more computing devices, a new prediction result on whether to trigger a second event by executing the one or more models with the adjusted parameters using new first sensor data ([145]-[146]).
Regarding claims 24, 32, White discloses The computer-implemented method of claim 21, comprising communicating a message about the predicted event to a device associated with a target area in which the predicted event was predicted to occur ([33]).
Regarding claim 25, White discloses The computer-implemented method of claim 21, wherein: the one or more first sensors of the monitoring system comprise at least one of a camera and a motion detector, and the one or more second sensors comprise at least one of a camera, a motion detector, a doormat, a button, an audio sensor, a glass break sensor, a pressure sensor, a distance sensor, a door open sensor, a doorbell, or a passive infrared (PIR) sensor ([35]).
Regarding claims 26, 33, White discloses The computer-implemented method of claim 21, wherein determining the prediction result comprises: determining, using the first sensor data, whether object data is present in the first sensor data and whether the object data satisfies a similarity threshold with a known object data; and in response to determining that the object data satisfies the similarity threshold, determining that the object is present in a target area in which the predicted event was predicted to occur, determining to trigger the event, or both ([146]-[151]).
Regarding claim 27, Yang teaches The computer-implemented method of claim 21, comprising: determining, using third sensor data captured by one or more third sensors of the monitoring system as input to the event prediction model, a second prediction result that indicates whether a second predicted event involving the object will likely occur; accessing fourth sensor data captured by one or more fourth sensors of the monitoring system after the capture of the third sensor data; determining, using the fourth sensor data, whether the fourth sensor data encodes ground truth data that indicates implicit proof of an occurrence of the second predicted event; in response to determining that the fourth sensor data does not encode ground truth data that indicates implicit proof of the occurrence of the second predicted event and that the second prediction result indicates that the second predicted event involving the object will not likely occur, determining to skip updating to the event prediction model using a second discrepancy result for the second predicted event (Fig.1, [39]-[40]).
Regarding claim 29, see the rejection for claim 21.
Claim(s) 28, 34, 35 is/are rejected under 35 U.S.C. 103 as being unpatentable over White, in view of Yang, and further in view of US 20190141383 A1 Kageyama; Tsuyoshi et al. (hereafter Kageyama).
Regarding claim 28, Kageyama teaches The computer-implemented method of claim 27, comprising: accessing first timestamp data for the predicted event and second timestamp data for the second event, determining, using the first timestamp data and the second timestamp data, whether a difference between the first timestamp data and the second timestamp data satisfies a timing threshold; and in response to determining that the difference does not satisfy the timing threshold, determining to not trigger the event ( [137]).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention having all the references White, Yang and Kageyama before him/her, to modify the computer-implemented method disclosed by White to include the teaching in the same field of endeavor of Yang, in order to provide technologies that accurately and robustly predict distances to objects or obstacles in an environment using a deep neural network, as identified by Yang and Kageyama , and a system and method for determining when to refresh sports statistic data used to generate simulated sports events, as identified by Kageyama.
Regarding claim 34, see the rejection for 21. White further discloses determining, using a first timestamp of the first sensor data and a second timestamp of the second sensor data, a timeline of actions indicating a sequence of events represented in the first sensor data and the second sensor data ([06]); Kageyama teaches determining, using the timeline of events, whether a difference between the first timestamp and the second timestamp satisfies a timing criteria ([15], [137]).
Regarding claim 35, White discloses The system of claim 34, wherein causing an update to the one or more models using the discrepancy result comprises: adjusting at least one parameter of the event prediction model using the discrepancy result, the difference, or both ([31]).
Allowable Subject Matter
Claims 36-40 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: WO 2022096127 A1, US 10414395 B1, US 20210263525 A1
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TRACY Y. LI whose telephone number is (571)270-3671. The examiner can normally be reached Monday Friday (8:30 AM- 4:30 PM) EST.
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/TRACY Y. LI/Primary Examiner, Art Unit 2487
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