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 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) a method for generating training data. This judicial exception is not integrated into a practical application because the judicial exception does not contain sufficient additional elements to amount to more than the abstract idea of data processing.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements amount to extra solution activity. The claims recite the “generating” being based on a first and second signal which is “acquired”. Such limitations are insufficient to tie the judicial exception to significantly more as this is merely a step of data gathering, essentially a data input, for the method step.
Claims 2-6 provide additional processing steps and limitations on the type of signal from which the input data is acquired, which taken alone or in combination, are insufficient to amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 102
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 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)(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.
Claim(s) 1-18 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Cinnamon (US 2020/0193666) which is a continuation of application No.15714932 filed 09/25/2017.
Claim 1: Cinnamon discloses A processing method comprising:
generating a displayed information for labeling, based on a first signal and a second signal, the second signal being based on a reflection wave acquired by irradiating an electromagnetic wave, the first signal being different from the second signal (para 0005, 0033, 0034, 0040, 0044-0048, 0064, 0078, 0148, 0149, 0236, 0237. These passages, amongst others, disclose the acquisition of position information, segmentation for classification of objects, display generation including positional information and labeling of objects)
acquiring a label data, based on the displayed information for labeling (fig 7, para 0064, 0067, 0078, 0104, 0143, 0234 disclosing the type of data stored in the classification engine including class labels and displaying textual indications of the class labels); and
generating training data including the label data and being associated with the second signal (para 0040-0058 disclosing the use of a neural network to implement bounding, segmentation, and classification as well as a training phase implemented using measurements by the system of real objects for creating learned features for classification)
Claim 2: Cinnamon discloses the label data comprises a name of an object and position of the object (para 0005, 0033, 0034, 0040, 0044-0048, 0064, 0078, 0148, 0149, 0236, 0237. These passages, amongst others, disclose the acquisition of position information, segmentation for classification of objects, display generation including positional information and labeling of objects)
Claim 3: Cinnamon discloses the reflection wave is reflected by an object (para 0033-0035, 0105 disclosing the use of mmWave scanners amongst others which are known in the art to inherently transmit and receive reflected signals for object detection)
Claim 4: Cinnamon discloses the second signal is generated based on the reflection wave (para 0033-0035, 0105 disclosing the use of mmWave scanners amongst others which are known in the art to inherently transmit and receive reflected signals for object detection)
Claim 5: Cinnamon discloses the electromagnetic wave having a wavelength of equal to or more than 30 micrometers and equal to or less than one meter (para 0033-0035, 0105 disclosing the use of mmWave scanners amongst others which are known in the art to inherently transmit and receive reflected signals for object detection)
Claim 6: Cinnamon discloses generating, by training the training data, an estimation model to estimate an object (para 0040-0058 disclosing the use of a neural network to implement bounding, segmentation, and classification as well as a training phase implemented using measurements by the system of real objects for creating learned features for classification)
Claim 7: Cinnamon discloses A processing apparatus comprising: at least one memory configured to store one or more instructions; and at least one processor configured to execute the one or more instructions (para 0078, 0187) to:
generate a displayed information for labeling, based on a first signal and a second signal, the second signal being based on a reflection wave acquired by irradiating an electromagnetic wave, the first signal being different from the second signal (para 0005, 0033, 0034, 0040, 0044-0048, 0064, 0078, 0148, 0149, 0236, 0237. These passages, amongst others, disclose the acquisition of position information, segmentation for classification of objects, display generation including positional information and labeling of objects)
acquire a label data, based on the displayed information for labeling (fig 7, para 0064, 0067, 0078, 0104, 0143, 0234 disclosing the type of data stored in the classification engine including class labels and displaying textual indications of the class labels); and
generate training data including the label data and being associated with the second signal (para 0040-0058 disclosing the use of a neural network to implement bounding, segmentation, and classification as well as a training phase implemented using measurements by the system of real objects for creating learned features for classification)
Claim 8: Cinnamon discloses the label data comprises a name of an object and position of the object (para 0005, 0033, 0034, 0040, 0044-0048, 0064, 0078, 0148, 0149, 0236, 0237. These passages, amongst others, disclose the acquisition of position information, segmentation for classification of objects, display generation including positional information and labeling of objects)
Claim 9: Cinnamon discloses the reflection wave is reflected by an object (para 0033-0035, 0105 disclosing the use of mmWave scanners amongst others which are known in the art to inherently transmit and receive reflected signals for object detection)
Claim 10: Cinnamon discloses the second signal is generated based on the reflection wave (para 0033-0035, 0105 disclosing the use of mmWave scanners amongst others which are known in the art to inherently transmit and receive reflected signals for object detection)
Claim 11: Cinnamon discloses the electromagnetic wave having a wavelength of equal to or more than 30 micrometers and equal to or less than one meter (para 0033-0035, 0105 disclosing the use of mmWave scanners amongst others which are known in the art to inherently transmit and receive reflected signals for object detection)
Claim 12: Cinnamon discloses the at least one processor is further configured to execute the one or more instructions to generate, by training the training data, an estimation model to estimate an object (para 0040-0058 disclosing the use of a neural network to implement bounding, segmentation, and classification as well as a training phase implemented using measurements by the system of real objects for creating learned features for classification)
Claim 13: Cinnamon discloses A non-transitory storage medium storing a program (para 0078, 0187) causing a computer to:
generate a displayed information for labeling, based on a first signal and a second signal, the second signal being based on a reflection wave acquired by irradiating an electromagnetic wave, the first signal being different from the second signal (para 0005, 0033, 0034, 0040, 0044-0048, 0064, 0078, 0148, 0149, 0236, 0237. These passages, amongst others, disclose the acquisition of position information, segmentation for classification of objects, display generation including positional information and labeling of objects)
acquire a label data, based on the displayed information for labeling (fig 7, para 0064, 0067, 0078, 0104, 0143, 0234 disclosing the type of data stored in the classification engine including class labels and displaying textual indications of the class labels); and
generate training data including the label data and being associated with the second signal (para 0040-0058 disclosing the use of a neural network to implement bounding, segmentation, and classification as well as a training phase implemented using measurements by the system of real objects for creating learned features for classification)
Claim 14: Cinnamon discloses the label data comprises a name of an object and position of the object (para 0005, 0033, 0034, 0040, 0044-0048, 0064, 0078, 0148, 0149, 0236, 0237. These passages, amongst others, disclose the acquisition of position information, segmentation for classification of objects, display generation including positional information and labeling of objects)
Claim 15: Cinnamon discloses the reflection wave is reflected by an object (para 0033-0035, 0105 disclosing the use of mmWave scanners amongst others which are known in the art to inherently transmit and receive reflected signals for object detection)
Claim 16: Cinnamon discloses the second signal is generated based on the reflection wave (para 0033-0035, 0105 disclosing the use of mmWave scanners amongst others which are known in the art to inherently transmit and receive reflected signals for object detection)
Claim 17: Cinnamon discloses the electromagnetic wave having a wavelength of equal to or more than 30 micrometers and equal to or less than one meter (para 0033, 0105 disclosing the use of mmWave scanners amongst others which are known in the art to inherently transmit and receive reflected signals for object detection)
Claim 18: Cinnamon discloses the program causing the computer to generate, by training the training data, an estimation model to estimate an object (para 0040-0058 disclosing the use of a neural network to implement bounding, segmentation, and classification as well as a training phase implemented using measurements by the system of real objects for creating learned features for classification)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The additionally cited prior art comprises the current state of the art in object detection and classification based on trained models, specifically with regards to contraband detection.
Fleisher (US 2013/0121529), Tzadok (US 2021/0063559), Childs (US 2019/0195989) disclose embodiments of contraband screening devices using RF transmission and reception, where objects are detected and classified by training machine learning systems.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PETER M BYTHROW whose telephone number is (571)270-1468. The examiner can normally be reached on Monday-Friday 830am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Resha Desai can be reached at (571) 270-7792. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PETER M BYTHROW/Primary Examiner, Art Unit 3648
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