DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
2. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
3. The information disclosure statement filed August 28, 2025 fails to comply with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 because the US publication numbers for the disclosed applications do not correspond to the cited authors and do not appear to be relevant to the instant application. It has been placed in the application file, but the information referred to therein has not been considered as to the merits. Applicant is advised that the date of any re-submission of any item of information contained in this information disclosure statement or the submission of any missing element(s) will be the date of submission for purposes of determining compliance with the requirements based on the time of filing the statement, including all certification requirements for statements under 37 CFR 1.97(e). See MPEP § 609.05(a).
Claim Status
4. Claims 1-11 are pending in this application. Claims 3-7 were amended by preliminary amendment and claims 8-11 are new.
Specification
5. The specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification. MPEP § 608.01.
Claim Objections
6. Applicant is advised that should claims 3 and 7 be found allowable, claims 4 and 8 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
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)(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.
7. Claims 1-11 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hamaguchi, et al., JP 2002312762 (hereinafter Hamaguchi).
8. Regarding claim 1,
Hamaguchi discloses:
A method for discriminating a sorting target (rice 1: fig. 1),Hamaguchi discloses this method in [0001].
the method comprising: transferring the sorting target;Hamaguchi discloses rice moving through a chute in [0019].
imaging the sorting target during the transferring;Hamaguchi discloses photographing the rice in [0019].
and discriminating the sorting target based on imaging information acquired in the imaging, wherein the imaging information includes adjacency information between sorting targets, and classification information of the sorting target.Hamaguchi discloses discriminating the rice in [0005] and [0020] based on the image data, adjacency information based on imaging in [0027], and classification of the sorting target in [0006] and [0056].
9. Regarding claim 2,
Hamaguchi discloses the limitations of claim 1 and also:
wherein the imaging information further includes background information of the sorting target.Hamaguchi discloses background information from the image information in [0025].
10. Regarding claim 6,
Hamaguchi discloses the limitations of claim 2 and also:
A method for sorting a sorting target, the method comprising: the method for discriminating the sorting target according to claim 2; and sorting the sorting target based on discrimination information acquired in the discriminating.Hamaguchi discloses in [0005] that its neural network method performs sorting and discrimination on its rice grain sorting targets.
11. Regarding claim 3,
Hamaguchi discloses the limitations of claim 1 and also:
wherein the discriminating is performed using an inference model generated based on learning information regarding the sorting target, and the learning information includes the adjacency information between the sorting targets and the classification information of the sorting target, and the discriminating includes discriminating the sorting target using the imaging information and the inference model.Hamaguchi implicitly discloses unnumbered inference models as part of methods NN1 and NN2 along with learning methods in [0008]. A person of ordinary skill in the art will understand that neural network methods such as NN1 and NN2 necessarily incorporate inference models that are based on learned training data; all neural net classification systems that discriminate inputs into classes incorporate such models based on such data. The adjacency information as part of image data was disclosed in [0027], the classification information in [0006] and [0056] (of course classification is an essential and inherent feature of most neural network systems). Neural net methods NN1 and NN2 take the image data as inputs so the discrimination of rice grains occurs with respect to the models and the adjacency and classification information.
12. Regarding claim 7,
Hamaguchi discloses the limitations of claim 3 and also:
A method for sorting a sorting target, the method comprising: the method for discriminating the sorting target according to claim 3; and sorting the sorting target based on discrimination information acquired in the discriminating.Hamaguchi discloses in [0005] that its neural network method performs sorting and discrimination on its rice grain sorting targets.
13. Regarding claim 4,
Hamaguchi discloses the limitations of claim 1 and also:
wherein the discriminating is performed using an inference model generated based on learning information regarding the sorting target, and the learning information includes the adjacency information between the sorting targets and the classification information of the sorting target, and the discriminating includes discriminating the sorting target using the imaging information and the inference model.Hamaguchi implicitly discloses unnumbered inference models as part of methods NN1 and NN2 along with learning methods in [0008]. A person of ordinary skill in the art will understand that neural network methods such as NN1 and NN2 necessarily incorporate inference models that are based on learned training data; all neural net classification systems that discriminate inputs into classes incorporate such models based on such data. The adjacency information as part of image data was disclosed in [0027], the classification information in [0006] and [0056] (of course classification is an essential and inherent feature of most neural network systems). Neural net methods NN1 and NN2 take the image data as inputs so the discrimination of rice grains occurs with respect to the models and the adjacency and classification information.
14. Regarding claim 8,
Hamaguchi discloses the limitations of claim 4 and also:
A method for sorting a sorting target, the method comprising: the method for discriminating the sorting target according to claim 4; and sorting the sorting target based on discrimination information acquired in the discriminating.Hamaguchi discloses in [0005] that its neural network method performs sorting and discrimination on its rice grain sorting targets.
15. Regarding claim 5,
Hamaguchi discloses the limitations of claim 1 and also:
A method for sorting a sorting target, the method comprising: the method for discriminating the sorting target according to claim 1; and sorting the sorting target based on discrimination information acquired in the discriminating.Hamaguchi discloses in [0005] that its neural network method performs sorting and discrimination on its rice grain sorting targets.
16. Regarding claim 9,
Hamaguchi discloses:
A sorting apparatus (grain sorting device: [0001]) comprising: a transfer portion (chute 2: fig. 1) configured to transfer a sorting target (rice 1: fig. 1);
an imaging portion configured (cameras S: fig. 1) to image the sorting target during the transfer by the transfer portion;
a discrimination portion (unnumbered) configured to discriminate the sorting target based on imaging information acquired by the imaging portion;Hamaguchi discloses an “autonomous neuro-board” in [0022] that performs its neural network discrimination method.
and a sorting portion (air gun 3: fig. 1) configured to sort the sorting target based on discrimination information acquired by the discrimination means, wherein the imaging information includes adjacency information between sorting targets, and classification information of the sorting target.Hamaguchi discloses discriminating the rice in [0005] and [0020] based on the image data, adjacency information based on imaging in [0027], and classification of the sorting target in [0006] and [0056].
17. Regarding claim 10,
Hamaguchi discloses the limitations of claim 9 and also:
wherein the discrimination portion includes an inference model generated based on learning information regarding the sorting target, and the learning information includes the adjacency information between the sorting targets and the classification information of the sorting target, and the discrimination portion is further configured to discriminate the sorting target using the imaging information and the inference model.Hamaguchi implicitly discloses unnumbered inference models as part of methods NN1 and NN2 along with learning methods in [0008]. A person of ordinary skill in the art will understand that neural network methods such as NN1 and NN2 necessarily incorporate inference models that are based on learned training data; all neural net classification systems that discriminate inputs into classes incorporate such models based on such data. The adjacency information as part of image data was disclosed in [0027], the classification information in [0006] and [0056] (of course classification is an essential and inherent feature of most neural network systems). Neural net methods NN1 and NN2 take the image data as inputs so the discrimination of rice grains occurs with respect to the models and the adjacency and classification information.
18. Regarding claim 11,
Hamaguchi discloses:
A discrimination apparatus (grain sorting device: [0001])We do not distinguish sorting and discrimination.
comprising: a transfer portion (chute 2: fig. 1) configured to transfer a plurality of discrimination targets (rice 1: fig. 1);
an imaging portion (cameras S: fig. 1) configured to image the plurality of discrimination targets during the transfer by the transfer portion;
and a discrimination portion (unnumbered) configured to discriminate the individual discrimination targets based on imaging information acquired by the imaging portion, wherein the imaging information includes proximity information indicating how much at least two adjacent discrimination targets are in proximity to each other.Hamaguchi discloses an “autonomous neuro-board” in [0022] that performs its neural network discrimination method. Hamaguchi discloses in [0027] that its imaging data (input to its neural network methods) includes measurements of the distance between adjacent grains of rice.
Conclusion
19. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. ISRs and foreign examiners of related family applications cited, among other references, US 2019/0364935 as anticipating all claims in the application prior to preliminary amendment.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAURENCE RAPHAEL BROTHERS whose telephone number is (703)756-1828. The examiner can normally be reached M-F 0830-1700.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ernesto Suarez can be reached at (571) 270-5565. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ERNESTO A SUAREZ/Supervisory Patent Examiner, Art Unit 3655
LAURENCE RAPHAEL BROTHERS
Examiner
Art Unit 3655A
/L.R.B./Examiner, Art Unit 3655