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 .
DETAILED ACTION
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 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.
(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, 3, 7-10, 12, and 16-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lien et al (US Pub 2020/0360969 A1).
Regarding claim 1, Lien discloses an electronic device, comprising: a transparent plate (paragraph 0020 and element 12); a plurality of cameras including a first camera (element 13) and a second camera (element 16), the first camera being disposed to face one surface of the transparent plate, the second camera being disposed to face a surface, opposite to the one surface, of transparent plate (see Fig. 1); and at least one processor (element 14), wherein the at least one processor is configured to: while a bean is moved on the transparent plate, simultaneously obtain a first image for a first surface of the bean through the first camera and a second image for a second surface of the bean through the second camera, the second surface being different from the first surface (paragraph 0021); using an artificial intelligence (AI) model, calculate, based on the first image, first probabilities that the bean belongs to each of a plurality of classification items and calculate, based on the second image, second probabilities that the bean belongs to each of the plurality of classification items (paragraph 0026); identify, among the plurality of classification items, a first classification item corresponding to a third probability highest among the first probabilities and identify, among the plurality of classification items, a second classification item corresponding to a fourth probability highest among the second probabilities (paragraph 0026 where there are multiple probabilities calculated for defects detected by the image capture devices); based on the first classification item being a same as the second classification item, determine the first classification item as a type of the bean (paragraph 0027; conforming coffee bean); based on the first classification item being not a same as the second classification item, identify whether the first classification item or the second classification item corresponds to a designated classification item, wherein the designated classification item is designated by a user input (paragraph 0027 non-conforming coffee bean); based on the first classification item or the second classification item corresponding to the designated classification item, identify whether a probability of a classification item, corresponding to the designated classification item between the first classification item and the second classification item, between the third probability and the fourth probability is greater than or equal to a first threshold (paragraph 0024 where a preset correct rate threshold value is used for determination); and based on the probability of the classification item corresponding to the designated classification item being greater than or equal to the first threshold, determine the classification item as the type of the bean (paragraph 0024 where a preset correct threshold value is indicative of identification correct rate is sufficient).
Regarding claim 3, Lien discloses the at least one processor is further configured to: based on the first classification item and the second classification item not corresponding to the designated classification item or the probability of the classification item corresponding to the designated classification item being less than the first threshold, identify whether the first classification item or the second classification item corresponds to a normal bean among the plurality of classification items (paragraph 0022 where a percent exceeding 50% black corresponds to a black bean and paragraph 0026 where a percent of probability 99% is indicative of a completely black bean); and based on the first classification item or the second classification item corresponding to the normal bean, determine, as the type of the bean, a classification item which does not correspond to normal bean between the first classification item and the second classification item (paragraphs 0022, 0024, and 0026-0027 where lower probabilities are indicative of beans that do not correspond to normal beans).
Regarding claim 7, Lien discloses the at least one processor is further configured to: based on a user input, set the first threshold corresponding to the classification item (paragraph 0024).
Regarding claim 8, Lien discloses the first surface of the bean includes an upper surface of the bean, and the second surface of the bean includes a lower surface of the bean (paragraph 0029).
Regarding claim 9, Lien discloses the first image and the second image includes still images (paragraph 0036) or moving images.
Regarding claim 10, Lien discloses a method for classifying a bean by an electronic device, the method comprising: while a bean is moved on a transparent plate (paragraph 0020 and element 12) of the electronic device, simultaneously obtain a first image for a first surface of the bean through a first camera (element 13) and a second image for a second surface of the bean through a second camera (element 16, the second surface being different from the first surface, wherein the first camera and the second camera are included in a plurality of cameras, the first camera being disposed to face one surface of the transparent plate, the second camera being disposed to face a surface, opposite to the one surface, of transparent plate (see Fig. 1); using an artificial intelligence (AI) model, calculating, based on the first image, first probabilities that the bean belongs to each of a plurality of classification items and calculating, based on the second image, second probabilities that the bean belongs to each of the plurality of classification items (paragraph 0026); identifying, among the plurality of classification items, a first classification item corresponding to a third probability highest among the first probabilities and identifying, among the plurality of classification items, a second classification item corresponding to a fourth probability highest among the second probabilities (paragraph 0026 where there are multiple probabilities calculated for defects detected by the image capture devices); based on the first classification item being a same as the second classification item, determining the first classification item as a type of the bean (paragraph 0027; conforming coffee bean); based on the first classification item being not a same as the second classification item, identifying whether the first classification item or the second classification item corresponds to a designated classification item, wherein the designated classification item is designated by a user input (paragraph 0027 non-conforming coffee bean); based on the first classification item or the second classification item corresponding to the designated classification item, identifying whether a probability of a classification item, corresponding to the designated classification item between the first classification item and the second classification item, between the third probability and the fourth probability is greater than or equal to a first threshold (paragraph 0024 where a preset correct rate threshold value is used to determination); and based on the probability of the classification item corresponding to the designated classification item being greater than or equal to the first threshold, determining the classification item as the type of the bean (paragraph 0024 where a preset correct threshold value is indicative of identification correct rate is sufficient).
Regarding claim 12, Lien discloses based on the first classification item and the second classification item not corresponding to the designated classification item or the probability of the classification item corresponding to the designated classification item being less than the first threshold, identifying whether the first classification item or the second classification item corresponds to a normal bean among the plurality of classification items (paragraph 0022 where a percent exceeding 50% black corresponds to a black bean and paragraph 0026 where a percent of probability 99% is indicative of a completely black bean); and based on the first classification item or the second classification item corresponding to the normal bean, determining, as the type of the bean, a classification item which does not correspond to normal bean between the first classification item and the second classification item (paragraphs 0022, 0024, and 0026-0027 where lower probabilities are indicative of beans that do not correspond to normal beans).
Regarding claim 16, Lien discloses based on a user input, setting the first threshold corresponding to the classification item (paragraph 0024).
Regarding claim 17, Lien discloses the first surface of the bean includes an upper surface of the bean, and the second surface of the bean includes a lower surface of the bean (paragraph 0029).
Regarding claim 18, Lien discloses the first image and the second image includes still images or moving images (paragraph 0036).
Regarding claim 19, Lien discloses an electronic device, comprising: a transparent plate (paragraph 0020 and element 12); a plurality of cameras including a first camera (element 13) and a second camera (element 16), the first camera being disposed to face one surface of the transparent plate, the second camera being disposed to face a surface, opposite to the one surface, of transparent plate (see Fig. 1); and at least one processor (element 14), wherein the at least one processor is configured to: while a bean, classified as a first type by a user, is moved on the transparent plate, simultaneously obtain a first image for an upper surface of the bean through the first camera and a second image for a lower surface of the bean through the second camera (paragraph 0021); using an artificial intelligence (Al) model, calculate, based on the first image, first probabilities that the bean belongs to each of a plurality of classification items and calculate, based on the second image, second probabilities that the bean belongs to each of the plurality of classification items (paragraph 0026); identify, among the plurality of classification items, a first classification item corresponding to a third probability highest among the first probabilities and identify, among the plurality of classification items, a second classification item corresponding to a fourth probability highest among the second probabilities (paragraph 0026 where there are multiple probabilities calculated for defects detected by the image capture devices); and based on a probability of a classification item corresponding to the first type between the first classification item and the second classification item between the third probability and the fourth probability, set a threshold for classifying the bean (paragraph 0024 where a preset correct rate threshold value is used for determination).
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 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 of this title, 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 2 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Lien in view of Kim (KR 102296107).
Regarding claim 2, Lien does not disclose the claim limitations. Kim teaches at least one patch, wherein the at least one processor is further configured to: obtain an image for the at least one patch through the plurality of cameras; and based on the image for the at least one patch, set a setting related to auto white balance and/or auto color correction of the plurality of cameras (paragraphs 0040-0041) for the purpose of clearly photographing the color of the coffee beans. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to modify Lien, as taught by Kim, for the purpose of clearly photographing the color of the coffee beans.
Regarding claim 11, Lien does not disclose the claim limitations. Kim teaches obtaining an image for the at least one patch through the plurality of cameras; and based on the image for the at least one patch, setting a setting related to auto white balance and/or auto color correction of the plurality of cameras (paragraphs 0040-0041) for the purpose of clearly photographing the color of the coffee beans. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to modify Lien, as taught by Kim, for the purpose of clearly photographing the color of the coffee beans.
Claims 4-6, 13-15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Lien in view of Cho (KR 102259009).
Regarding claim 4, Lien does not disclose the claim limitations. Cho teaches the at least one processor is further configured to: based on the first classification item and the second classification item not corresponding to the normal bean among the plurality of classification items, identify whether a difference between the third probability and the fourth probability is greater than or equal to a second threshold; and based on the difference between the third probability and the fourth probability being greater than or equal to the second threshold, determine, as the type of the bean, a classification item, corresponding to probability higher between the third probability and the fourth probability, between the first classification item and the second classification item (paragraphs 0246-0247 where the ripening degree such as 1st, 2nd, and 3rd ripeness delineate multiple threshold levels of classification) for the purpose of judging color from an image to base the level of acceptability of the scanned item. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to modify Lien, as taught by Cho, for the purpose of judging color from an image to base the level of acceptability of the scanned item.
Regarding claim 5, Lien does not disclose the claim limitations. Cho teaches the at least one processor is further configured to: based on the difference between the third probability and the fourth probability being less than the second threshold, identify weights set for the first classification item and the second classification item, and based on the third probability, the fourth probability, and the weights, determine the type of the bean (paragraph 0246-0249 where items are a degree of ‘ripeness’ and that the preset reference threshold is set to a desired threshold, or weight, based on the season) for the purpose of judging color from an image to base the level of acceptability of the scanned item. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to modify Lien, as taught by Cho, for the purpose of judging color from an image to base the level of acceptability of the scanned item.
Regarding claim 6, Lien does not disclose the claim limitations. Cho teaches the at least one processor is further configured to: based on a user input (paragraph 0248), set the weights for the purpose of judging color from an image to base the level of acceptability of the scanned item. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to modify Lien, as taught by Cho, for the purpose of judging color from an image to base the level of acceptability of the scanned item.
Regarding claim 13, Lien does not disclose the claim limitations. Cho teaches based on the first classification item and the second classification item not corresponding to the normal bean among the plurality of classification items, identifying whether a difference between the third probability and the fourth probability is greater than or equal to a second threshold; and based on the difference between the third probability and the fourth probability being greater than or equal to the second threshold, determining, as the type of the bean, a classification item, corresponding to probability higher between the third probability and the fourth probability, between the first classification item and the second classification item (paragraphs 0246-0247 where the ripening degree such as 1st, 2nd, and 3rd ripeness delineate multiple threshold levels of classification) for the purpose of judging color from an image to base the level of acceptability of the scanned item. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to modify Lien, as taught by Cho, for the purpose of judging color from an image to base the level of acceptability of the scanned item.
Regarding claim 14, Lien does not disclose the claim limitations. Cho teaches based on the difference between the third probability and the fourth probability being less than the second threshold, identifying weights set for the first classification item and the second classification item; and based on the third probability, the fourth probability, and the weights, determining the type of the bean (paragraph 0246-0249 where items are a degree of ‘ripeness’ and that the preset reference threshold is set to a desired threshold, or weight, based on the season) for the purpose of judging color from an image to base the level of acceptability of the scanned item. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to modify Lien, as taught by Cho, for the purpose of judging color from an image to base the level of acceptability of the scanned item.
Regarding claim 15, Lien does not disclose the claim limitations. Cho teaches based on a user input (paragraph 0248), setting the weights judging color from an image to base the level of acceptability of the scanned item for the purpose of judging color from an image to base the level of acceptability of the scanned item. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to modify Lien, as taught by Cho, for the purpose of judging color from an image to base the level of acceptability of the scanned item.
Regarding claim 20, Lien does not disclose the claim limitations. Cho teaches the at least one processor is further configured to: based on the probability of the classification item being different from a previously set threshold, set the probability of the classification item as the threshold for classifying the bean (paragraphs 0246-0247 where the ripening degree such as 1st, 2nd, and 3rd ripeness delineate multiple threshold levels of classification) for the purpose of judging color from an image to base the level of acceptability of the scanned item. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to modify Lien, as taught by Cho, for the purpose of judging color from an image to base the level of acceptability of the scanned item.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Kalyanavenkateshware Kumar whose telephone number is (571)272-8102. The examiner can normally be reached on M-F 08:00-16:30.
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/K.K./Examiner, Art Unit 3653
/MICHAEL MCCULLOUGH/Supervisory Patent Examiner, Art Unit 3653