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 .
2. This is in response to the applicant response filed on 08/05/2026. In the applicant’s response, claims 1-3, and 11 were amended. Accordingly, claims 1-29 are pending and being examined. Claims 1 and 11 are independent form.
Claim Rejections - 35 USC § 112(b)
3. The claim rejections under 35 USC § 112(b) make in the previous office action are withdrawn in view of applicant’s amendment.
Claim Rejections - 35 USC § 101
4. The claim rejections under 35 USC § 101 make in the previous office action are STILL MAINTAINED because the claimed inventions are directed to non-statutory subject matter (an abstract ideal without significantly more). For example, at step one, claim 1 is directed to collecting information including “irregularity in operating the food processing line and a point in time of the at least one irregularity”, analyzing them including “select[ing] at least a subset of the plurality of images that are relevant for determining a cause of the at least one irregularity from the plurality of images recorded by the at least one camera”, and “adjust[ing] the operation of the food processing line based on the cause of the at least one irregularity”. As such, claim 1 merely recites a common practice long performed by field practitioners. At step two, there is no additional inventive elements in claim 1 but for using generic hardware to accomplish the abstract idea within the specific field of food line quality assessment. Therefore, the claim as a whole does not integrate the judicial exception into a practical application and is not patent eligible. Likewise, independent claim 11 is analogous to claim 1 and is not patent eligible. Their respective dependent claims are patent ineligible as well as explained in the previous office action.
Claim Rejections - 35 USC § 103
5. 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.
6. 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.
7. Claims 1-29 are rejected under 35 U.S.C. 103 as being unpatentable over 이종문 et al (KR101010136, hereinafter “136’”) in view of Ago et al (WO2019151394, hereinafter “Ago”). A machine translated English version of each of them is provided by the examiner with the previous office action.
Regarding claim 1, 136’ (i.e., the document KR101010136-Eng) discloses a the hierarchical temporal memory based intelligent quality determination system includes image and quality sensors; see figs.2-3, see abstract and pg.6, lines 27-31) comprising,
a sensor apparatus for determining at least one irregularity in operating the the system may recognize the good or bad state of a product in a timely manner; see S120-S130 of fig.2 and pg.6, lines 36-43);
at least one camera configured to continuously record a plurality of images of at least one region of the the product inspection step of the system may take an image of the product; see S150 of figs.2/3 and pg.7, lines 14-24); and
a central data processing device that, during operation of the (the product recognition step of the system may determine whether or not the input image product is defective according to the quality standard of each product; see S160 of fig.2 and pg. 8, lines 28-30),
wherein the central data processing device is further configured to adjust the operation of the see pg.9, lines 14-19: “The subsequent action step (S180), the facility control process (S182) for stopping the equipment when the product defects occur continuously for more than a predetermined number of times, and generates an alarm when the product defects occur more than a predetermined number of times in a row.”).
As explained above, 136’ discloses the claimed invention expect for “a food product line”. However, 136’ teaches that, the scope of the invention can be modified based on the spirit of the invention (see pg.10, lines 30-32). As evidence, in the same filed of endeavor, that is, in the field of product quality inspection using images of products, Ago (i.e., the document WO2019151394-Eng) teaches a food quality inspection system based on food item images. See Abstract and fig.6. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the teachings of Ago into the teachings of 136’ and apply the method taught by 136’ to a food item inspection taught by Ago. Suggestion or motivation for doing so would have been to inspect food items and “product high-quality food and control costs” as taught by Ago, see Abstract. Therefore, the claim is unpatentable over 136’ in view of Ago.
Regarding claim 2, 13, the combination of 136’ and Ago discloses, wherein the central data processing device is configured to automatically recognize the cause and/or a point of origin of the at least one irregularity based on the subset of the plurality of images and/or at least a subset of the sensor data of the sensor apparatus and to initiate suitable countermeasures (136’, wherein the HTM model used for product recognition is “an automatically artificial neural network (ANN)”; see pg.4, lines 16-28).
Regarding claim 3, 19, 20, the combination of 136’ and Ago discloses, wherein the central data processing device is configured to learn through machine learning the cause of the at least one irregularity and/or how the at least one irregularity is to be eliminated (136’, wherein the HTM model used for product recognition is “an artificial neural network (ANN)”; see pg.4, lines 16-28).
Regarding claim 4, the combination of 136’ and Ago discloses the food processing line of claim 1, wherein the at least one camera comprises a plurality of cameras, and wherein each of the plurality of cameras, during operation, continuously records images of a different region of the food processing line (136’, the product inspection step of the system may take an image of the product; see S150 of figs.2/3 and pg.7, lines 14-24;).
Regarding claim 5, 14, the combination of 136’ and Ago discloses, wherein the sensor apparatus comprises a plurality of sensors to determine various irregularities in the food processing line (136’, the product recognition step of the system may determine whether or not the input image product is defective according to the quality standard of each product; see S160 of fig.2 and pg. 8, lines 28-30).
Regarding claim 6, the combination of 136’ and Ago discloses the food processing line of claim 1, wherein the sensor apparatus comprises the at least one camera (136’, the hierarchical temporal memory based intelligent quality determination system includes image and quality sensors; see pg.6, lines 27-31)
Regarding claim 7, the combination of 136’ and Ago discloses the food processing line of claim 1, wherein the at least one irregularity comprises at least two irregularities, and wherein the central data processing device is configured to automatically perform a prioritization of an irregularity of the at least two irregularities when the at least two irregularities simultaneously occur (136’, wherein the product line includes two or more products as shown by fig3, each of them may be defective according to the quality standard of each product; see S160 of fig.2 and pg. 8, lines 28-30).
Regarding claim 8, the combination of 136’ and Ago discloses the food processing line of claim 1, wherein the central data processing device is configured to detect trends based on the at least one irregularity (136’, the product recognition step of the system may determine whether or not the input image product is defective according to the quality standard of each product; see S160 of fig.2 and pg. 8, lines 28-30).
Regarding claim 9, the combination of 136’ and Ago discloses the food processing line of claim 1, wherein the central data processing device is configured to determine incorrect settings at the food processing line based on the at least one irregularity (136’, the product recognition step of the system may determine whether or not the input image product is defective according to the quality standard of each product; see S160 of fig.2 and pg. 8, lines 28-30).
Regarding claim 10, the combination of 136’ and Ago discloses the food processing line according to claim 1, wherein the at least one camera is a spectral camera (Ago, see on pg. 16, lines 20-28: “As an example of such a camera, both an RGB camera and an infrared camera may be used. Alternatively, a multispectral camera or a hyperspectral camera having a larger number of wavelengths that can be captured may be used.”).
Regarding claim 25, the combination of 136’ and Ago discloses the food processing line of claim 1, further comprising: at least one of: the conveyor belt 210. The camera 230 to transmit the same, the lens 240 provided at the tip of the camera 230, and in the direction of the production line to provide the same illumination at all times within the shooting range of the camera 230 and to minimize shadows.; see fig.4, pg.7, line 44-pg.8, line3),
Regarding claim 26, the combination of 136’ and Ago discloses the food processing line of claim 4, wherein the plurality of cameras comprises mobile cameras and stationary cameras (Ago, see on pg. 16, lines 20-28: “As an example of such a camera, both an RGB camera and an infrared camera may be used. Alternatively, a multispectral camera or a hyperspectral camera having a larger number of wavelengths that can be captured may be used.”).
Regarding claim 27, the combination of 136’ and Ago discloses the food processing line of claim 5, wherein the plurality of sensors comprises different sensor types (ibid.).
Regarding claim 28, the combination of 136’ and Ago discloses the food processing line of claim 8, wherein the trends comprise wear at the food processing line (Ago, see pg.14, lines 1-4: “examples of defects include those related to shapes such as chips, cracks, and twists”).
Regarding claim 29, the combination of 136’ and Ago discloses the food processing line of claim 9, wherein the central data processing device is configured to automatically correct the incorrect settings (136’, see pg.6, lines 1-13: “Product recognition step (S160) to recognize the correct product shape by specifying the image of the product, and the product finally judged in the product recognition step (S160) by comparing the reference image of the product with the final product to determine the defective product. If product defects are caused by the quality determination step (S170) and the product quality determination step (S170), the facility is controlled according to the control method set in the facility control method setting step (S140) at the same time as a notification of the fact to the outside. It consists of a subsequent action step (S180) and the like.” See on pg.12, lines 7-9: “A product recognition step (S160) of recognizing a product image measured in the product inspection step (S150), and recognizing the type and correct product shape of the product”).
Regarding claim 11, the claim is an inherent variation of claim 1, thus it is interpreted and rejected for the reasons set forth in the rejection of claim 1.
Regarding claim 12, 15, 16, 17, the combination of 136’ and Ago discloses the method of claim 11, further comprising: determining a type of the cause of the irregularity; and eliminating the cause of the irregularity based on the type (136’, see on pg.12, lines 7-9: “A product recognition step (S160) of recognizing a product image measured in the product inspection step (S150), and recognizing the type and correct product shape of the product”).
Regarding claim 18, the combination of 136’ and Ago discloses the method of claim 11, wherein determining the cause of the irregularity comprises comparing data collected on the irregularity with data from previously detected irregularities (136’, the product recognition step of the system may determine whether or not the input image product is defective according to the quality standard of each product; see S160 of fig.2 and pg. 8, lines 28-30).
Regarding claim 21, 22, 23, the combination of 136’ and Ago discloses the method of claim 11, further comprising: categorizing the irregularity by at least one of: a type of irregularity, a component associated with the irregularity, the point in time of detecting the irregularity, the point in time of an occurrence of the irregularity, a severity of the irregularity (136’, See on pg.12, lines 7-9: “A product recognition step (S160) of recognizing a product image measured in the product inspection step (S150), and recognizing the type and correct product shape of the product”).
Regarding claim 24, the combination of 136’ and Ago discloses the method of claim 11, wherein the at least one image acquired by the camera is processed before the at least one image is released for output (136’, see fig.4, pg.7, line 44--pg.8, line 3, “wherein the product is conveyed by the conveyor belt 210. The camera 230 to transmit the same, the lens 240 provided at the tip of the camera 230, and in the direction of the production line to provide the same illumination at all times within the shooting range of the camera 230 and to minimize shadows...The support 220 is installed at the upper end of the cradle 260, the conveyor belt 210 is inclined to the cradle 260. Therefore, when the product is transported along the conveyor belt 210, the photographing device 200 is to take an image of the product.”).
Response to Arguments
8. Applicant’s arguments, with respects to the claim rejection under 35 USC 101, filed on 08/05/2026, have been fully considered but they are not persuasive. For the detailed explanation, see the section of Claim Rejection Under 35 USC 101 above.
9. Applicant’s arguments, with respects to the claim rejection under 35 USC 103, filed on 08/05/2026, have been fully considered but they are not persuasive.
On page 9, applicant argues ‘136 does not disclose “to select at least a subset of the plurality of images that are relevant for determining a cause of the at least one irregularity from the plurality of images recorded by the at least one camera” as recited by claim 1. The examiner respectfully disagrees with that. It is because ‘136, see S160 of fig.2 and pg. 8, lines 28-30, clearly states, the system may determine whether or not the input image product is defective according to the quality standard of the product. Here, the cause of defectiveness of a product image is the poor quality of the product image.
10. Therefore, in view of the above reasons, examiner maintains rejections.
Conclusion
11. THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
12. Any inquiry concerning this communication or earlier communications from the examiner should be directed to RUIPING LI whose telephone number is (571)270-3376. The examiner can normally be reached 8:30am--5:30pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, HENOK SHIFERAW can be reached on (571)272-4637. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RUIPING LI/Primary Examiner, Ph.D., Art Unit 2676
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