CTNF 18/834,689 CTNF 86621 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 07-04-01 AIA 07-04 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. Claim 1 along with its dependent claims 2-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim 1 is drawn to at a storage medium in the specification in paragraph ¶[0014] According to a third aspect of the present invention, a storage medium stores a program for making a computer thus explicitly defined to encompass both transitory and non-transitory, can be a signal or carrier wave etc; therefore, fail(s) to fall within at least one of the four categories of patent eligible subject matter . It has been understood by the office that the at least one computer readable media is the same as "computer program medium" and "computer usable medium" and that these are forms of memory which are statutory (e.g., ROM, RAM, hard drive, a hard disk, an integrated circuit assembly, magnetic tape, cartridge, optical disk, a CD or DVD, or other fixed or removable medium that is read by, written to or accessed by media drive 512. As these examples illustrate, the storage media 514 can include a computer usable storage medium having stored therein computer software or data) and also transitory propagating signals, per se, thus includes both transitory, and non-statutory subject matter ; since the specification explicitly states are used to generally refer to transitory or non-transitory media such as . Therefore claims 11-20 do not fit within the recognized categories of statutory subject matter. See MPEP 2106. The office respectfully recommend the applicant to amend claim 11 limitation “at least one computer readable media encoded with instructions which, when executed, cause the system to perform operations, where the at least one computer readable media encoded with instructions which, when executed” to reflect the limitation “a non-transitory computer readable media encoded with instructions which, when executed, cause the system to perform operations, where the at least one computer readable media encoded with instructions which, when executed”. Claims 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Regarding claim 1: Claim 1 is directed to idea of itself (abstract idea). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception for the following reason(s): Step 1: Claim 1 recites series of acts for evaluate candidate regions based on rotational movement and analyze those movements using machine learning models. Thus, the claim is directed to a process, which is one of the statutory categories of the invention. Step 2A The claims use generic, conventional hardware components—a "storage medium" and a "processor". Simply instructing a generic computer to execute algorithms, compare images, and apply learning models does not add an inventive concept; it merely adds a "do it on a computer" limitation, which the Supreme Court explicitly struck down as insufficient. Step 2B, the additional limitations left in the claim a highly particularized environment: evaluating images of a "detection target inside a transparent container" while "rotating the transparent container," and using dual learning models (one for static image information, one for chronological change). (e.g. comparing ). Treating claim 1 as a whole, the additional limitations do not show inventive concept in applying the judicial exception (e.g. images being analyzed in a clear container, comparing) or do not provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment . Thus, claim 1 as a whole is not significantly more than the abstract idea itself and is ineligible. And the same analysis applies for claim 2-11. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim s 1-2, and 9-11 are rejected under 35 U.S.C. 103 as being unpatentable over Budd (US 2006/0072111) in view of Bremer (US 2018/0025253) . As per claim 1, Budd teaches, an image processing device and method and a non-transitory storage medium comprising: a storage medium configured to store instructions (Budd, ¶[0 072] “The analysis of the image data is performed using special software written to extract the particles in solution. The system acquires multiple HR images in rapid secession (4-60 images) and stores them in separate frame buffers.” This represents a storage medium ); and a processor configured to execute the instructions to: compare multiple images capturing a detection target inside a transparent container (Budd, ¶[0072] The software compares each image the previous image (except in the case of the first) and isolates any object with the image field of view that moves.” This represents comparing ), the images being captured while rotating the transparent container (Budd, ¶[0073] “The control line between the motor controller and the motor is indicated by item 39. In the evaluation unit a high torque stepper motor with lower inertia was used to rotate the test sample.” Images being taken while container rotates), and determines, among candidate regions for being the detection target appearing in the images, candidate regions that move in a movement direction in accordance with the rotation (Budd, ¶[0082] “The particle under investigation is moved into a well-defined region where it can be measured precisely. The particle is indicated by Item 54 in FIG. 13. The particle appears in the region on the bottom center of the container labeled Item 53.” And “the particles that have a density greater than that of the fluid but less than about 1.8 gm/cc will be pulled upward by the vortex and appear in a region center on the spin axis and above the bottom of the container indicated by Item 56.” This represents among candidate regions for being the detection target appearing in the images, candidate regions that move in a movement direction in accordance with the rotation since there is a well-defined region and there is a spin axes that gets measured and images get taken). Budd does not clearly teach, determine a presence or absence of the detection target by using first determination results obtained by using a first learning model and image information for the candidate regions that move in the movement direction in accordance with the rotation to determine whether or not the candidate regions are the detection target, and second determination results obtained by using a second learning model and information indicating a chronological change in the candidate regions that move in the movement direction in accordance with the rotation to determine whether or not the candidate regions are the detection target. However Bremer teaches, determine a presence or absence of the detection target by using first determination results obtained by using a first learning model (Bremer, [0026] “For example, the classifier may be trained using segments that are generated from images of prohibited objects. Because the image analysis system has already filtered out segments that likely do not represent prohibited objects, the classifier can focus on a very small subset of the segments, which is likely to reduce the number of false positive and false negative identifications of segments.” This represent determine a presence or absence of the detection target by using first determination results obtained by using a first learning model because it is training to be able to see the right object ) and image information for the candidate regions that move in the movement direction in accordance with the rotation to determine whether or not the candidate regions are the detection target (Bremer, fig.2 205 “determine whether portions” which represent regions are the detection target “segments represent uncommon object” and in movement in that direction ), and second determination results obtained by using a second learning model and information indicating a chronological change in the candidate regions that move in the movement direction in accordance with the rotation to determine whether or not the candidate regions are the detection target (Bremer, ¶[0027] “[0027] In some embodiments, the images collected by an airport security system can be added to the collection of images on an ongoing basis so that the corpus of common objects can change over time to represent changes in the types of objects that are commonly included in checked bags. Also, clusters of segments generated from images of prohibited objects could be added to the collection and tagged as prohibited. During normal airport security screening, if a segment matches a cluster tagged as prohibited, the image analysis can flag the segment for special manual or automated processing. In addition, airport personnel could be requested to label those segments not identified as containing a common object with an object type. Over time, the image analysis system can use those labeled common objects to identify matching objects that would not otherwise be identified as common objects because of, for example, an insufficient number of segments in a cluster.” Objects changing over time and editing this represents a second learning model). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Bremer with the teaching of Budd to be able to have two learning models to tell the difference. The motivation would have been to be able to see new objects and things as they come up as taught by Budd in ¶[005]. As per claim 2, Budd in view of Bremer teaches, the image processing device according to claim 1, wherein the processor is configured to execute the instructions to: extract candidate regions for being the detection target in a newly acquired image by comparing pixel values of respective pixels of the newly acquired image with a reference histogram generated based on pixel values of respective pixels of the multiple images prepared in advance by fixing the image capture device of the images and rotating the transparent container (Bremer, ¶[004] “partition the image into multiple segments (groups of pixels or voxels) that each may represent an object within the image. This partitioning is referred to as image segmentation. A goal of image segmentation is to use the low-level information at each voxel to infer high-level semantics such as differentiating between foreground and background or finding an object.” This represents partition of the objects in sections by pixels). As per claim 9, Budd in view of Bremer teaches, the image processing device according to claim 1, wherein: the images capture detection targets accumulated in a curved portion of a wall surface of the transparent container or detection targets accumulated at a surface at which a medium enclosed inside the transparent container contacts another medium inside the transparent container (Budd, fig.8, 29 as can be seen the liquid touches the bottom which would be considered curved). Allowable Subject Matter Claims 3-8 are 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. The limitations “identify regions with similar light reflection based on differences between respective pixels in a first image captured by fixing the image capture device of the images and capturing the transparent container with the image capture device, and a second image capturing the transparent container with the image capture device after having shaken the transparent container, and extract the candidate regions for being the detection target in which there is a change in the pixel values based on differences between the first image and the second image in which the regions with similar light reflection are matched” were not found in the prior art. And as long as the 101 rejection gets overcome as well. As well as “wherein the processor is configured to execute the instructions to: identify, in the multiple pixels, associated regions in accordance with movement of the candidate region based on rotation of the transparent container, in the identifying, wherein the processor is configured to execute the instructions to use the candidate regions in chronologically successive images captured among the multiple images and weight information indicating a level of continuity of the candidate regions in those images to identify, as the associated regions, the candidate regions in the successive images.” Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to SANTIAGO GARCIA whose telephone number is (571)270-5182. The examiner can normally be reached Monday-Friday 9:30am-5:30pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chineyere Wills-Burns can be reached at (571) 272-9752. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /SANTIAGO GARCIA/Primary Examiner, Art Unit 2673 /SG/ Application/Control Number: 18/834,689 Page 2 Art Unit: 2673 Application/Control Number: 18/834,689 Page 3 Art Unit: 2673 Application/Control Number: 18/834,689 Page 4 Art Unit: 2673 Application/Control Number: 18/834,689 Page 5 Art Unit: 2673 Application/Control Number: 18/834,689 Page 6 Art Unit: 2673 Application/Control Number: 18/834,689 Page 7 Art Unit: 2673 Application/Control Number: 18/834,689 Page 8 Art Unit: 2673 Application/Control Number: 18/834,689 Page 9 Art Unit: 2673
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