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 .
Specification
The use of the term “IEEE”, which is a trade name or a mark used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter, for the reasons as follows:
In re to claim 1, the claim is directed to a system, which falls within one of the four statutory categories. Additionally, due to reciting similar limitations, claim 8 and 15 are rejected for the same reasons provided below (as the method and non-transitory computer readable memory executed by the system of claim 1).
Claim 1 recites: “a system comprising: a memory that stores computer executable components; and
a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise: a measurement component that accesses a k-distance data tree comprising positional coordinates of a plurality of shapes within an image; and
measures distances between neighboring shapes of the plurality of shapes, wherein the measuring comprises parsing the k-distance data tree for nearest neighbor shapes within the plurality of shapes.”
The limitations of claim 1, as drafted, are considered to fall under the category of an abstract concept.
For example, an individual may perform an evaluation of a data tree within their mind to determine distances between shapes.
Thus, the claim recites an abstract idea. Additionally, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “…a memory …” and “…a processor…”
The additional elements do not recite an improvement in the functioning of a computer or other technology or technical field, the claimed steps are not performed using a particular machine, the claimed steps do not effect a transformation, and the additional elements do not apply the judicial exception in any meaningful way beyond generically linking the use of the judicial exception to a particular technological environment (See MPEP 2106.04(d)). Therefore, the analysis under prong two of step 2A of the Subject Matter Eligibility Test does not result in a conclusion of eligibility (See flowchart in
MPEP 2106).
Furthermore, the additional elements do not add significantly more to the judicial exception. Memory may be implemented by a generic memory component that performs functions that are well- understood, routine and conventional. Memory is a computer element which performs generic computer data storage. Thus, this element does not amount to more than implementing the abstract idea with a computerized system.
A processor may be implemented by a generic computer that performs functions that are well- understood, routine and conventional. It is a computer element which performs generic computer functions/computations. Thus, this element does not amount to more than implementing the abstract idea with a computerized system.
Thus, taken alone, the additional elements do not amount to significantly more than the above-
identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination
adds nothing that is not already present when looking at the elements taken individually. There is no
indication that the combination of elements improves the functioning of a computer or improves any
other technology. Their collective functions merely provide conventional computer implementation, and
mere implementation on a generic computer does not add significantly more to the claims. Accordingly,
the analysis under step 2B of the Subject Matter Eligibility Test does not result in a conclusion of eligibility (See flowchart in MPEP 2106). Similarly, claims 11 and 12 (which recite similar limitations) are rejected for the same reasons.
Further, the limitation “…a measurement component that accesses a k-distance data tree comprising positional coordinates of a plurality of shapes within an image …” is regarded as insignificant extra-solution activity with respect to MPEP 2106.05 (g), being only related to the measurement of data derived from the data tree, understood as mere data gathering.
Dependent claims
Claims 2 and 3 (dependent on claim 1), claims 9 and 10 (dependent on claim 8) as well as claims 16-17 (dependent on claim 15) disclose additional details regarding the measurement operations of claims 1, 8, and 15 (respectively). They do not add significantly more than the abstract idea, nor integrate it into a practical application. As such, they are a part of the abstract idea.
Claims 4-6 (dependent on claim 1), claims 11-13 (dependent on claim 9), and claims 18-20 disclose shape generation for components within the image, with claims 6, 13, and 20 (respectively) further denoting that the objects are of memory cells of a semiconductor device. These limitations do not add significantly more than the abstract idea, nor integrate it into a practical application. Further, the add additional details regarding intermediate processes taken by the system of claim 1 do not provide limitations that make the system a particular machine. As such, they are a part of the abstract idea.
Claims 7 (dependent on claim 1) and 14 (dependent on claim 8) disclose additional details regarding the generation of the data trees used in claim 1 and in claim 8 (respectively). They do not add significantly more than the abstract idea, nor integrate it into a practical application. As such, they are a part of the abstract idea.
Claim Rejections - 35 USC § 102
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, 4-6, 15, and 18-20 are rejected under 35 U.S.C. 102 (a)(1)/(a)(2) as being anticipated by Chhabara et al. (US publication 20240087135 A1; hereinafter “Chhabra”).
In re to claim 1, Chhabra teaches wherein: a system comprising: a memory (non-transitory storage medium; [0094] lines 25-30 discloses storage of software to execute processes of the system) that stores computer executable components ([0094] lines 3-9 disclose the use of instructions in the form of software to perform processes shown in figs. 5A-5D. Additionally, [0094] lines 25-30 discloses use of a processing device to execute instructions stored on the non-transitory storage medium. It is understood processes performed by the processing device are the computer executable components); and
a processor that executes the computer executable components stored in the memory ([0094] lines 25-30 discloses use of a processing device (understood as the processor) to execute instructions stored on the non-transitory storage medium), wherein the computer executable components comprise: a measurement component that accesses a k-distance data tree comprising positional coordinates ([0089] discloses the use of the processing device to determine nearest neighbors regarding a substrate using a k-dimension tree (understood as a k-distance data tree). This is understood to comprise positional coordinates of a plurality of shapes due to the k-dimension tree being stated to an organization of points in dimensional space) of a plurality of shapes within an image ([0089] discloses that the k-dimension tree (KDTree) is used to assess holes (422), these holes being understood as the plurality of shapes. Further, [0089] lines 1-8 discloses that the holes being assessed are a part of an image processed by the system); and
measures distances between neighboring shapes of the plurality of shapes, wherein the measuring comprises parsing the k-distance data tree for nearest neighbor shapes within the plurality of shapes ([0089] lines 8-18 discloses determination of the nearest neighbor for the shapes (correspondent to the claims) according to the KDTree. [0089] lines 18-23 further discloses the use of nearest neighbor distance to perform connections and associations. Thus, by using nearest neighbor distances of a KDTree for further operations, it is understood to disclose the measurement of distances between neighboring shapes (correspondent to the claims) by parsing the KDTree for nearest neighbor shapes (correspondent to the claims)).
In re to claim 4 [dependent on claim 1], Chhabra teaches wherein: the computer executable components further comprise a shape generation component that identifies one or more objects within the image ([0086] discloses shape generation by virtue of contour detection to further generate an image showing the contours of detected holes (as shown in Fig. 4E (430C)));
extracts contours of the one or more objects ([0086] and Fig. 4E (430C) disclose the extraction of contours by virtue of generating shape contours following the application of a contour detection algorithm. Additionally, it is understood that an individually detected hole is the object whose contour is extracted); and
generates the one or more shapes based on the extracted contours ([0086] discloses generation of the processed image, which as shown in Fig. 4E, comprises holes that are represented by contour detections. Thus, disclosing generation of the one or more shapes based on extracted contours).
As to claim 8, it is the method executed by the system of claims 1. As such it recites similar limitations and is rejected for the same reasons as provided above.
As to claim 15, it is the non-transitory computer readable memory executed by the system of claims 1. As such it recites similar limitations and is rejected for the same reasons as provided above.
As to claims 18-20, they are the non-transitory computer readable memory executed by the system of claims 4-6. As such they recite similar limitations and is rejected for the same reasons as provided above.
Claim Rejections - 35 USC § 103
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.
Claims 5 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Adiga (US publication 20210263430 A1; hereinafter “Adiga”).
In re to claim 5 [dependent on claim 4], Chhabra teaches wherein: the shape generation component comprises a segmentation ([0086] discloses generation of the processed image, which as shown in Fig. 4E, comprises holes that are represented by contour detections. It is understood that the generation of different instances of contoured holes within an imaged substrate is a segmentation that results in shape generation).
Chhabra does not explicitly teach wherein: segmentation is performed by a neural network that identifies the one or more objects within the image.
However, in a similar field of endeavor, Adiga teaches wherein: segmentation is performed by a neural network that identifies the one or more objects within the image ([0031] lines 8-14 discloses the performance of segmentation using neural network models on image data).
Adiga, like Chhabra, teaches a system that uses image data to analyze semiconductor devices with respect to structures on said devices using k-nearest neighbor operations.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective
filing date of the claimed invention to modify Chhabra, to perform segmentations, as taught by Adiga. The motivation for the proposed modification would have been to leverage a machine learning operation to perform segmentation. Allowing the system to be trained to identify structures based on region of interest characteristics, thereby avoiding issues that could occur due to low contrast (as is noted by Adiga [0023]-[0024] with regard to segmentation assisted by machine learning).
In re to claim 6 [dependent on claim 4], Chhabra does not explicitly teach wherein: the one or more objects comprise memory cells of a semiconductor device.
However, in a similar field of endeavor, Adiga teaches wherein: the one or more objects comprise memory cells of a semiconductor device ([0054] and Fig. 6 shows the contour detection of memory cells).
Adiga, like Chhabra, teaches a system that uses image data to analyze semiconductor devices with respect to structures on said devices using k-nearest neighbor operations.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective
filing date of the claimed invention to modify Chhabra, to perform detect memory cells, as taught by Adiga. The motivation for the proposed modification would have been to aid the user in seeing memory cells, aiding them in observing the composition of an imaged semiconductor device by highlighting memory cell structures alongside holes.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Chhabra in view of Bently (non-patent literature titled “Multidimensional Binary Search Trees Used for Associative Searching”; hereinafter “Bently”).
In re to claim 7 [dependent on claim 1], Chhabra teaches wherein: the computer executable components further comprise a tree generation component that generates the k-distance data tree ([0089] discloses the processing device utilizing the KDTree to define hole neighbors. As such, the system discloses generation of a KDTree (and a subsequent generation component) due to the processing device obtaining and using a KDTree. Further, it is understood that this comprises the computer executable components as this is performed by the processing device), wherein the tree generation component generates the k-distance data tree by: converting the plurality of shapes into a plurality of positional coordinates ([0089] discloses the KDTree as a dimensional organization structure used to determine nearest neighbors for the imaged holes. Further, the system is understood to disclose converting the plurality of shapes (correspondent to the claims) to positional coordinates for tree generation due to said tree being a dimensional organization data structure that identifies distances between each hole (see [0089] lines 20-25). It is understood that the portion of the system that determines the hole coordinates and produces the KDTree to be used is the generation component);
Chhabra does not explicitly teach wherein: selecting starting positional coordinates from the plurality of positional coordinates; and
Chhabra teaches wherein: generating one or more subtrees from the starting positional coordinates based on alternating dimension hyperplanes between positional coordinates of the plurality of positional coordinates.
However, in a related field of endeavor, Bently teaches wherein: selecting starting positional coordinates from the plurality of positional coordinates (Fig. 1 shows a two dimensional coordinate system containing points indicative of positional coordinates. Further, as shown in the second diagram of Fig. 1, point A is selected as a starting positional coordinate of the plurality of positional coordinates); and
Chhabra teaches wherein: generating one or more subtrees from the starting positional coordinates based on alternating dimension hyperplanes between positional coordinates of the plurality of positional coordinates (Fig. 1 shows the generation of subtrees based on the starting positional coordinate (shown by the branches that further develop branches of their own in Fig. 1’s second diagram). Additionally, the first diagram of Fig. 1 shows the use of alternating dimension hyperplanes between positional coordinates by virtue of horizonal lines branching off of the vertical line that point A is on).
Bently, like Chhabra, performs analysis of K-dimensional data trees, using them to evaluate coordinate data related characteristics in a given data set.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective
filing date of the claimed invention to modify Chhabra, to perform KDtree data searching, as taught by Bently. The motivation for the proposed modification would have been to enable the presentation of tree data in the format of Bently section 2 and in Fig. 1, providing a given user the ability to visualize the KDtree searches, fostering a greater understanding of the system’s operations for the sake of transparency.
As to claim 14, it is the method executed by the system of claims 7. As such it recites similar limitations and is rejected for the same reasons as provided above.
Allowable Subject Matter
Claim 2-3, 9-13, and 16-17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form to overcome the 101 rejection above, and if rewritten including all of the limitations of the base claim and any intervening claims.
The following is an examiner’s statement of reasons for allowance for claims 2-3, 9-13, and 16-17. The claimed features of claims 2, 9, and 16 (as well as their respective dependent claims) are not anticipated nor obvious in view of prior art of record.
Chhabra teaches a system that inspects a semiconductor device using a KDTree to further evaluate structural aspects on the substrate of said device. It further discloses an evaluation of distances between shapes per [0089]. It determines both a distance of each hole to a centroid and evaluates shapes with respect to other shapes through a nearest neighbor operation. Additionally, as the nearest neighbor operation is an evaluation of distance between points, it is understood to determine a distance value. It also determines shape contours, as is described in [0086]. However, it does not explicitly state that a selection takes place among the plurality of shapes to then be followed by a parsing of KDtree to generate a line. Nor does it determine a distance between edges of selected shapes based on intersections of the aforementioned line.
Watanabe et al. (US publication 20010013867 A1; hereinafter Watanabe) discloses an object search system that is shown to search KDtree data (as shown in Fig. 8). It further generates lines between points within a coordinate area, and thus provides a measure of distance between said points. However, no individual line explicitly is generated between a center of a selected shape and that of a determined nearest neighbor (as required by the claims). Additionally it does not explicitly indicate a determination of distance based on the intersection of a line and the edges of selected shapes based on intersections of the aforementioned line.
Zhang et al. (non-patent literature titled “A Distributed Storage and Computation k-Nearest Neighbor Algorithm Based Cloud-Edge Computing for Cyber-Physical-Social Systems”; hereinafter “Zhang”) discloses the use of K-nearest neighbor determinations. It further shows a graphical representation of distances between shapes in a K-nearest neighbor search in figure 2. However, the lines shown do not go through the centers of the shapes. As such, it also does not further use the line that goes centers of the shapes in order to determine a distance based on intersections of edges.
Additionally, the other known prior art or record do not address all the limitations of the independent claims without the use of impermissible hindsight bias. As such, claims 2-3, 9-13, and 16-17 are neither anticipated nor rendered obvious in view of prior art of record.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KEVIN M COOMBER whose telephone number is (571)270-0950. The examiner can normally be reached Monday - Friday 8:00am-5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Gregory Morse can be reached at (571) 272-3838. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KEVIN M COOMBER/Examiner, Art Unit 2663
/GREGORY A MORSE/Supervisory Patent Examiner, Art Unit 2698
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