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 .
Response to Amendments
2. The Amendment filled 06/11/2026 in response to Non-Final Office Action mailed 12/12/2025 has been entered.
3. Claims 16, and 18-30 are currently pending.
Response to Arguments
4. Applicant’s arguments, see pg. 1, with regard to the objection to the abstract have been fully considered and are persuasive. Specifically, the amended abstract has obviated the objections.
5. Applicant’s arguments, see pg. 2-5, have been fully considered and are persuasive. Specifically, the examiner agrees that Rodriguez does not specifically disclose identifying a marking is incorrect because it is in an incorrect language, because it is not in an intended position on the product, or because it is not machine readable, and selecting said product and marking for correction. With respect to the argument presented on pg. 3, par. 3 with regard to the position identification for markings, the examiner notes that Rodriguez teaches the use of the position of the marking and determines if the position is machine readable, but does not specifically use said information to identify an incorrect marking as taught in the amended claim language. Likewise, with respect to the argument presented on pg. 3, par. 4, as well as pg. 4, par. 5 with regard to the language identification, the examiner notes that the arguments are partially persuasive. Specifically, the examiner notes that while Rodriguez uses OCR to identify text and/or language information present in a marking during a code checking step and compare said information to a reference information database, Rodriguez does not specifically teach identifying the language of the text or wherein if said language is incorrect to identify a marking is incorrect. The examiner used Plant to teach identifying a language of the marking, and notes that pg. 4, par. 5 argues that the rationale to combine would not have been applicable since it is directed toward identifying products or recognized label content and does not explain why a person of ordinary skill would have modified Rodriguez to include a correctness determination for the marking as amended. In this respect, the examiner disagrees. Specifically, the examiner notes that Plant teaches a correctness identification for the marking of a product ([par. 0020, ln. 1-8], [par. 0103, ln. 1-9], [par. 0105, ln. 1-14], [par. 108, ln. 1-10], [par. 0109, ln. 1-19], [par. 0157, ln. 1-4] see 103 rejections for full citations). The examiner specifically notes this is directly analogous to identifying an incorrect marking when the marking is not in an intended position, is not machine readable, or is in an incorrect language; and selecting the incorrect marking for correction. Therefore, the 102 rejections have been withdrawn in view of Rodriguez failing to specifically disclose the above noted limitations. However, upon further consideration, a new grounds of rejection is made in view of Rodriguez, and further in view of Plant. The examiner has re-written and adjusted the mapping to provide clarity in view of the amended claim language.
Claim Rejections - 35 USC § 103
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, 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
7. Claims 16 and 18-30 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Publication No. 2021/0157998 to Rodriguez et al. (hereinafter Rodriguez), and further in view of U.S. Publication No. 2021/0374926 to Plant et al. (hereinafter Plant).
8. Regarding Claim 16, Rodriguez discloses a method for checking a marking of a product, comprising ([par. 0100, ln. 1-15] “…the present technology concerns a method for identifying items, e.g., by a supermarket checkout system… involves moving an item to be purchased along a path, such as by a conveyor. A first camera arrangement captures first 2D image data depicting the item when the item is at a first position along the path. Second 2D image data is captured… at a second position along the path. A programmed computer, or other device, processes the captured image data—in conjunction with geometrical information about the path and the camera—to discern 3D spatial orientation information for a first patch on the item. By reference to this 3D spatial orientation information, the system determines object-identifying information from the camera's depiction of at least the first patch.”, [par. 0102, ln. 1-9] “The object-identifying information can be a machine-readable identifier, such as a barcode or a steganographic digital watermark, either of which can convey a plural-bit payload. This information can… comprise text—recognized by an optical character recognition engine… the product can be identified by other markings, such as by image fingerprint information that is matched to reference fingerprint information in a product database.”):
acquiring, in an image acquisition step, at least one image of the marking arranged on a surface of the product by a calibrated test camera ([Fig. 1A-B], [Fig. 27-29], [par. 0100, ln. 1-15], [par. 0102, ln. 1-9], [par. 0198, ln. 1-5] “Another approach simply characterizes the perspective distortion of the camera across its field of view, in a calibration operation—before use. This information is stored, and later recalled to correct imagery captured during use of the system.”, [par. 0199, ln. 1-11] “One calibration technique places a known reference pattern (e.g., a substrate marked with a one-inch grid pattern) on the conveyor. This scene is photographed by the camera, and the resulting image is analyzed to discern the perspective distortion at each 2D location across the camera's field of view (e.g., for each pixel in the camera's sensor). The operation can be repeated, with the calibrated reference pattern positioned at successively elevated heights above the plane of the conveyor (e.g., at increments of one inch). Again, the resulting imagery is analyzed, and the results stored for later use.”);
checking, in a layout checking step, using the at least one image, a quality of the marking applied to the surface with respect to a position of the marking on the product and machine-readability of the marking ([par. 0170, ln. 1-5] “…The text detector module outputs data indicating that this letter is presented in the image excerpt at an orientation of 202 degrees (FIG. 13).”, [par. 0171, ln. 1-9] “With this as a clue as to the orientation of any embedded watermark, the system next rotates the image excerpt clockwise 158 degrees, so that the “B” is oriented vertically (i.e., 0 degrees), as shown in FIG. 14A. A watermark decode operation is then attempted on this excerpt. The decoder looks for a watermark pattern at this orientation. If unsuccessful, it may further try looking for the watermark pattern at small orientation offsets (e.g., at selected orientation angles +/−8 degrees of the FIG. 14A orientation).”, [par. 0172, ln. 1-4] “If no watermark is found, the system can next rotate the image excerpt a further 270 degrees clockwise, to the orientation depicted in FIG. 14B. Again, the same decode operations can be repeated”, [par. 0173, ln. 1-8] “… if no watermark is then decoded, the system may conclude that there probably is no watermark, and curtail further watermark processing of the image excerpt. Alternatively, it may employ a prior art method to undertake a more exhaustive analysis of the image excerpt to try to find a watermark—considering all possible orientations (e.g., as detailed in the assignee's U.S. Pat. No. 6,590,996).”, [par. 0176, ln. 1-8] “More economical, however, is for the system to rank the different rotation states based on the likelihood of finding a watermark at that orientation state. In the FIG. 15 example, the system ranks the 150 degree rotation as number 1, because this rotation orients the prominent text character “B” most nearly upright. If a watermark is present in the image excerpt, it will most likely be found by examining this number 1-ranked excerpt (again, +/−15 degrees).”, [par. 0178, ln. 1-5] “If no watermark is yet decoded, the system may give up, or it may consider other rotational states (e.g., perhaps ranked number 3 because of the orientation of other detected text). Or, again, it may invoke a prior art method to search for a watermark of any rotational state.”, [par. 0293, ln. 1-18] “Application of the above procedure to the 3D arrangement of FIG. 31 results in a segmented 3D model, such as is represented by FIG. 33. Each object is represented by data stored in memory indicating, e.g., its shape, size, orientation, and position. An object's shape can be indicated by data indicating whether the object is a cylinder, a rectangular hexahedron, etc. The object's size measurements depend on the shape…Orientation can be defined—for a cylinder—by the orientation of its principal axis (in the three-dimensional coordinate system in which the model is defined). For a regular hexahedron, orientation can be defined by the orientation of its longest axis. The position of the object can be identified by the location of an object keypoint. For a cylinder, the keypoint can be the center of the circular face that is nearest the origin of the coordinate system. For a hexahedron, the keypoint can be the corner of the object closest to the origin.”, [par. 0312, ln. 1-10] “The position of a barcode (or other marking) on an object is additional evidence—even if the captured imagery does not permit such indicia to identify the object with certainty. For example, if a hexahedral shape is found to have has a barcode indicia on the smallest of three differently-sized faces, then candidate products that do not have their barcodes on their smallest face can be ruled out—effectively pruning the universe of candidate products, and increasing the confidence scores for products that have barcodes on their smallest faces.”, [par 00538, ln. 1-12] “The first keypoint descriptor from the input image is compared against each of the million or so reference keypoint descriptors in the FIG. 45 data structure. For each comparison, a Euclidean distance is computed, gauging the similarity between the subject keypoint, and a keypoint in the reference data. One of the million reference descriptors will thereby be found to be closest to the input descriptor. If the Euclidean distance is below a threshold value (“A”), then the input keypoint descriptor is regarded as matching a reference keypoint. A vote is thereby cast for the product associated with that reference keypoint, e.g., Kellogg's Rice Crispies cereal.”);
acquiring, using the at least one image recorded using the test camera, in a code checking step variable product information contained in the marking ([par. 0310, ln. 1-14] “A great variety of other information can be used in this manner Consider, for example, that the image of FIG. 31 may reveal identification markings on the cylindrical face of Object 3 exposed in that view. Such markings may comprise, for example, a barcode, or distinctive markings that comprise a visual fingerprint (e.g., using robust local features). A barcode database may thereby unambiguously identify the exposed cylindrical shape as a 10.5 oz. can of Campbell's Condensed Mushroom Soup. A database of product information—which may be the barcode database or another (located at a server in the supermarket or at a remote server)—is consulted with such identification information, and reveals that the dimensions of this Campbell's soup can are 3″ in diameter and 4″ tall.”);
identifying {a language of} the marking in the code checking step by comparing the variable product information with {language} information from a reference information database ([par. 0169, ln. 1-15] “…a segmentation module identifies and extracts the portion of the camera imagery depicting the shaded surface of item 90. (Known 2D segmentation can be used here.) This image excerpt is passed to a text detector module that identifies at least one prominent alphabetic character. (Known OCR techniques can be used.) More particularly, such module identifies a prominent marking in the image excerpt as being a text character, and then determines its orientation, using various rules. (E.g., for capital letters B, D, E, F, etc., the rules may indicate that the longest straight line points up-down; “up” can be discerned by further, letter-specific, rules. The module applies other rules for other letters.) The text detector module then outputs data indicating the orientation of the analyzed symbol.”, [par. 0310, ln. 1-14], [Fig. 47-50], see Auxiliary Info, Logo Print, in database of Fig. 47 which includes OCR identifiable markings, as shown in Figs. 48-50);
{identifying an incorrect marking when the marking is not in an intended position, is not machine readable, or is in an incorrect language; and
selecting the incorrect marking for correction.}
Rodriguez does not specifically disclose wherein language of the marking is determined in the code checking step, or to identify an incorrect marking when the marking is not in an intended position, is not machine readable, or is in an incorrect language; and selecting the incorrect marking for correction.
However, Plant specifically discloses wherein a language of a marking is identified ([par. 0068, ln. 1-2] “Multi-language string variables have their own particular representation in the variables pane.”, [par. 0105, ln. 1-20] “The printed label in its simplest sense might be a product code expressed in alphanumeric text. More usually there will be a mixture of texts, such as branding, use instructions, batch numbers, product numbers, certification marks, manufacture dates, use-by dates, weight values, and so on. The text may be present in the same of different typefaces, font sizes and in different orientations (e.g. horizontal or vertical). Text may be in different languages or may be in non-Latin text, such as Chinese and Japanese symbols or Arabic letters.”, [par. 0161, ln. 1-15] “To generate an inspection mask from this label oblong inspection blocks 18, 19, 20, 21 are applied to the each of the four text, and corner blocks 22, 23 for the label corners (FIG. 4). For each block an inspection wizard script is run. So in FIG. 5 one can see the wizard before being applied to the label. There are a series of dialog boxes that can be viewed or modified once the wizard has been run. There is a switch to invoke use of the wizard. The wizard uses the label format specification to identify the nature of the information in each block (i.e. text, or picture, dithered picture, barcode if present, non-Latin scripts (e.g. Arabic or Chinese). An appropriate inspection tool is then applied for each block (or region). The wizard then applies some pre-determined default parameter values to the various blocks.”), and identifying an incorrect marking when the marking is not in an intended position, is not machine readable, or is in an incorrect language; and selecting the incorrect marking for correction ([par. 0020, ln. 1-8] “…wherein the data processing means includes a label checking module in which there is provided a reference image of the label, and the checking module is configured so that the acquired images are sequentially compared against the reference image according to pre-determined quality control indicators relating to the expected information content and location in the label regions, and wherein the label is flagged for review or rejection if it is non-compliant.”, [par. 0103, ln. 1-9] “A typical system for label checking includes on the one hand a printer which receives label printing instructions from label printing software loaded onto a printer-serving computer… For applications such as medicine or clinical trial labelling it is usually necessary to defect test every printed label in each batch.”, [par. 0105, ln. 1-14] “The printed label in its simplest sense might be a product code expressed in alphanumeric text. More usually there will be a mixture of texts, such as branding, use instructions, batch numbers, product numbers, certification marks, manufacture dates, use-by dates, weight values, and so on. The text may be present in the same of different typefaces, font sizes and in different orientations (e.g. horizontal or vertical). Text may be in different languages or may be in non-Latin text, such as Chinese and Japanese symbols or Arabic letters. There may also be pictures such a product images, trade marks, logos, schematic instructions. The pictures may be monotone, grey scale toned or dithered. The labels may have pre-printed borders, boxes or other images and text.”, [par. 108, ln. 1-10] “In order to address any printing problems which arise, it is important in most embodiments that label checking be carried out ‘on the fly’ and synchronized with the label printing process, so that if a defect is detected the print run can be paused or slowed, or printing parameters adjusted to address the issue causing the defect. In a batch-operated process in which defect detection is separate the batch printing, any defective labels will still be detected, but if any defects prove to be systemic across a proportion of the labels, then considerable wasted time and material will arise.”, [par. 0109, ln. 1-19] “Defects which may occur include drifting of the information from its intended location, scratches, spots or ‘blobs’, missing portions or dots, streaks, banding and intensity variation. The defects may be isolated, or may form a trend which may become worse over time. Some defects may require a label to be rejected whereas others are less critical and can be allowed, or may be used to indicate that printer servicing may be required. However for critical applications such as medical, pharmaceutical or drug trial applications we are primarily concerned with detecting and responding to labels which must be rejected. It must also be recognized that errors can be introduced not just during printing, but also during the scanning process, so scanner alignment, stability and tolerances with respect to the transported print medium (e.g. label arrays) are vital to avoid false results and needless rejections. However printer errors tend to dominate as they are inherently more prone to produce defects (by various modes) than scanners which involve optical interaction.”, [par. 0157, ln. 1-4] “A label is passed or rejected based upon whether the difference (or mismatch) exceeds certain threshold values. For instance missing part of text or image beyond a certain size could be considered as a defect.”). One of ordinary skill in the art, before the effective filling date of the claimed invention, would recognize Rodriguez and Plant as within the same field of scanning for product labeling, and as analogous to the claimed invention. With regard to the incorrect label identification, the motivation to combine would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, in that it allows the product verification of Rodriguez to prevent loss due to mislabeling (e.g., a product that has been marked down may be accidentally labeled incorrectly, once mislabeling is identified flag products on markdown to be checked to verify labels are correct). One of ordinary skill in the art, before the effective filling date of the claimed invention, would also recognize it could likewise serve anti-theft purposes (e.g., shoplifter switches label for similar object, etc.). With regard to the motivation to combine the language identification, one of ordinary skill in the art, before the effective filling date of the claimed invention, would recognize you could expand the applicability and accuracy of the method to more products and/or locations (e.g., foreign goods/stores where the language is different). One of ordinary skill in the art, before the effective filling date of the claimed invention, would have combined the method of Rodriguez to identify multiple languages and incorrect markings as taught in Plant through known means, with no change to their respective function, and the combination would have yielded nothing more than predicable results.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to combine the method of Rodriguez to identify multiple languages and incorrect markings as taught in Plant to obtain the invention as specified in claim 16.
9. Regarding Claim 18, a combination of Rodriguez and Plant teaches the method of claim 16. Rodriguez discloses wherein in the code checking step the product information, encrypted in at least one one-dimensional or multi-dimensional and machine-readable code and/or security element ([Fig. 54-55], [par. 0599, ln. 1-10] “FIG. 54 is a block diagram summarizing signal processing operations involved in embedding and reading a watermark. There are three primary inputs to the embedding process: the original, digitized signal 100, the message 102, and a series of control parameters 104. The control parameters may include one or more keys. One key or set of keys may be used to encrypt the message. Another key or set of keys may be used to control the generation of a watermark carrier signal or a mapping of information bits in the message to positions in a watermark information signal.”, [par. 0602, ln. 1-12] “The watermark embedding process 106 converts the message to a watermark information signal. It then combines this signal with the input signal and possibly another signal (e.g., an orientation pattern) to create a watermarked signal 108. The process of combining the watermark with the input signal may be a linear or non-linear function. Examples of watermarking functions include: S*=S+gX; S*=S(1+gX); and S*=S e.sup.gX; where S* is the watermarked signal vector, S is the input signal vector, and g is a function controlling watermark intensity. The watermark may be applied by modulating signal samples S in the spatial, temporal or some other transform domain.”), is acquired and is compared by a comparison of the variable product information with reference product information from the reference information database ([par. 0102, ln. 1-9], [par. 0310, ln. 1-14]). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to combine the method of Rodriguez to identify multiple languages and incorrect markings as taught in Plant to obtain the invention as specified in claim 18.
10. Regarding Claim 19, a combination of Rodriguez and Plant teaches the method of claim 16. Rodriguez further discloses in the image acquisition step, UV light, and/or visible light, and/or infrared light is directed by a light source onto the marking and is reflected thereby, is acquired by the test camera, and processed in a data processing facility ([par. 0132, ln. 1-8] “One particular implementation illuminates the items with a repeating sequence of three colors: white, infrared, and ultraviolet. Each color is suited for different purposes. For example, the white light can capture an overt product identification symbology; the ultraviolet light can excite anti-counterfeiting markings on genuine products; and the infrared light can be used to sense markings associated with couponing and other marketing initiatives.”, [par. 0310, ln. 1-14] see “…A database of product information… located at a server in the supermarket or at a remote server… is consulted with such identification information…”). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to combine the method of Rodriguez to identify multiple languages and incorrect markings as taught in Plant to obtain the invention as specified in claim 19.
11. Regarding Claim 20, a combination of Rodriguez and Plant teaches the method of claim 19. Rodriguez further discloses wherein in the image acquisition step, the at least one image recorded by the test camera is stored in a storage device of the data processing facility ([Fig. 47], [par. 0310, ln. 1-14]). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to combine the method of Rodriguez to identify multiple languages and incorrect markings as taught in Plant to obtain the invention as specified in claim 20.
12. Regarding Claim 21, a combination of Rodriguez and Plant teaches the method of claim 20. Rodriguez discloses wherein {in a reference database generation step, a target layout comprising a desired static data of the marking is generated}, wherein the target layout is stored in the reference information database ([par. 0102, ln. 1-9], [par. 0310, ln. 1-14]), wherein the static data of the target layout are compared with the static data of an actual layout ([par. 0310, ln. 1-14], [par. 0310, ln. 14-27] “In this case, the model segmentation depicted in FIG. 33 is known to be wrong. The cylinder is not 8″ tall. The model is revised as depicted in FIG. 36. The certainty score of Object 3 is increased to 100, and a new, wholly concealed Object 6 is introduced into the model. Object 6 is assigned a certainty score of 0—flagging it for further investigation. (Although depicted in FIG. 36 as filling a rectangular volume below Object 3 that is presumptively not occupied by other shapes, Object 6 can be assigned different shapes in the model.) For example, Objects 1, 2, 3, 4 and 5 can be removed from the volumetric model, leaving a remaining volume model for the space occupied by Object 6 (which may comprise multiple objects or, in some instances, no object).”, [par. 0312, ln. 1-10] “The position of a barcode (or other marking) on an object is additional evidence—even if the captured imagery does not permit such indicia to identify the object with certainty. For example, if a hexahedral shape is found to have has a barcode indicia on the smallest of three differently-sized faces, then candidate products that do not have their barcodes on their smallest face can be ruled out—effectively pruning the universe of candidate products, and increasing the confidence scores for products that have barcodes on their smallest faces.”, [par. 0313, ln. 1-5] “Similarly, the aspect ratio (length-to-height ratio) of barcodes varies among products. This information, too, can be sensed from imagery and used in pruning the universe of candidate matches, and adjusting confidence scores accordingly.”, [par. 0316, ln. 1-15] “…different segmented shapes can be refined by reference to other sensor data, consider weight data. Where the weight of the pile can be determined (e.g., by a conveyor or cart weigh scale), this weight can be analyzed and modeled in terms of component weights from individual objects—using reference weight data for such objects retrieved from a database. When the weight of the identified objects is subtracted from the weight of the pile, the weight of the unidentified object(s) in the pile is what remains. This data can again be used in the evidence-based determination of which objects are in the pile. (For example, if one pound of weight in the pile is unaccounted for, items weighing more than one pound can be excluded from further consideration.)”), and wherein a correspondence characteristic is determined by the comparison ([par. 0310, ln. 1-27] see “The certainty score of Object 3 is increased to 100” following size comparisons, [par. 0312, ln. 1-10], [par. 0316, ln. 1-15]). Rodriguez does not specifically disclose wherein in a reference database generation step, a target layout comprising a desired static data of the marking is generated.
However, Plant teaches a reference database generation step, a target layout comprising a descried static data of the marking is generated ([par. 0025, ln. 1-9] “…the reference image is an e-image constructed from information which is used to instruct the printer when printing the label. By using the image sent to the printer to construct (or same process to construct) the reference e-image the degree to which variation between expected printed image and obtained scanned image can vary is greatly limited. However, alternatively, the reference image may be obtained as an imported image of an exemplar compliant label.”). One of ordinary skill in the art, before the effective filling date of the claimed invention, would recognize Rodriguez and Plant as within the same field of product labeling, and as analogous to the claimed invention. The motivation to combine would have been obvious to one of ordinary skill in the art, and is disclosed in Plant, wherein by constructing the reference database to comprise a target layout comprising a desired static data of the marking you prevent variation between the actual and target layout from increasing unnecessarily ([par. 0025, ln. 1-9]). One of ordinary skill in the art, before the effective filling date of the claimed invention, would have combined the method of Rodriguez with the reference database generation of Plant through known means, with no change to their respective function, and the combination would have yielded nothing more than predicable results.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to combine the method of Rodriguez to identify multiple languages and incorrect markings as taught in Plant to obtain the invention as specified in claim 21.
13. Regarding Claim 22, a combination of Rodriguez and Plant teaches the method of claim 21. Rejections analogous to claim 21 are further applicable to claim 22. Specifically, Rodriguez discloses in the image acquisition step, the actual layout is generated from the static data from the at least one image, wherein the actual layout is stored in the storage device ([par. 0293, ln. 1-18], [par. 0312, ln. 1-10]), and wherein the static data of the actual layout are compared with the static data of the target layout, wherein a correspondence characteristic is determined by the comparison ([par. 0310, ln. 1-27], [par. 0312, ln. 1-10], [par. 0316, ln. 1-15]). Specifically, one of ordinary skill in the art, before the effective filling date of the claimed invention, would recognize that the image acquired by Rodriguez would be the actual layout to be compared to the reference data comprising the target layout as generated in the reference database generation of Plant. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to combine the method of Rodriguez to identify multiple languages and incorrect markings as taught in Plant to obtain the invention as specified in claim 22.
14. Regarding Claim 23, a combination of Rodriguez and Plant teaches the method of claim 21. Rodriguez further discloses wherein the target layout is a generated edge model of the desired marking, wherein the target layout is stored in the reference information database as an edge model ([par. 0180, ln. 1-8] “Another implementation functions without regard to the presence of text in the imagery. Referring to FIG. 16, the system passes the segmented region to an edge finding module, which identifies the longest straight edge 98 in the excerpt… The angle of this line serves as a clue to the orientation of any watermark.”, [par. 0290, ln. 1 to par. 0292, ln.15] “Geometrical rules are applied to identify faces that form part of the same object. For example, as shown in FIG. 31A, if edges A and B are parallel, and terminate at opposite end vertices (I, II) of an edge C—at which vertices parallel edges D and E also terminate, then the region between edges A and B is assumed to be a surface face that forms part of the same object as the region (surface face) between edges D and E…. some rules take precedence over others. Consider edge F in FIG. 32. Normal application of the just-stated rule would indicate that edge F extends all the way to the reference plane. However, a contrary clue is provided by parallel edge G that bounds the same object face (H). Edge G does not extend all the way to the reference plane; it terminates at the top plane of “Object N.” This indicates that edge F similarly does not extend all the way to the reference plane, but instead terminates at the top plane of “Object N.” This rule may be stated as: parallel edges originating from end vertices of an edge (“twin edges”) are assumed to have the same length. That is, if the full length of one edge is known, a partially-occluded twin edge is deduced to have the same length.”, [par. 0293, ln. 1-18], [par. 0310, ln. 1-27]). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to combine the method of Rodriguez to identify multiple languages and incorrect markings as taught in Plant to obtain the invention as specified in claim 23.
15. Regarding Claim 24, a combination of Rodriguez and Plant teaches the method of claim 21. Rejections analogous to claim 22 and 23 are further applicable to claim 24. Specifically, Rodriguez discloses wherein the actual layout is an edge model, wherein the edge model is generated from the at least one image recorded in the image acquisition step ([par. 0180, ln. 1-8], [par. 0290, ln. 1 to par. 0292, ln.15], [par. 0293, ln. 1-18], [par. 0310, ln. 1-27]), and wherein the actual layout is stored in the storage device as an edge model for comparison with the target layout ([par. 0293, ln. 1-18], [par. 0310, ln. 1-27]). Specifically, one of ordinary skill in the art, before the effective filling date of the claimed invention, would recognize that the image acquired by Rodriguez would be the actual layout to be compared to the reference data comprising the target layout as generated in the reference database generation of Plant. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to combine the method of Rodriguez to identify multiple languages and incorrect markings as taught in Plant to obtain the invention as specified in claim 24.
16. Regarding Claim 25, a combination of Rodriguez and Plant teaches the method of claim 16. Rodriguez further discloses wherein based on a correspondence characteristic determined in the layout checking step, in an event of a value of the correspondence being exceeded ([par. 0024, ln. 1-6], [par. 0536, ln. 1, to par. 0546, ln. 12], [par. 0610, ln. 1-10]), at least one product information region is cut out of the actual layout ([par. 0606, ln. 1-11] “The watermark detector 110a operates on a digitized signal suspected of containing a watermark. As depicted generally in FIG. 54, the suspect signal may undergo various transformations 112a, such as conversion to and from an analog domain, cropping, copying, editing, compression/decompression, transmission etc. Using parameters 114 from the embedder (e.g., orientation pattern, control bits, key(s)), it performs a series of correlation or other operations on the captured image to detect the presence of a watermark. If it finds a watermark, it determines its orientation within the suspect signal.”, [par. 0618, ln. 1-16] “Consider an example where the watermark is defined in a transform domain (e.g., a frequency domain such as DCT, wavelet or DFT). The embedder segments the image in the spatial domain into rectangular tiles and transforms the image samples in each tile into the transform domain. For example in the DCT domain, the embedder segments the image into N by N blocks and transforms each block into an N by N block of DCT coefficients. In this example, the assignment map specifies the corresponding sample location or locations in the frequency domain of the tile that correspond to a bit position in the raw bits. In the frequency domain, the carrier signal looks like a noise pattern. Each image sample in the frequency domain of the carrier signal is used together with a selected raw bit value to compute the value of the image sample at the location in the watermark information signal.”, [par. 0619, ln. 1-12] “Now consider an example where the watermark is defined in the spatial domain. The embedder segments the image in the spatial domain into rectangular tiles of image samples (i.e. pixels). In this example, the assignment map specifies the corresponding sample location or locations in the tile that correspond to each bit position in the raw bits. In the spatial domain, the carrier signal looks like a noise pattern extending throughout the tile. Each image sample in the spatial domain of the carrier signal is used together with a selected raw bit value to compute the value of the image sample at the same location in the watermark information signal.”), wherein in the code checking step, the variable product information of the at least one product information region is checked ([par. 0102, ln. 1-9], [par. 0310, ln. 1-14], [par. 0618, ln. 1-16], [par. 0619, ln. 1-12]). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to combine the method of Rodriguez to identify multiple languages and incorrect markings as taught in Plant to obtain the invention as specified in claim 25.
17. Regarding Claim 26, the claim language is directly analogous to claim 1 with the exception of “A device…” and “…wherein the device comprises a digital data processing facility which is designed such that…”. Rodriguez further discloses a device and a data processing facility which is designated such that it performs the functions of the method of Rodriguez ([Fig. 73] see computer 1220, camera scanner 1243, remote computer 1249, [par. 0100, ln. 1-15] see “…A programmed computer…”, [par. 0310, ln. 1-14], [par. 0755, ln. 1-10] “The computer 1220 operates in a networked environment using logical connections to one or more remote computers, such as a remote computer 1249. The remote computer 1249 may be a server, a router, a peer device or other common network node, and typically includes many or all of the elements described relative to the computer 1220, although only a memory storage device 1250 has been illustrated in FIG. 73…”). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to combine the device and data processing facility of Rodriguez to identify multiple languages and incorrect markings as taught in Plant to obtain the invention as specified in claim 26.
18. Regarding Claim 27, a combination of Rodriguez and Plant teaches the device of claim 26. Rodriguez further discloses wherein the data processing facility comprises a storage device that is connected to the test camera, wherein, in the image acquisition step, images recorded by the test camera can be stored in the storage device ([Fig. 73] see computer 1220, camera scanner 1243, remote computer 1249, [par. 0100, ln. 1-15] see “…A programmed computer…”, [par. 0310, ln. 1-14], [par. 0755, ln. 1-10]). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to combine the device and data processing facility of Rodriguez to identify multiple languages and incorrect markings as taught in Plant to obtain the invention as specified in claim 27.
19. Regarding Claim 28, a combination of Rodriguez and Plant teaches the device of claim 27. Rodriguez further discloses wherein the data processing facility comprises a database, wherein the database is connected for signal transmission to the storage device, such that images stored in the storage device can be matched with images of the database ([Fig. 73] see computer 1220, camera scanner 1243, remote computer 1249, [par. 0100, ln. 1-15] see “…A programmed computer…”, [par. 0310, ln. 1-14], [par. 0755, ln. 1-10]). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to combine the device and data processing facility of Rodriguez to identify multiple languages and incorrect markings as taught in Plant to obtain the invention as specified in claim 28.
20. Regarding Claim 29, a combination of Rodriguez and Plant teaches the device of claim 26. Rodriguez further discloses wherein the test camera comprises at least one light source, by means of which the marking to be checked can be illuminated ([par. 0132, ln. 1-8], [par. 0388, ln. 1-4] “The unit may also include an illumination source (e.g., a visible, IR, or UV LED) which is activated during a period of image capture (e.g., a thirtieth of a second, every 5 minutes) to assure adequate illumination.”). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to combine the device and data processing facility of Rodriguez to identify multiple languages and incorrect markings as taught in Plant to obtain the invention as specified in claim 29.
21. Regarding Claim 30, a combination of Rodriguez and Plant teaches the device of claim 26. Rodriguez further discloses wherein the test camera is designed to detect ultraviolet light and/or visible light and/or infrared light ([par. 0132, ln. 1-8], [par. 0363, ln. 1-9] “…the Kinect sensor does not rely on feature extraction or feature tracking. Instead, it employs a structured light scanner (a form of range camera) that works by sensing the apparent distortion of a known pattern projected into an unknown 3D environment by an infrared laser projector, and imaged by a monochrome CCD sensor. From the apparent distortion, the distance to each point in the sensor's field of view is discerned.”, [par. 0365, ln. 1-10] “In Kinect-related embodiments of the present technology, the sensor typically is not moved. Its 6DOF information is fixed. Instead, the items on the checkout conveyor move. Their motion is typically in a single dimension (along the axis of the conveyor), simplifying the volumetric modeling. As different surfaces become visible to the sensor (as the conveyor moves), the model is updated to incorporate the newly-visible surfaces. The speed of the conveyor can be determined by a physical sensor, and corresponding data can be provided to the modeling system.”). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to combine the device and data processing facility of Rodriguez to identify multiple languages and incorrect markings as taught in Plant to obtain the invention as specified in claim 30.
Conclusion
22. Applicant's amendment necessitated the new grounds of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
Inquiry
23. The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. See PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAULO ANDRES GARCIA whose telephone number is (703)756-5493. The examiner can normally be reached Mon-Fri, 8-4:30PM ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chan Park can be reached on (571)272-7409. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PAULO ANDRES GARCIA/Examiner, Art Unit 2669 /CHAN S PARK/Supervisory Patent Examiner, Art Unit 2669
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