Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This action is in response to the applicant's communication filed on 08/24/2026 In virtue of this communication, claims 1-18 filed on 08/24/2026 are currently pending in the instant application.
Claim 1, 7, 12, 13, and 18 have been amended without adding a new subject matter.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 08/24/2026 has been entered.
Response to Arguments
Applicants’ arguments filed 08/24/2026 have been fully considered:
- With regard to prior art rejection, the arguments are moot in view of new ground of rejection necessitated by amendment filed on 08/24/2026.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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.
Claim(s) 1-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over
Rodriguez et al. (US 2021/0157998), further in view of Pinkus (US 2021/0073776).
As per claim 1, A data capture device comprising:“ an imaging assembly configured to capture images over one or more fields of view;”( Rodriguez, Figure 3A-B and figure 5 and related paragraphs. Show different assembly of cameras. ¶[0196] discloses one approach uses image segmentation to identify different items in the field of view. Most physical items are characterized by perpendicular edges (e.g., a cereal box is a rectangular cuboid; a can is a right cylinder). The edges discerned from the segmented imagery are examined to determine if any pair of edges is nearly parallel or nearly perpendicular (i.e., within, e.g., 20, 10 or 5 degrees or less). The physical edges to which these depictions correspond can be assumed to be truly parallel or perpendicular, with the angular variance in the depicted image due to perspective distortion. A corrective perspective transformation is then applied to restore these edges to parallel or perpendicular relationship.)
“one or more processors connected to the imaging assembly;”( Rodriguez , ¶[0434]) “ one or more memories communicatively coupled to the one or more processors;”( Rodriguez ,¶[0746])
“and computing instructions stored on the one or more memories that, when executed, cause the data capture device to: capture, via the imaging assembly, images of an object in one or more fields of view, wherein the data capture device decodes indicia on objects in image data and the data capture device transmits the decoded indicia data to a host device over a scanner terminal of the host device”( Rodriguez, ¶[0016] discloses a variety of recognition technologies are used at a checkout station—looking for different indicia of product identification (watermark, barcode, color histogram, weight, temperature, etc.). The system applies a set of rules to the collected evidence, and outputs a product identification based on the available information. ¶[0125] discloses The distance along the conveyor can be determined by reference to the difference in times at which the images of FIGS. 1A and 1B are captured, if the conveyor velocity is uniform and known. As noted, the belt may be provided with markings by which its movement alternatively can be determined. (The markings can be promotional in nature, e.g., Tony the Tiger, sponsored by Kellogg's.) In still other embodiments, a conveyor is not used. Instead, the item is moved past the camera by hand. In such case, the distance and other path parameters can be estimated by feature tracking, from features in the captured imagery. Alternatively, a structured light scanning arrangement can be employed. ¶ [0143] discloses thus, in this exemplary embodiment, the plenoptic information captured by camera 50 is processed to yield a multitude of different focal planes of image information, slicing the hemispherical volume with planes every three inches, and at every 15 degrees. The resulting sets of image information are then analyzed for product identification information (e.g., by applying to watermark decoder, barcode decoder, fingerprint identification module, etc.). Depending on the location and orientation of the item surfaces within the examined volume, different of these planes can reveal different product identification information.)
“ in response to being unable to identify a decodable indicia on the object, provide generated image data associated with the images to an object prediction application deployed on the one or more memories, identify, via the object prediction application, one or more aspects of the object,”( Rodriguez, ¶ [0152] discloses FIG. 8 shows a checkout conveyor 14 carrying various items for purchase, from the perspective of an illustrative imaging camera. The items are arranged on the conveyor in such a manner that item 80 is largely obscured. Its position may be such that no barcode is ever visible to any camera as the item passes along the conveyor, and its visible surfaces may be too small to enable object recognition based on other technologies, such as image fingerprinting or digital watermarking. ¶ [0196] discloses one approach uses image segmentation to identify different items in the field of view. Most physical items are characterized by perpendicular edges (e.g., a cereal box is a rectangular cuboid; a can is a right cylinder). The edges discerned from the segmented imagery are examined to determine if any pair of edges is nearly parallel or nearly perpendicular (i.e., within, e.g., 20, 10 or 5 degrees or less). The physical edges to which these depictions correspond can be assumed to be truly parallel or perpendicular, with the angular variance in the depicted image due to perspective distortion. A corrective perspective transformation is then applied to restore these edges to parallel or perpendicular relationships. ¶[0251] discloses by color histogram analysis, the system may make a tentative identification of an item as, e.g., a six-pack of Coke. With this tentative identification, the system can obtain—from the database—information about the configuration of such product, and can use this information to discern the pose or orientation of the product as depicted in the camera imagery. This pose information may then be passed to a digital watermark decoding module. Such information allows the watermark decoding module to shortcut its work (which typically involves making its own estimation of spatial pose). Further see ¶[0316] and [0611] discloses when the original signal is unavailable, the reader can estimate or predict the original signal based on properties of the watermarked signal. The original or predicted version of the original signal can then be used to recover an estimate of the watermark message..)
“generate, via the object prediction application, object candidate data corresponding to the object from the identification, wherein the object prediction application is deployed on the one or more memories and the object prediction application is configured to generate object candidate data corresponding to one or more objects detected within captured images”( Rodriguez, ¶[0313] discloses 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.¶[0370] discloses this module can rely on reference information about products in the store's inventory, stored in a database or other data structure. It can likewise rely on analysis rules, stored in similar fashion. These rules may cause the module to accord the different input information with different evidentiary weight, depending on circumstances and candidate item identifications. ¶[0635] discloses there may be several candidates with a promising measure of correlation. These candidates may be subjected to one or more additional correlation stages to select the one that provides the best match.)
“generate object identifier data for each object candidate in the object candidate data”( Rodriguez, ¶[0227] discloses the system then iterates from that starting point trying lines at increasing distances either side of the assumed center line 148, in an attempt to extract an item identifier. ¶[0374] discloses such a system may be self-learning. A new product may be recognized, initially, by an express identifier, such as a watermark or a barcode. Through repeated exposure, the system collects information about image fingerprints, weights, color histograms, temperature, etc., that it associates with such product. Later, the system becomes able to recognize the item even without reference to the original identifier. In some staged recognition systems, data from one stage of the analysis is used in determining an order of a later part of the analysis. For example, information captured in the first stage of analysis (e.g., color histogram data) may indicate that the item is probably a carton of Diet Coke product, but may leave uncertain whether it is a 6-pack or a 12-pack. This interim result can cause the analysis next to consider the item weight. If the item weighs between 9 and 10 pounds, it can be identified as highly likely to be a 12-pack carton of Diet Coke. If the item weighs half that amount, it can be identified as highly likely to be a 6-pack. (If it weighs less than 4.5 pounds, the initial identification hypothesis is strongly refuted.))
“and transmit one or more of (i) the object candidate data or (ii) the object identifier data to the host device over the scanner terminal of the host device.” (Rodriguez, ¶[0443] discloses in which the shopper and the clerk both simultaneously present items for identification (e.g., to one or more scanners). ¶[0454] discloses thereafter, any such produce is presented for checkout by a shopper, one or more sensors at the checkout station repeats the sensing operation. The collected data is checked against the reference data earlier collected to identify a best match. If the produce is unambiguously identified, it is added to the checkout tally without further intervention (except, perhaps, weighing). If the sensed signature appears to potentially correspond to several reference items, tiles for each possible are presented on the clerk's touch panel, for selection among the presented options. Further, ¶[0637] When there are several viable candidates, the detector can select a set of the top candidates and apply an additional correlation stage. Each candidate has a corresponding rotation and scale parameter. The correlation stage rotates and scales the FFT of the orientation pattern and performs a matching operation with the rotated and scaled pattern on the FFT of the target image. The matching operation multiplies the values of the transformed pattern with sample values at corresponding positions in the target image and accumulates the result to yield a measure of the correlation. The detector repeats this process for each of the candidates and picks the one with the highest measure of correlation. As shown in FIG. 59, the rotation and scale parameters (614) of the selected candidate are then used to find additional parameters that describe the orientation of the watermark in the target image. further see ¶[0812].)
However Rodriguez does not explicitly disclose the following which would have been obvious in view of Pinkus from similar filed of endeavor “wherein generating the object identifier data comprises converting an object identifier of the object candidate data, as assigned by the data capture device, into an object identifier in a format readable by a register log application executing on the host device”(Pinkus, ¶[0012], discloses a point of sale intermediation system and scanning device scan an electronic machine-readable text such as a UPC-A barcode, QR code, PDF417 or similar encoded or non-encoded code or text, and in turn produce an electronic scanned text data output representing the machine-readable text.¶[0015], then ¶[0016] discloses The data manager 32 may then utilize or manipulate the data in the form of the barcode scanned text data output for other purposes. For example, the barcode scanned text data output may arrive as a. complex two-dimensional barcode (QR graphic scan), which is a machine-readable text or data that is not understood by the terminal, wherein the data manager may then convert the two-dimensional barcode text data output to a simple 1D (one dimensional) graphic scan (barcode scan) as the modified barcode scanned text data output, which is a machine-readable text or data that is understood by the terminal.¶[0017] discloses data manager may use the product coder represented by the barcode scanned data output, modify that output and send the modified barcode data to the terminal. ¶[0018] discloses Once the terminal 14 receives the modified barcode scanned text data output, it utilizes the modified barcode scanned text data output in conventional fashion, such as adding the product code, price, etc. to the list of items in the “basket” or total. ¶[0022] discloses The data manager converts the complex decoded value to a 1D decoded value recognized by the terminal, transmitting that converted value to the terminal, adding it to the shopping cart, and ultimately recording the required data in the sale terminal software. ¶[0025]. )
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine Pinkus technique of converting scanned codes to detect items into Rodriguez technique to provide the known and expected uses and benefits of Pinkus technique over identifying object technique of Rodriguez. The proposed combination would have constituted a mere arrangement of old elements with each performing their known function, the combination yielding no more than one would expect from such an arrangement.
Therefore, it would have been obvious to a person of ordinary skill in the art to incorporate Pinkus into Rodriguez in order to accurately track and adjust inventory and perform management. (Refer to Pinkus paragraph [0003].)
Claims 7 and 13 have been analyzed for the reasons indicated in claim 1 above.
As per claim 2, The data capture device of claim 1, Rodriguez as modified by Pinkus further discloses “further comprising an electronic weight scale connected to the imaging assembly, and wherein the computing instructions further cause the data capture device to: detect, via the electronic weight scale, a change in weight of a display area, wherein the images are captured in response to the electronic weight scale detecting the change in weight, .”( Rodriguez, ¶[0316] discloses of how the system's assessments about the 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. ¶[0457])
“the imaging assembly has a field of view of the display area” (Rodriguez, ¶[0199] discloses the 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).
Claims 8 and 14 have been analyzed for the reasons indicated in claim 2 above.
As per claim 3, The data capture device of claim 1, Rodriguez as modified by Pinkus further discloses “wherein the object prediction application is further configured to: generate a confidence score for each object candidate in the object candidate data,” (Rodriguez, ¶[0302] discloses the confidence metric can be based, at least in part, on statistical data about the different products offered for sale in the supermarket. This statistical data can include dimensional information, as well as other data such as historical sales volumes per item. (If the supermarket sells 100 cans of Pringles potato chips in a month, and 2000 cans of Campbell's soup, then the confidence score for Object 3 will be lower than if the sales volumes for these items were reversed.))
“ determine a greatest confidence score exceeds a second greatest confidence score by a threshold amount,”( Rodriguez, ¶[0305] discloses the uncertainty zone shown in FIG. 34, which is brought to the attention to the human clerk (or other system component), can be threshold-defined, using the computed confidence metric. For example, if Object 3 has a confidence metric of 20 (on a scale of 1-100), and if Objects 1, 2, 4 and 5 have confidence metrics of 97, 80, 70 and 97, respectively, then the uncertainty zone is as depicted in FIG. 34 if the threshold is set to highlight uncertainty zones associated with objects having confidence metrics less than 50. )
“and generate determined object candidate data corresponding to the object candidate with the greatest confidence score, wherein object identifier data of the determined object candidate data is generated and the object identifier data of the determined object candidate data is transmitted to the host device.”( Rodriguez, ¶ [0346] discloses by such an arrangement, collected evidence is used to refine the confidence scores of the different objects seen, or deduced to be, presented for checkout, until all are identified within a given certainty (e.g., in excess of 99.99%). After all evidence is considered, any object(s) not identified with such accuracy is indicated for manual examination by a clerk, or is mechanically diverted from the pile for further evidence collection (e.g., by imaging, weighing, etc.).)
Claims 9 and 15 have been analyzed for the reasons indicated in claim 3 above.
As per claim 4, The data capture device of claim 1, wherein: Rodriguez as modified by Pinkus further discloses “a combination of the one or more processors and the one or more memories is separately housed from the imaging assembly and the imaging assembly is communicatively connected to the host device via the scanner terminal.”( Rodriguez, ¶[0752] discloses a digital camera or scanner 43 may be used to capture the target image for the detection process described above. The camera and scanner are each connected to the computer via a standard interface 44. Currently, there are digital cameras designed to interface with a Universal Serial Bus (USB), Peripheral Component Interconnect (PCI), and parallel port interface. Two emerging standard peripheral interfaces for cameras include USB2 and 1394 (also known as firewire and iLink). ¶[0755] discloses the computer 1220 operates in a networked environment using logical connections to one or more remote computers, such as a remote computer 1249.)
Claims 10 and 16 have been analyzed for the reasons indicated in claim 4 above.
As per claim 5, The data capture device of claim 1, Rodriguez as modified by Pinkus further discloses “wherein: a combination of the one or more processors and the one or more memories is separately housed from the imaging assembly and the combination of the one or more processors and the one or more memories is communicatively connected to the host device via the scanner terminal.” (Rodriguez, ¶[0128] discloses This arrangement includes a first camera looking up through a glass window 32 in a checkout counter 33, and a second camera looking across the checkout counter through a window 34 in a vertical housing. The two cameras are positioned so that their camera axes intersect at right angles.¶[0380] discloses A camera/illuminator in a lid of such a container can apply object recognition techniques to visually distinguish different products (e.g., popcorn, sugar, nuts, flour, etc.). Existing containers may be retro-fit with sensor-equipped lids. Such devices can be self-powered (e.g., by battery), or energized based on parasitic excitation from another source. Such devices wirelessly communicate with other such devices, or with a computer, via a mesh or other network. ¶[0756] discloses when used in a LAN networking environment, the computer 1220 is connected to the local network 1251 through a network interface or adapter 1253. When used in a WAN networking environment, the computer 1220 typically includes a modem 1254 or other means for establishing communications over the wide area network 1252, such as the Internet. The modem 1254, which may be internal or external, is connected to the system bus 1223 via the serial port interface 1246.)
Claims 11 and 17 have been analyzed for the reasons indicated in claim 5 above.
As per claim 6, The data capture device of claim 1, Rodriguez as modified by Pinkus further discloses “wherein: the imaging assembly is housed in a same housing as the one or more processors and the one or more memories and the data capture device is communicatively connected to the host device via the scanner terminal.” (Rodriguez, ¶[0672] discloses for some applications, the detector will operate in a system that provides multiple image frames of a watermarked object. One typical example of such a system is a computer equipped with a digital camera. In such a configuration, the digital camera can capture a temporal sequence of images as the user or some device presents the watermarked image to the camera. ¶[0774] discloses laser scanners used in supermarket checkouts are specialized, expensive devices. In contrast, certain embodiments of the present technology use mass-produced, low-cost cameras of the sort popular in HD video chat applications. Further see ¶[0812].)
Claims 12 and 18 have been analyzed for the reasons indicated in claim 6 above.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAGHAYEGH AZIMA whose telephone number is (571)272-1459. The examiner can normally be reached Monday-Friday, 9:30-6:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vincent Rudolph can be reached at (571)272-8243. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHAGHAYEGH AZIMA/Examiner, Art Unit 2671
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