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 01/29/2026. In virtue of this communication, claims 1-18 filed on 01/29/2026 are currently pending in the instant application.
Terminal Disclaimer
The terminal disclaimer filed on 01/29/2026 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of the full Statutory term of the US patent application 18/516881 has been reviewed and is accepted. The terminal disclaimer has been recorded.
Response to Arguments
Applicant's arguments filed 01/29/2026 have been fully considered:
- With regard to rejection under 35 USC §112 (b), the rejection is withdrawn in view of amendment filed on 01/29/2026.
- With regard to rejection under Double patenting, the rejection is withdrawn in view of terminal disclaimer filed on 01/29/2026.
- With regard to prior art rejection, the arguments are not persuasive. Please see the response below.
Applicant argued: A single prior art reference must disclose, either expressly or inherently, each and every element of a claimed invention to support a rejection for anticipation. The rejection here is improper because D1 fails to disclose the specific, integrated architecture required by Claim 1.
Claim 1 recites, in part (emphasis added):
"A data capture device comprising:
an imaging assembly configured to capture images...;
one or more processors connected to the imaging assembly;
one or more memories communicatively coupled to the one or more processors; and
computing instructions stored on the one or more memories that, when executed, cause the data capture device to:
...provide generated image data 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,
generate, via the object prediction application, object candidate data..."
The plain language of Claim 1 thus requires a specific and self-contained architecture. The "data capture device" is an integrated system comprising the imaging assembly, processors, and memories. Crucially, the claim mandates that the "computing instructions" for the "object prediction application" are
stored on and executed by the processors and memories that are part of this same singular data capture device.
This interpretation is fully supported by the specification of the present application. The application explicitly describes this integrated structure as a key feature. For example:
Paragraph [0033] of the application states: "The data capture device may include an imaging assembly and/or a prediction controller. In some embodiments, the imaging assembly and the prediction controller may be enclosed in a singular housing and/or share components (e.g., processing elements, memories, etc.)."
Paragraph [0098] further elaborates: "...in some embodiments, the imaging assembly and the prediction controller may be combined into a singular housing... Alternatively, in some embodiments, some components of the imaging assembly and the prediction controller may be the same (e.g., the one or more processors 112 of the imaging device 111 and the one or more processors 122 of the prediction controller 121) such that the combination of the imaging assembly and the prediction controller may be considered a singular device."
These passages clearly establish that the invention as claimed is an integrated device where image capture and object prediction processing occur locally within the same housing and on the same or shared hardware.”
Examiner response: Examiner respectfully disagrees with above characterization of claim, and in response to the Applicant argument asserting that Claim 1 requires a “specific integrated and self-contained architecture” Examiner notes that applicant characterization of claim 1 is not commensurate with the actual claim language. Specifically, the portion of claim 1 reproduced by Applicant is incomplete and omits several material limitations. For example, Claim 1 expressly recites:
“In response to being unable to identify a decodable indicia on the object, provide a generated image data associated…, ”
By omitting these limitations, Applicants isolates only selected portions of the claim directed to an object prediction application and relies on that incomplete characterization to argue that claim 1 requires a particular “self-contained” architecture. However, claim 1, when read a whole, does not impose such as a restriction. Furthermore, Claim 1 does not exclude implementations in which components (e.g. object prediction functionality or host interaction) are distributed or externally supported, so long as the recited functional relationships are satisfied. Applicant argument’s improperly imports additional architectural constraints that are not recited in the claim. Accordingly, Applicant’s argument is not persuasive because it is based on incomplete and inaccurate representation of claim 1.
Furthermore, Applicant argued:
In stark contrast, D1 teaches a fundamentally different, distributed system architecture. D1 does not teach that the "object prediction application" is "deployed on the one or more memories" of the data capture device itself. On the contrary, D1's disclosure points exclusively to a distributed processing model where imaging devices are mere data collectors for a separate computational system:D1, at paragraph [0769],
explicitly states: "Data captured by cameras and other sensors... may be referred to the cloud for analysis, or processing may be distributed between local and cloud resources." This teaches away from the integrated structure required by Claim 1. D1's system does not perform object prediction on the data capture device; it sends the data elsewhere for processing.D1's system is further de
scribed with reference to a "Decision Module" that receives inputs from a multitude of disparate sensors (watermark, barcode, weight, temperature, etc.) and processes them against "Analysis Rules" and "Reference Info." This "Decision Module" is clearly a centralized or networked computational entity, not a processor and memory integrated within the imaging device itself.D1, at paragraph [0770],
further describes a highly networked architecture: "a point-of-sale (POS) station... is networked with a main store computer system, which commonly includes a database system... In turn, the main store computer system is typically networked across the internet, or otherwise, with a corporate data processing system." Nowhere in D1 is it taug
Nowhere in D1 is it taught or suggested that the image processing steps of identifying aspects of the object and generating object candidate data are performed by computing instructions stored on the memory of the same device that comprises the imaging assembly. D1's cameras are sensors in a distributed network; the data capture device of the present invention is an intelligent, self-contained processing unit.
Examiner response: Examiner respectfully disagrees with above mentioned argument. Examiner notes Applicant’s asserts that Claim 1 requires a “self-contained”, locally integrated architecture in which capture and object prediction processing occur within the same housing or hardware. However, Claim 1 does not recite such as a requirement. The claim merely recites that an object prediction application is deployed on one or more memories of the data capture device, without imposing any restriction that processing must be confined to a single housing or exclude distributed implementations. Accordingly, Applicant’s distinction over D1 is based on limitations not present in claim and is therefore unpersuasive.
Claim Rejections - 35 USC § 102
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 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)(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.
Claim(s) 1-18 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Rodriguez et al. (US 2021/0157998.)
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 relationship. ¶[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].)
Claims 7 and 13 have been analyzed for the reasons indicated in claim 1 above.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure of claims 1, 7, and 13.
Wilfred et al. (US 20200193281), please see ¶’s[0051-0053] then ¶’s[0058-0068].
As per claim 2, The data capture device of claim 1, “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, “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: “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, “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, “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.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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.
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, 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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