DETAILED ACTION
This action is in reply to the submission filed on 4/10/2026.
Status of Claims
Applicant’s cancellation of claim 3, amendments to claims 1-2, 4-20, and addition of claim 21 are acknowledged.
Claims 1-2 and 4-21 are currently pending and have been examined.
Request for Continued Examination
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 4/10/2026 has been entered.
Response to Remarks
Applicant's remarks filed 4/10/2026 have been fully considered and have been found not persuasive in full. In response to the amendments and applicant’s remarks concerning subject matter eligibility, the claims are now subject matter eligible, as a meaningfully limiting embodiment of a combination of interconnected hardware elements are used in a specific order to accomplish a specific technological goal relating to computerized object identification. However, the cited prior art reads on the claims, as Barkan teaches the workflow of using one image, then another, to identify an item if the first image is not sufficiently useful. Barkan also teaches multiple imagers. In response to remarks page 28 and 29, Examiner posits Barkan teaches determining a confidence of item identification based on partial barcode decoding, and using second image data alongside image analysis in a neural network to determine item identification. It is further posited that neural networks are comparing stored object identifiers with captured data to determine candidate objects. See Barkan para. 39 showing the trained model with image data representative of data corresponding to the item. This reads on the candidate object identifiers.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that forms the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 4-5, 8, 9, 15, 16, 19 and 21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Barkan (US 2021/0264215 A1).
Claims 1 and 15. Barkan teaches a method, comprising:
by a point of sale (POS) system having a processing circuitry operably coupled to first optical sensor (para. 5 processor; para. 17 of Barkan showing POS with optical sensor) configured to capture image data of objects disposed within a checkout region without requiring alignment of a machine readable code with the sensor, (Abstract, imager FOV over scanning area and items within FOV), a second optical sensor operable to capture an image, (para. 23 showing secondary imager taking pictures of items)
and a network interface, (paragraphs 32 and 33 showing transmission of data through networking interface; paras. 42 and 57 showing server receiving data from imagers on POS) the first optical sensor being operable to analyze the captured image data to detect at least a portion of a visual object identifier pattern including fewer than all characters of a corresponding machine readable code, (para. 22 of Barkan showing optic imager for barcodes; para. 54 showing portion of barcode and one or more character symbols) with each code representing one of a set of stored object identifiers with each stored object identifier being specific to a respective object of a set of objects and represented by a series of characters, (para. 54 showing barcode with characters being identified) and associated with a contextual feature of the respective object that includes a representative object image, (para. 52 showing image recognition with comparative database analysis)
with the first optical sensor being positioned on or about the POS system so that a field of view of the first optical sensor is directed towards a presentation region or the checkout region of the POS system, (para. 5 showing field of view of sensor in scanning area)
obtaining, based on the captured image data, a partial identifier segment from the detected portion of the visual object identifier pattern on a target object and a contextual feature of the target object determined from the image data: (para. 52 showing partial barcode and partial object feature analysis)
determining, from the set of stored object identifiers, those stored object identifiers that correspond to the partial identifier segment to obtain a set of candidate object identifiers; (para. 54 showing identification of object through OCR)
obtaining representative object images of a set of candidate objects corresponding to the set of candidate object identifiers; (paragraphs 38 and 39 showing obtaining candidate object images based on barcode analysis)
responsive to determining that the detected portion of the visual object identifier pattern includes fewer than all characters of the corresponding machine readable code, receiving, from the second optical sensor, second captured image data representing a second captured image of the target object; (para. 26 showing second sensor activated after first; para. 36 showing one camera for barcode recognition and another for object recognition) (para. 45 showing workflow where first image is not successfully useful, then second image is used)
sending, via the network interface, to a network node over a network, an indication that includes a request to identify the target object based on the second captured image data, wherein the network node includes an artificial intelligence circuit trained on image data associated with the set of objects; (paras. 39-42 showing neural network image analysis from one of the sensors)
receiving, via the network interface, from the network node over the network, an indication that includes one or more predicted objects and corresponding confidence levels (para. 46 showing confidence level of item match)
determining the corresponding object identifier by correlating the obtained partial identifier segment, the obtained contextual feature, the representative object images of the set of candidate objects, and the one or more predicted objects and corresponding confidence levels with the set of stored object identifiers so that the target object can be identified even when the visual object identifier code is only partially captured and without requiring complete decoding of the machine readable code; (para. 46 showing identification from a portion of a barcode where the other portion is not scannable; para. 54 showing OCR for character recognition; para. 52 showing partial barcode and partial object feature analysis for object recognition; para. 48 showing list of items with matching features)
and identifying the target object as one of the set of candidate objects based on the corresponding object identifier. (para. 47 showing item identification from candidates of items through image analysis)
Claim 15 additionally: a memory containing instructions executable by processing circuity in the POS. (para. 59 showing processing circuitry and instructions)
Claim 2. Barkan teaches the method of claim 1, wherein the obtaining step includes:
receiving, by the processing circuitry of the POS system, from the first optical sensor, an indication that includes the portion of the series of characters that represents the partial identifier segment. (para. 5 showing a portion of barcode and para. 54 showing barcode character/symbol recognition)
Claims 4 and 19. Barkan teaches the method of claim 1, further comprising:
receiving, by the processing circuitry of the POS system, from a second optical sensor of the POS system, image data that represents a captured image of the target object, (para. 23 showing secondary imager taking pictures of items)
with the second optical sensor being positioned on or about the POS system so that a field of view of the second optical sensor is directed towards the presentation region associated with scanning the visual object identifier codes disposed on the objects; and (para. 23 showing secondary imager positioned above POS station)
identifying the target object based on the set of stored object identifiers, the image data, and the partial identifier segment. (para. 29 showing primary and secondary images transmitted; para. 36 showing object identification using image data and barcode identification; para. 39 showing character recognition)
Claim 5. Barkan teaches the method of claim 4, wherein the identifying step further comprises:
sending, by the POS system, to a network node over a network, (paragraphs 32 and 33 showing transmission of data through networking interface; paras. 42 and 57 showing server receiving data from imagers on POS)
an indication that includes a request to identify the target object based on the image data of the target object, (Para. 38 showing directions for identifying item based on image)
wherein the network node includes an artificial intelligence circuit operable to perform object identification so as to determine an identity of the target object, (para. 39 showing neural network identifier)
with the artificial intelligence circuit being trained on image data associated with the set of objects; (para. 39 showing training network with said image data representative of items)
receiving, by the POS system, from the network node over the network, an indication that includes one or more predicted objects and corresponding confidence levels; and (para. 49 showing identification sent to POS; para. 46 showing identification through threshold confidence)
wherein the identifying step is further based on the received indication, including determining that one of the predicted objects corresponds to one of the set of candidate objects and has a confidence level above a threshold. (Para. 49 showing said identification based on trained object recognition model; para. 46 showing identification through threshold confidence)
Claim 8. Barkan teaches the method of claim 1, further comprising:
determining which of the set of stored object identifiers corresponds to the partial identifier segment. (para. 54 showing identification of object through OCR)
Claim 9. Barkan teaches the method of claim 8, wherein the object identifier determining step further comprises:
determining which of the set of stored object identifiers have the same characters in the same positions as the partial identifier segment. (para. 50 showing position detection of barcode/number on items)
Claim 16. Barkan teaches the POS system of claim 15, wherein the memory includes further instructions executable by the processing circuitry whereby the circuitry is configured to: identifying the target object based on the set of stored object identifiers and the partial identifier segment. (para. 5 showing a portion of barcode and para. 54 showing barcode character/symbol recognition)
Claim 21. The method of claim 1, wherein determining the corresponding object identifier further comprises determining that one of the predicted objects corresponds to one of the set of candidate objects and has a confidence level above a threshold, and wherein identifying the target object comprises identifying the target object as the one of the set of candidate objects that corresponds to the one of the predicted objects having the confidence level above the threshold. (para. 46 showing narrowing of product to a category by confidence level threshold)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 6-7 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Barkan in view of Ron (US 11,620,822).
Claims 6 and 20. Barkan teaches the method of claim 1, further comprising:
identifying the target object based on the partial identifier segment, the set of stored object identifiers, and the weight measurement. (para. 52 showing confirming weight of item alongside object identification, barcode identification; para. 54 showing character recognition)
Barkan does not, but Ron teaches:
receiving, by the processing circuitry of the POS system, from a load sensor of the POS system, an indication that includes a weight measurement of the target object. (Ron column 16, lines 20-27 showing weight sensor data used by system)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system of object identification by weight in Barkan, with the known technique of data collection by load sensor in Ron, because applying the known technique would have yielded predictable results and resulted in an improved system by allowing for accurate data input. (Ron column 16, lines 20-27 showing sensor data used to identify item)
Claim 7. Barkan as modified by Ron teaches the method of claim 6. Barkan does not, but Ron teaches wherein the identifying step further comprises:
determining that the measured weight of the target object corresponds to a range of potential weights associated with one of the set of candidate objects, (Ron column 16, lines 20-27 showing a weight range for an item, and matching measured weight to said range)
wherein each candidate object has a certain range of potential weights; (Ron column 16, lines 20-27 showing a weight range for an item, and matching measured weight to said range)
and identifying the target object as the one of the set of candidate objects having the range of potential weights corresponding to the measured weight. (Ron column 16, lines 20-27 showing a weight range for an item, and matching measured weight to said range for purposes of item identification)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system of object identification by weight in Barkan, with the known technique of data collection by load sensor in Ron, because applying the known technique would have yielded predictable results and resulted in an improved system by allowing for accurate data input. (Ron column 16, lines 20-27 showing sensor data used to identify item)
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Barkan in view of Brunelli (US 6,305,606).
Claim 10. Barkan teaches the method of claim 8. Barkan teaches matching one or more character symbols to an item. Barkan does not, but Brunelli teaches wherein the object identifier determining step further comprises:
determining which of the set of stored object identifiers that have the same characters in the same order as the partial identifier segment. (Column 2, lines 41-61 showing matching of character position)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system of object identification by weight in Barkan, with the known technique of character position matching in Brunelli, because applying the known technique would have yielded predictable results and resulted in an improved system by allowing for barcode identification (Brunelli column 2, lines 41-61 showing character position matching for item identification.)
Claims 13 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Barkan in view of Bachelder (US 2019/0108379).
Claim 13. Barkan teaches the method of claim 1.
Barkan does not, but Bachelder teaches:
wherein the partial identifier segment includes characters at a first position and a last position of the series of characters, (paragraphs 142 and 143 showing first and last character)
with one or more positions between the first and last positions of the series of characters corresponding to unscanned, undetected or undecoded characters of the series of characters. (para. 143 showing undecoded characters within the string portion)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system of object identification by weight in Barkan, with the known technique of character decoding in Bachelder, because applying the known technique would have yielded predictable results and resulted in an improved system by allowing for improved character recognition. (Bachelder para. 142 demonstrating techniques for character recognition.)
Claim 14. Barkan teaches the method of claim 1. Barkan does not, but Bachelder teaches wherein one or more positions that start at a first position or end at a last position of the series of characters corresponds to unscanned, undetected or undecoded characters of the series of characters. (paragraphs 143 and 144 showing undecoded characters being the last character.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system of object identification by weight in Barkan, with the known technique of character decoding in Bachelder, because applying the known technique would have yielded predictable results and resulted in an improved system by allowing for improved character recognition. (Bachelder para. 142 demonstrating techniques for character recognition.)
Claims 11, 12, 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Barkan in view of Forutanpour (US 2022/0114854).
Claims 11 and 17. Barkan teaches the method of claim 1, further comprising:
determining from the set of stored object identifiers, the set of candidate object identifiers corresponding to the partial identifier segment; (para. 54 showing OCR recognition of partial barcode characters on item for item identification)
obtaining the set of candidate objects corresponding to the set of candidate object identifiers; (para. 54 showing determining product type)
identifying the target object as the selected object. (para. 54 showing determining product type)
Barkan does not, but Forutanpour teaches:
outputting, for display on a presence sensitive display of the POS system, (para. 153 showing presence detecting display on POS) a visual representation associated with a request to select one of the set of candidate objects; (paragraphs 123 and 124 showing selection of objects on display)
receiving, from the presence sensitive display, an indication of a touch gesture (para. 64 touchscreen) detected at or about the visual representation associated with one of the set of candidate objects; (paragraphs 123 and 124 showing selection of objects on display)
determining that the detected touch gesture corresponds to one of the set of candidate objects to obtain a selected object. (paragraphs 123 and 124 showing selection of objects on display)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system of object identification by weight in Barkan, with the known technique of touch input in Forutanpour, because applying the known technique would have yielded predictable results and resulted in an improved system by allowing for accurate data input. (Forutanpour para. 153 showing present detection for touchscreen to reduce power consumption.)
Claims 12 and 18. Barkan as modified by Forutanpour teaches the method of claim 11.
Barkan does not, but Forutanpour teaches:
outputting, for display on a presence sensitive display of the POS system, (para. 153 showing presence detecting display on POS) a visual representation associated with a request to verify that the target object is the selected object; (paragraphs 123 and 124 showing selection of objects on display)
receiving, from the presence sensitive display, an indication of a touch gesture (para. 64 touchscreen) detected at or about the visual representation associated with the verification request; (paragraphs 123 and 124 showing selection of objects on display)
determining that the detected touch gesture corresponds to the visual representation associated with the verification request; (paragraphs 123 and 124 showing selection of objects on display)
and confirming that the certain object is the selected object. (Forutanpour para. 124 showing use confirmation of item at checkout)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system of object identification by weight in Barkan, with the known technique of touch input in Forutanpour, because applying the known technique would have yielded predictable results and resulted in an improved system by allowing for accurate data input. (Forutanpour para. 153 showing present detection for touchscreen to reduce power consumption.)
Claim 18 additionally:
determining from the set of stored object identifiers, the set of candidate object identifiers corresponding to the partial identifier segment; (Barkan para. 54 showing OCR recognition of partial barcode characters on item for item identification)
obtaining the set of candidate objects corresponding to the set of candidate object identifiers; (Barkan para. 54 showing determining product type)
identifying the target object as the one of the candidate objects. (Barkan para. 54 showing determining product type)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Aaron Tutor, whose telephone number is 571-272-3662. The examiner can normally be reached Monday through Friday, 9 AM to 5 PM.
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/AARON TUTOR/Primary Examiner, Art Unit 3627
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