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 06/08/2026. In virtue of this communication, claims 1-20 filed on 06/08/2026 are currently pending in the instant application.
Claims 1, 2, 4, 5, 7, 8, 9-10, 13, and 20 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 03/09/2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered:
With regard to 112 rejection the rejections are withdrawn in view of the amendments.
With regard to 101 rejection the rejection is withdrawn in view of the amendments and arguments.
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) 1-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Carter et al. (US 2021/0287013), further in view of Glaser et al.(US 2021/0150618).
As per claim 1, A system for detecting the number of articles, the system comprising: memory; a processor; and “a camera, the camera being configured to capture an image of articles in a shopping container,” (Carter, ¶[0075] discloses the shopping cart 30 may include a camera or camera module capable of imaging the contents of the basket. ¶[0076] discloses An item addition event can trigger capture of an image of the art content. )
“the processor being coupled to the memory and the camera,”(Carter, ¶[0078] discloses the camera module 217 includes, an imager 217B, an image pre-processor 217C, a control processor or MCU 217D, a wireless transceiver 217E, a battery 217F, and memory. Further see ¶[0103].)
“the processor being configured to: obtain the image of the articles in the shopping container from the camera;” (Carter, ¶[0079] discloses When a likely item-add event is detected, the control processor 217D may initiate the capture and initial processing of an image (or a set of two or more images). ¶[0108] discloses the CVU obtains images of shopping baskets 205 within the field of view of its camera 410. The processor 420 of the CVU can execute a machine learning or computer vision object detection model.)
“detect the number of articles in a shopping container according to an image of the articles in the shopping container;”(Carter, ¶[0004] discloses A system for monitoring shopping baskets (e.g., baskets on human-propelled carts, motorized shopping or mobility carts, or hand-carried baskets) can include a computer vision unit that can image a surveillance region (e.g., an exit to a store), determine whether a basket is empty or loaded with merchandise, and assess a potential for theft of the merchandise. The computer vision unit can include a camera and (optionally) an image processor programmed to execute a computer vision algorithm to identify shopping baskets in the image and to determine a load status of the basket. ¶[0207] discloses the imaged items detected in the cart basket (e.g., number of items detected, product categories of these items, product IDs where products are identifiable, etc.))
“classify the image of the articles in the shopping container by using a classifier, and obtain a classification weight corresponding to a classification result;”(Carter, ¶[0037] discloses The images can be still images or one or more frames from a video. In some embodiments, the system may also use the images to classify or identify the items in the basket; for example, a detected item can be classified as merchandise versus non-merchandise, or can be classified in terms of whether it is a high-theft-risk merchandise item. ¶[0115-0116] discloses The image processor 420 can classify an object in the image set as one of the following (any of which may be referred to as a load status of the basket): (a) a shopping basket containing merchandise; (b) a shopping basket not containing merchandise (e.g., the basket is not necessarily empty, e.g., a shopping cart with an open child seat 1620 may still contain a child, a handbag, etc.); or (c) an object other than a shopping basket (e.g., a shopper). The load status can include a weighted score or value that accounts for the amount of the load as well as an estimate of the value of the load (e.g., whether the load includes high value items). For example, a basket partially loaded with high value items (e.g., liquor bottles) may have a load status that is higher than a basket fully loaded with bulky, inexpensive items (e.g., paper towels), because the partially loaded basket represents a greater monetary loss to the store. Further see ¶[0188] and ¶[0212].)
“and calculate a prediction value of the articles in the shopping container based on the number of the articles in the shopping container detected by the detecting device and the classification weight.”(Carter, ¶[0205] A more sophisticated method may involve scoring the cart in terms of overall theft risk. For example, a score can be generated by summing the item prices of any detected high theft risk items, or by counting the number of detected high theft risk items. Further, an anti-theft action may automatically be taken if a large number of units (e.g., ten or more) of the same item are detected in the cart, especially if the item is not an item commonly purchased in such quantities. The score generation algorithm may also consider amounts of time spent by the cart in specific store areas; for example, if a cart spends a relatively long time stopped in a high theft risk merchandise area, the algorithm may boost the score to reflect an increased theft risk, even if the system does not detect the addition of any items to the cart while in the high theft risk area. ¶[0206] In some embodiments, multiple scores may be maintained for a cart during a shopping session. For example, one score may represent a probability that the cart contains merchandise that has not yet been paid for (referred to herein as “unpaid” merchandise), and another score may represent a probability that the cart contains a high theft risk item. The score(s) may be updated substantially in real time as events occur during a session. For example, if a cart visits an active checkout station, the probability that it contains unpaid merchandise may be reduced to a low value; but if the cart then returns to a merchandise area without leaving the store, the probability may be increased. ¶[0207] A separate score may also be maintained representing the likelihood that the cart contains one or more non-merchandise items such as a purse, backpack or reusable shopping bag. A cart determined to likely contain only a non-merchandise item may be treated as having a slightly higher theft risk than a truly empty cart (e.g., due to the possibility that the non-merchandise classification of the detected item is erroneous) ¶[0208] In embodiments in which the system builds a record of data describing the imaged items detected in the cart basket (e.g., number of items detected, product categories of these items, product IDs where products are identifiable, etc.), the system can compare this record to associated payment/checkout transaction to assess whether the customer has paid for all of the items. If no corresponding payment transaction is found, or if a significant discrepancy is detected (e.g., one or more high price items were detected in the cart but are not included in the partially-matching transaction record), an appropriate anti-theft action can be initiated.)
However Carter is silent on the following which would have been obvious in view of Glaser from similar filed of endeavor “and calculate a prediction value of the number of articles.” (Glaser, ¶[0045] discloses a shopper adding boxes from the cereal aisle into the shopping cart. The number of boxes within the cart may not be observable. A shopper's cart similarly can be assigned a probabilistic distribution of having some set of contents. At some future point, the shopper's cart may be observed to contain three visible cereal boxes. Such information is used to update the inventory representation in the EOG such that the shopping cart contents is predicted with a 80% probability to have three boxes and a 20% probability to have four or more boxes. ¶[0103] disclose products with a prediction confidence level above a threshold can be automatically added. the EOG may be able to detect that some quantity of a variety of apples are probably selected by the shopper. ¶[0153] discloses compound object modeling can be an initiation process based on statistical models that can be based on the object classification. the object composition prediction can be generated for new occurrences of an object that has been observed to commonly contain particular objects. ¶[0154] discloses The classification of objects can additionally include assigning object attributes such as size, shape, and volume are assigned, and the volume and shape may restrict the quantity and type of the contained objects. ¶[0209] discloses estimating a filled state of the shipping crate can use the volume and dimensions of the shipping crate and cookie box in order to guess at the number of cookie boxes that would fit within the shipping crate. At that point, the cookie box possessed by a person is classified with a high confidence because of the affirmative classification, and the shipping crate can be modeled as containing a number of cookie boxes with a moderate confidence level. Further ¶[0213].)
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 Glaser technique of Automatic checkout into Carter technique to provide the known and expected uses and benefits of Glaser technique over shopping cart monitoring technique of Carter. 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 Glaser to Carter in order to provide better shopping experience in shopping centers. (Refer to Glaser paragraph [0004-0005].)
Claims 8-9 have been analyzed and are rejected for the reasons indicated in claim 1 above.
As per claim 2, in view of claim 1, Carter as modified by Glaser further discloses wherein the processor is further configure to “a determine a difference between the value and a scanning value, wherein the scanning value is the number of the articles obtained by scanning the articles in the shopping container by a scanner.” (Carter, ¶[0152], further ¶[0208] discloses ¶[0208] In embodiments in which the system builds a record of data describing the imaged items detected in the cart basket (e.g., number of items detected, product categories of these items, product IDs where products are identifiable, etc.), the system can compare this record to associated payment/checkout transaction to assess whether the customer has paid for all of the items. If no corresponding payment transaction is found, or if a significant discrepancy is detected (e.g., one or more high price items were detected in the cart but are not included in the partially-matching transaction record), an appropriate anti-theft action can be initiated. ¶[0209] FIG. 17 illustrates on embodiment of such a process. This process may be implemented by a CCU, CVU, CTU, and/or other processing nodes of the system, as a shopping cart approaches a store exit. In block 1710, the process receives a notification that a cart containing system-classified items is approaching a store exit. In blocks 1720 and 1730, the system acquires the cart's path history, compares it checkout point locations to identify a payment point passed by the cart, and looks up the most recent payment transaction(s) processed by the payment point. In some cases, the payment transactions may be anonymized and/or summarized to protect customer privacy. In block 1740, the process determines whether any of these payment transactions “match” the record of cart contents. Minor discrepancies may be disregarded for purposes of determining whether a match is found. If no match is found (indicating that the customer likely did not pay for items in the cart), the cart may be prevented from exiting the store (block 1750). If a match is found, the cart may be permitted to exit the store. Various other types of anti-theft actions may additionally or alternatively be performed. For example, if a transaction record is found that nearly matches the record of cart contents, but one or more high priced (or high theft risk) items imaged in the cart are missing from the transaction record, the system may prompt store personnel to check the customer's cart and receipt for the high priced item(s). )
Claim 10 has been analyzed and is rejected for the reasons indicated in claim 2 above.
As per claim 3, in view of claim 2, wherein, Carter as modified by Glaser further discloses “the processor configured to compare the difference with a threshold, and generate alarm information when the difference is greater than or equal to the threshold.”(Carter, ¶[0005] discloses The system can identify a shopping basket that is exiting the store, determine a load status (e.g., at least partially loaded), determine that there are no indicia of the customer having paid for the merchandise, and execute an anti-theft action, e.g., actuate an audible or visual alarm, notify store personnel, activate a store surveillance system, activate an anti-theft device associated with the basket (e.g., a locking shopping cart wheel), activate an external anti-theft device such as a remote-controlled locking gate, etc. ¶[0205], ¶[0208] discloses If no corresponding payment transaction is found, or if a significant discrepancy is detected (e.g., one or more high price items were detected in the cart but are not included in the partially-matching transaction record), an appropriate anti-theft action can be initiated.)
As per claim 4, in view of claim 3, Carter as modified by Glaser further discloses “wherein, the threshold is set according to a range where the value is located.”(Carter, ¶[0205] discloses scoring the cart in terms of overall theft risk. For example, a score can be generated by summing the item prices of any detected high theft risk items, or by counting the number of detected high theft risk items. Further, an anti-theft action may automatically be taken if a large number of units (e.g., ten or more) of the same item are detected in the cart, especially if the item is not an item commonly purchased in such quantities. The score generation algorithm may also consider amounts of time spent by the cart in specific store areas; for example, if a cart spends a relatively long time stopped in a high theft risk merchandise area, the algorithm may boost the score to reflect an increased theft risk, even if the system does not detect the addition of any items to the cart while in the high theft risk area. ¶[0206] In some embodiments, multiple scores may be maintained for a cart during a shopping session. For example, one score may represent a probability that the cart contains merchandise that has not yet been paid for (referred to herein as “unpaid” merchandise), and another score may represent a probability that the cart contains a high theft risk item. The score(s) may be updated substantially in real time as events occur during a session. For example, if a cart visits an active checkout station, the probability that it contains unpaid merchandise may be reduced to a low value; but if the cart then returns to a merchandise area without leaving the store, the probability may be increased. ¶[0207] A separate score may also be maintained representing the likelihood that the cart contains one or more non-merchandise items such as a purse, backpack or reusable shopping bag. A cart determined to likely contain only a non-merchandise item may be treated as having a slightly higher theft risk than a truly empty cart (e.g., due to the possibility that the non-merchandise classification of the detected item is erroneous).)
As per claim 5, in view of claim 4, Carter as modified by Glaser further discloses “wherein, the larger the prediction value is, the larger the threshold is.” (Carter, ¶[0115-0116], further ¶[0205] discloses scoring the cart in terms of overall theft risk. For example, a score can be generated by summing the item prices of any detected high theft risk items, or by counting the number of detected high theft risk items. Further, an anti-theft action may automatically be taken if a large number of units (e.g., ten or more) of the same item are detected in the cart, especially if the item is not an item commonly purchased in such quantities. The score generation algorithm may also consider amounts of time spent by the cart in specific store areas; for example, if a cart spends a relatively long time stopped in a high theft risk merchandise area, the algorithm may boost the score to reflect an increased theft risk, even if the system does not detect the addition of any items to the cart while in the high theft risk area. ¶[0206] In some embodiments, multiple scores may be maintained for a cart during a shopping session. For example, one score may represent a probability that the cart contains merchandise that has not yet been paid for (referred to herein as “unpaid” merchandise), and another score may represent a probability that the cart contains a high theft risk item. The score(s) may be updated substantially in real time as events occur during a session. For example, if a cart visits an active checkout station, the probability that it contains unpaid merchandise may be reduced to a low value; but if the cart then returns to a merchandise area without leaving the store, the probability may be increased. ¶[0207] A separate score may also be maintained representing the likelihood that the cart contains one or more non-merchandise items such as a purse, backpack or reusable shopping bag. A cart determined to likely contain only a non-merchandise item may be treated as having a slightly higher theft risk than a truly empty cart (e.g., due to the possibility that the non-merchandise classification of the detected item is erroneous).
As per claim 6, in view of claim 1, Carter as modified by Glaser further discloses “wherein, the processor configure to divide the classification result into at least two categories according to an extent to which a bottom of the shopping container is covered by the articles in the image.”(Carter, ¶[0115-0116] The image processor 420 can classify an object in the image set as one of the following (any of which may be referred to as a load status of the basket): (a) a shopping basket containing merchandise; (b) a shopping basket not containing merchandise (e.g., the basket is not necessarily empty, e.g., a shopping cart with an open child seat 1620 may still contain a child, a handbag, etc.); or (c) an object other than a shopping basket (e.g., a shopper). The load status may represent a range of values associated with an amount of the load of the shopping basket. For example, the range may be a number (e.g., 1 to 5, with 1 empty and 5 fully loaded), a grade (e.g., A to E, where A represents fully loaded and E represents empty), or some other type of score, discriminative or semantic classifier, or probability scaling for a plurality of load levels (e.g., full, ¾ full, ½ full, ¼ full, or empty). The load status can include a weighted score or value that accounts for the amount of the load as well as an estimate of the value of the load (e.g., whether the load includes high value items). For example, a basket partially loaded with high value items (e.g., liquor bottles) may have a load status that is higher than a basket fully loaded with bulky, inexpensive items (e.g., paper towels), because the partially loaded basket represents a greater monetary loss to the store.¶[0150] An empty shopping basket has a flat bottom, whereas an at least partially loaded basket will have items that extend above the flat bottom. Non-empty shopping baskets accordingly have a 3D topography that is substantially different from empty shopping baskets. This topography can be used, at least in part, to determine that the basket is non-empty but also may provide information on the type of items that are in the basket (e.g., the topography due to a roughly cubical package of baby diapers is different than the topography due to bottles of liquor). Accordingly, in some implementations, the anti-theft system 400 includes sensors that provide depth information. As described with reference to FIG. 4A, such sensors 460 can include depth cameras, stereo pairs of cameras, ultrasonic sensors, time-of-flight sensors, lidar (scanning or non-scanning), millimeter wave radar, etc.)
Claim 11 and 18 have been analyzed and are rejected for the reasons indicated in claim 6 above.
As per claim 7, in view of claim 1, Carter as modified by Glaser further discloses “wherein, the processor configured to multiple the detected number of the articles in the shopping container by the classification weight to obtain the prediction value of the number of the articles in the shopping container.”(Carter, ¶[0115] discloses the load status may represent a range of values associated with an amount of the load of the shopping basket. For example, the range may be a number (e.g., 1 to 5, with 1 empty and 5 fully loaded), a grade (e.g., A to E, where A represents fully loaded and E represents empty), or some other type of score, discriminative or semantic classifier, or probability scaling for a plurality of load levels (e.g., full, ¾ full, ½ full, ¼ full, or empty). The load status can include a weighted score or value that accounts for the amount of the load as well as an estimate of the value of the load (e.g., whether the load includes high value items). For example, a basket partially loaded with high value items (e.g., liquor bottles) may have a load status that is higher than a basket fully loaded with bulky, inexpensive items (e.g., paper towels), because the partially loaded basket represents a greater monetary loss to the store. )
Claims 13 and 20 have been analyzed and are rejected for the reasons indicated in claim 7 above.
As per claim 12, The method according to claim 11, “wherein the at least two categories include a first category corresponding to an area covered by the articles being less than 1/2 of an area of the bottom of the shopping container, and a second category corresponding to the area covered by the articles being greater than 1/2 of the area of the bottom of the shopping container.” (Carter, ¶[00048] discloses the cart may include sensors that can determine whether its shopping basket is at least partially loaded (e.g., by analyzing vibration data of the cart) and the CT may communicate a load status (e.g., empty, partially loaded, fully loaded) to the AP.¶[0115] disclose a plurality of load levels (e.g., full, ¾ full, ½ full, ¼ full, or empty).¶[0150] disclose an empty shopping basket has a flat bottom, whereas an at least partially loaded basket will have items that extend above the flat bottom.)
Claims 16 and 19 have been analyzed and are rejected for the reasons indicated in claim 12 above.
Allowable Subject Matter
Claims 15 and 17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and on the pending conditions of the rejected and objected matter set forth in this action.
The following is a statement of reasons for the indication of allowable subject matter: the prior art of record, alone or in combination, fails to teach or suggest the limitations set forth by each of claims 15 and 17.
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 divi
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