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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 5/26/26 has been entered. Claims 1-20 are presented for examination.
Claim Rejections - 35 USC § 112
Applicant’s amendments have been considered and entered. The rejection of claim 5 under 35 U.S.C. 112(b) is withdrawn.
Claim Rejections - 35 USC § 101
Applicant’s remarks, in view of the amendments have been considered. The Examiner agrees with the remarks. Therefore, the rejection of claims 1-20 under 35 USC § 101 is withdrawn.
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.
Claims 1-12, 16-19 are rejected under 35 U.S.C. 103 as being unpatentable Adato et al. (US 20190213390 A1), in view of Rodgers et al. (US 9802728 B1).
Re-claim 1, Adato et al. teach--- A method for determining a dimension of a product placed in a product presentation device, wherein the method comprises the following steps, namely:
--automatic detection of a change in a parameter, among a plurality of parameters that are representative of at least one dimension of the product,
(see e.g. [0184] The embodiments disclosed herein may use any sensors configured to detect one or more parameters associated with products (or a lack thereof). For example, embodiments may use one or more of pressure sensors, weight sensors, light sensors, resistive sensors, capacitive sensors, inductive sensors, vacuum pressure sensors, high pressure sensors, conductive pressure sensors, infrared sensors, photo-resistor sensors, photo-transistor sensors, photo-diodes sensors, ultrasonic sensors, or the like.
[0214] monitoring a rate at which detection element signals change as products are added to a shelf (e.g., when areas of a pressure sensitive pad change from a default value to a product-present value).
[0217] Method 1000 may further include additional steps. For example, method 1000 may include identifying a change in at least one characteristic associated with one or more of the first signals (e.g., signals from a first group or type of detection elements).
--and the sensors are located in the product presentation device,
(see e.g. [0782] In some embodiments, the at least one processor may receive real-time image data from a plurality of image sensors fixedly mounted to store shelves).
Adato et al. do not explicitly teach the following limitations.
However, Rodgers et al. --wherein the parameters are detected by means of a plurality of electronic sensors with intersecting detection directions that intersect with each other and with the product and the sensors are located in the product presentation device, - automatic determination of at least one dimension of the product based on the detected change in the parameter.
(see e.g. col. 19, lines 64-67- col. 20, lines 1-5) By providing two or more sensors that are aligned to emit sensing beams along axes which intersect at a point associated with a conveying unit, the systems and methods of the present disclosure may be used to determine one or more dimensions or other attributes of an object, e.g., a width or a length of the object, and determine an angular orientation of the object based on such dimensions or attributes.
col. 20, lines 47-55 --Because the angled sensor 1064 detected the presence of the container 100A first, e.g., at time t.sub.1, before the normal sensor 1062 detected the presence of the container 100A, it may be understood that the container 100A is wider than the width to the intersect point 1066, or w.sub.IP. Therefore, once the width difference w.sub.DIFF has been determined, the width of the object w.sub.OBJ may be calculated by adding the width difference w.sub.DIFF to the width to the intersect point 1066, or w.sub.IP.
col. 21, lines 52-58 --the times at which each of the respective sensors 1062, 1064 detects the presence of an object, e.g., a container, may be used to determine a distance traveled by the object between such times, and the distance may be used to determine a width of the object with respect to a distance of the intersect point, which may be purely theoretical.
col. 9, lines 35-38 - For example, the reflected light within the infrared bands may be processed in order to recognize a distance to the object, as well as one or more dimensions (e.g., heights, widths or lengths) of the object.)
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Adato et al., and include the steps cited above, as taught by Rodgers et al., because by providing two or more sensors that are aligned to emit sensing beams along axes which intersect at a point associated with a conveying unit, the systems and methods of the present disclosure may be used to determine one or more dimensions or other attributes of an object, e.g., a width or a length of the object, and determine an angular orientation of the object based on such dimensions or attributes. (see e.g. col. 19, lines 64-67- col. 20, lines 1-5).
Re-claim 2, Adato et al. teach – The method according to Claim 1, wherein each of the sensors - can either be positioned in a variable location - or is permanently located.
[0160] In some embodiments, system 500 may receive an output signal from at least one sensor located on an opposing retail shelving unit. For example, system 500B may receive output signals from a sensing system located on second retail shelving unit 604.)
Re-claims 3, 10, Adato et al. teach -- The method according to claim 1, wherein the plurality of sensors comprise at least one of the following formations, namely:
- a time-of-flight sensor,
- a camera,
- a3D-camera system,
- a time-of-flight camera,
-a LIDAR,
- as a pressure-sensitive sensor mat,
- as a sensor mat with an array of light-sensitive elements.
(see e. g. [0207] In another example, an artificial neural network configured to recognize product types may be used to analyze the signals received by step 1005 (such as signals from pressure sensors, from light detectors, from contact sensors, and so forth) to determine product types associated with products placed on an area of a shelf (such as an area of a shelf associated with the first subset of detection elements). In yet another example, a machine learning algorithm trained using training examples to recognize product types may be used to analyze the signals received by step 1005 (such as signals from pressure sensors, from light detectors, from contact sensors, and so forth) to determine product types associated with products placed on an area of a shelf (such as an area of a shelf associated with the first subset of detection elements.
[0208] For example, a soda may have a base detectable by a pressure sensitive pad as a continuous ring. Further, the can of soda may be associated with a first weight signal having a value recognizable as associated with such a product. A 16 ounce bottle of soda may be associated with a base having four or five pressure points, which a pressure sensitive pad may detect as arranged in a pattern associated with a diameter typical of such a product.
[0260] camera
[0115] [0471] 3D camera
10. The method according to claim 1, wherein the sensors comprise based on time-of-flight measurement of a sensor signal (2) is used
(see e.g. [0115] Examples of capturing devices may include, a digital camera, a time-of-flight camera, a stereo camera, an active stereo camera, a depth camera, a Lidar system, a laser scanner, CCD based devices, or any other sensor based system capable of converting received light into electric signals.).
Re-claims 4, 5, Adato et al. teach The method according to claim 1, wherein the determination of the dimension of the product is carried out only when a learning period for determining the dimension of the product has been triggered.
The method according to Claim 4, wherein the observed change in the representative parameter is checked for at least one trigger and the period is triggered when the presence of this trigger is detected.
(see e.g. [0192] Any of the profile matching described above may include use of one or more machine learning techniques. For example, one or more artificial neural networks, random forest models, or other models trained on measurements annotated with product identifiers may process the measurements from the detection elements and identify products therefrom.
[0269] In yet another example, when the existing product model comprises parameters of a machine learning model trained by a machine learning algorithm using training examples to identify products, the modification to the existing product model may include a change to at least one of the parameters of the machine learning model, for example using a continuous learning scheme, using a reinforcement algorithm, and so forth. This may increase the accuracy in the product models, and may help analyze future received images.)
Re-claims 6, 7, Adato et al. do not explicitly teach the following limitations.
However, Rodgers et al. 6. -- The method according to claim 1, wherein the determination of the dimension of the product is made directly from a single change in the parameter.
(see e.g. For example, referring again to the system 300 shown in FIG. 3, a height, length, width or any other dimension of the container 30 may be determined using the depth sensor 362,
col. 20, lines 20-24 -- As is shown in FIG. 10A and FIG. 10B, the alignment of the normal sensor 1062 with respect to the angled sensor 1064 enables a width of a container to be determined based on the differences in the times at which each of the respective sensors detects the container.)
7.--The method according to claim 1, the determination of the dimension of the product is made from a plurality of changes in the parameter.
(see e.g. col. 19, lines 64-67- col. 20, lines 1-5 -- By providing two or more sensors that are aligned to emit sensing beams along axes which intersect at a point associated with a conveying unit, the systems and methods of the present disclosure may be used to determine one or more dimensions or other attributes of an object, e.g., a width or a length of the object, and determine an angular orientation of the object based on such dimensions or attributes.
col. 23, lines 9-20 -- At box 1150, whether the first sensor to detect the object was the normal sensor or the angled sensor is determined. If the first sensor to detect the object was the angled sensor, then the process advances to box 1152, where the width of the object is defined as the sum of the distance to the intersect point determined at box 1125 and the width difference determined at box 1145. If the first sensor to detect the object was the normal sensor, then the process advances to box 1154, where the width of the object is defined as the difference between the distance to the intersect point determined at box 1125 and the width difference determined at box 1145.)
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Adato et al., and include the steps cited above, as taught by Rodgers et al., in order to determine one or more dimensions or other attributes of an object, e.g., a width or a length of the object, and determine an angular orientation of the object based on such dimensions or attributes. (see e.g. col. 19, lines 64-67- col. 20, lines 1-5).
Re-claim 8, Adato et al. teach -- The method according to claim 1, wherein the determination of at least one dimension is carried out by means of artificial intelligence, which processes or evaluates the changes in the representative parameter.
(see e.g. [0136] In one embodiment, memory device 226 may store database 140. Database 140 may include product type model data 240 (e.g., an image representation, a list of features, a model obtained by training machine learning algorithm using training examples, an artificial neural network, and more) that may be used to identify products in received images;
[0244] In another example, the product model may include exemplary images of the product or products. In yet another example, the product model may include parameters of an artificial neural network configured to identify particular products.
[0192] Any of the profile matching described above may include use of one or more machine learning techniques. For example, one or more artificial neural networks, random forest models, or other models trained on measurements annotated with product identifiers may process the measurements from the detection elements and identify products therefrom. In such embodiments, the one or more models may use additional or alternative input, such as images of the shelf (e.g., from capturing devices 125 of FIGS. 4A-4C explained above) or the like.)
[0207] n another example, an artificial neural network configured to recognize product types may be used to analyze the signals received by step 1005 (such as signals from pressure sensors, from light detectors, from contact sensors, and so forth) to determine product types associated with products placed on an area of a shelf (such as an area of a shelf associated with the first subset of detection elements).
Re-claim 9, Adato et al. do not explicitly teach the following limitations.
However, Rodgers et al. teach --The method according to claim 1, wherein, - the dimension of the product is its depth measured in the direction of the depth of the product presentation device or a storage structure,
(see e.g. col. 13, lines 23-27 --- Based at least in part on the dimensional information, the mass and/or the imaging information obtained using the depth sensor 362, the scale 364 and the imaging device 366, the container 30 may be identified, and an orientation or alignment of the container 30 may be determined. )
and wherein - the change in the parameter is given by a change in distance (Ly) in the direction of the depth of the product presentation device or the depth of the storage structure, as determined by the sensor, and wherein - the depth of the product is determined by the detected change in distance (Hy).
Re-claim 11, Adato et al. teach -- A method for monitoring the inventory in a product presentation device in which at least one product can be placed,
- wherein for the product at least one dimension which is representative for inventory monitoring, is known in advance,
(see e.g. [0210] For example, step 1010 may include extrapolating based on stored dimensions of each product and stored dimensions of the shelf area to determine an area and/or volume available for additional products. Step 1010 may further include extrapolation of the number of additional products based on the stored dimensions of each product and determined available area and/or volume.)
[0008] The at least one processor may be configured to receive first signals from a first subset of detection elements from among the plurality of detection elements after one or more of a plurality of products are placed on at least one area of the store shelf associated with the first subset of detection elements and use the first signals to identify at least one pattern associated with a product type of the plurality of products.)
--wherein the inventory monitoring method comprises the following method steps, namely:
--automatic detection of a change in a parameter that is representative of at the least one dimension of the product,
(see e. g. [0214] monitoring a rate at which detection element signals change as products are added to a shelf (e.g., when areas of a pressure sensitive pad change from a default value to a product-present value).
[0217] Method 1000 may further include additional steps. For example, method 1000 may include identifying a change in at least one characteristic associated with one or more of the first signals (e.g., signals from a first group or type of detection elements).
--and the sensors are located in the product presentation device,
(see e.g. [0782] In some embodiments, the at least one processor may receive real-time image data from a plurality of image sensors fixedly mounted to store shelves).
-automatic detection of a change in the number of products, wherein the change in the representative parameter is evaluated with relation to the at least one representative dimension for inventory monitoring.
(see e. g. [0217] For example, the change in at least one characteristic associated with one or more of the first signals may be indicative of removal of at least one product from a location associated with the at least one area of the store shelf associated with the first subset of detection elements. )
Adato et al. do not explicitly teach the following limitations.
However, Rodgers et al. teach --wherein the parameters are detected by means of a plurality of electronic sensors with intersecting detection directions that intersect with each other and with the product.
(see e.g. col. 19, lines 64-67- col. 20, lines 1-5) By providing two or more sensors that are aligned to emit sensing beams along axes which intersect at a point associated with a conveying unit, the systems and methods of the present disclosure may be used to determine one or more dimensions or other attributes of an object, e.g., a width or a length of the object, and determine an angular orientation of the object based on such dimensions or attributes.
col. 20, lines 47-55 --Because the angled sensor 1064 detected the presence of the container 100A first, e.g., at time t.sub.1, before the normal sensor 1062 detected the presence of the container 100A, it may be understood that the container 100A is wider than the width to the intersect point 1066, or w.sub.IP. Therefore, once the width difference w.sub.DIFF has been determined, the width of the object w.sub.OBJ may be calculated by adding the width difference w.sub.DIFF to the width to the intersect point 1066, or w.sub.IP.
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Adato et al., and include the steps cited above, as taught by Rodgers et al., because by providing two or more sensors that are aligned to emit sensing beams along axes which intersect at a point associated with a conveying unit, the systems and methods of the present disclosure may be used to determine one or more dimensions or other attributes of an object, e.g., a width or a length of the object, and determine an angular orientation of the object based on such dimensions or attributes. (see e.g. ([0125)].
Re-claim 12, Adato et al. teach – The method of monitoring inventory according to Claim 11, wherein it is examined whether the automatically detected change in the number of products leads to a fall below a threshold value of the number of products, wherein, in the event of a positive test result, a restocking alarm is triggered.
(see e.g. [0657] In another example, a restocking event may be detected when a part of a shelf is determined to be empty, when an amount of products on a part of a shelf is determined to be below a threshold associated with the part of the shelf and/or with the product type of the products (for example, according to a planogram), and so forth.
[0661] a restocking task may be generated in response to a detected condition indicating that one or more products needs restocking.)
Re-claims 16, 17, Adato et al. teach -- The method according to Claim 1, wherein the determination of the dimension of the product is carried out only upon an external trigger that is external to the sensor.
(see e.g. [0582]) By way of example, system 100 may receive image 3050 and detect the first group of products represented by the dark shade and the second group of products represented by the lighter shade. System 100 may analyze image 3050 to detect product 3055 and determine that it is displayed in a nonstandard orientation. The determination that product 3055 is in a nonstandard orientation may include detecting that the height of product 3055, as displayed, is different from that of either the first or second group of products but detecting that the width of product 3055, as displayed, matches the height of the first group of products, for example, product 3052).
-The method according to Claim 1, wherein the determination of the dimension of the product is carried out only upon an internal trigger based on the automatically detected change in the parameter.
(see e.g. [0220] Method 1050 may include a step 1055 of determining a change in at least one characteristic associated with one or more first signals. For example, the first signals may have been captured as part of method 1000 of FIG. 10A, described above. For example, the first signals may include pressure readings when the plurality of detection elements includes pressure sensors,
[0579] Consistent with this disclosure, the dimensions of products may also be determined. For example, system 100 may detect the first group of products in image 3020 and further determine that the width of each product of the first group of products is W1. )
Re-claim 18, Adato et al., in view of Rodgers et al. do not explicitly teach --The method according to Claim 10, wherein the sensor has a resolution in the cm range or sub-cm range. .
However, it is considered an obvious variation of Adato et al. based on the following teaching:
(see e.g. The distances and angles of the image capturing devices relative to the captured products should be selected such as to enable adequate product identification, especially when considered in view of image sensor resolution and/or optics specifications.
[0154] Consistent with the present disclosure, image capture device 506 may include an image sensor having sufficient image resolution to enable detection of text associated with labels on an opposing retail shelving unit. )
Re-claim 19, Adato et al. teach -- The method of monitoring inventory according to Claim 11, wherein the dimension is a depth of the product.
(see e.g. [0165] n these embodiments, the image data acquired by the first image capture device and the second image capture device may enable a calculation of depth information (e.g., based on image parallax information) associated with at least one product positioned on an opposing retail shelving unit.)
Note: Rodgers et al. also teach the limitation in at least col. 13, lines 23-27 --- Based at least in part on the dimensional information, the mass and/or the imaging information obtained using the depth sensor 362, the scale 364 and the imaging device 366, the container 30 may be identified, and an orientation or alignment of the container 30 may be determined. )
Claims 13-15, 20 are rejected under 35 U.S.C. 103 as being unpatentable Adato et al. (US 20190213390 A1), in view of Rodgers et al. (US 9802728 B1), in further view of Swafford et al. (US 20190279149).
Re-claims 13-15, 20, Adato et al., in view of Rodgers et al. do not teach the limitations as claimed.
However, Swafford et al. teach – 13. The method of monitoring inventory according to Claim 11, wherein it is examined whether an automatically detected change in the number of products leads to an exceedance of a threshold value of the change in the number of products, wherein a theft alarm is triggered in the event of a positive test result.
(see e. g. [0174] Third, if more than a predetermined number of product packages have been removed in less than a predetermined amount of time, the microcomputer may determine that a potential theft situation is in progress
[0104] In another embodiment, a two-tiered response could be implemented. If the change in position of the pusher 25 was greater than normal, a signal could be transmitted to the security camera 195. In addition, an inaudible notification could be provided directly to security personnel. If the positional change of the pusher 25 more clearly indicated a potential theft, an audible alarm and flashing lights could also be activated.
[0108] The position of the pusher 25 and the number of products corresponding to that position of the pusher 25 can be used to calculate the quantity of remaining products based on a later position of the pusher 25 through the use of well-known extrapolation techniques.).
14. Swafford et al. teach -- The method of monitoring inventory according to Claims 12, wherein product-specific or product group-specific threshold values are used in the test.
(see e.g. [0237] In one specific example, the display management system controller device 2400 may receive motion data from a single display management system (e.g. system 1800, 2100, or 2300) and determine that the received motion data represents removal of a plurality of a same product from the display management system. Further, the display management system controller device 2400 may calculate a rate at which products are being removed from this display management system. In one example, if a rate at which the products are being removed from this display management system is above a threshold level, the display management system controller device 2400 may determine that the removal of products may represent an attempted theft. )
15. Swafford et al. teach – The method of monitoring inventory according to Claim 11, wherein, during automatic detection of the change in the quantity of products, an additional system component is taken into account, or an optical monitoring system by means of which a digital recording of the product presentation device is created, in which the change in the number of products has been detected.
(see e.g. [0098] The store computer 190 determines that the rate of change in product level of the product associated with the controller 155 is indicative of a potential theft. The store computer 190 then transmits a signal, either wired, or wirelessly, to an antenna 196, which is mounted to the security camera 195. The signal instructs the security camera 195 to monitor a position associated with the location of the controller 155. As can be appreciated, security personnel can sometimes provide a more nuanced response, thus it is advantageous to notify security personnel. Therefore, the store computer 190 can also notify security personnel to monitor the area by displaying a warning on the store computer screen or by transmitting a signal to a security computer or by activating an audible tone or flashing light in the vicinity of the potential theft or by other known methods of notification such as a signal to the pager or beeper carried by the security personnel.
[0099] Information from the security camera could be sent to a television or other visual display device that is located near the location where the potential theft is occurring. The visual display device could display an image of the potential thief such that the potential thief could appreciate the fact that the thief was being watched.)
20. --The method of monitoring inventory according to Claim 15, wherein the additional system component is a billing or cash register system to which the change in the number of products is communicated electronically.
(see e.g. [0098] The store computer 190 determines that the rate of change in product level of the product associated with the controller 155 is indicative of a potential theft. The store computer 190 then transmits a signal, either wired, or wirelessly, to an antenna 196, which is mounted to the security camera 195. The signal instructs the security camera 195 to monitor a position associated with the location of the controller 155. As can be appreciated, security personnel can sometimes provide a more nuanced response, thus it is advantageous to notify security personnel. Therefore, the store computer 190 can also notify security personnel to monitor the area by displaying a warning on the store computer screen or by transmitting a signal to a security computer or by activating an audible tone or flashing light in the vicinity of the potential theft or by other known methods of notification such as a signal to the pager or beeper carried by the security personnel.
[0099] Information from the security camera could be sent to a television or other visual display device that is located near the location where the potential theft is occurring. The visual display device could display an image of the potential thief such that the potential thief could appreciate the fact that the thief was being watched.).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Adato et al., in view of Rodgers et al., and include the steps cited above, as taught by Swafford et al., in order to aid in determining the inventory on the shelf in a retail store (see e.g. [0002]).
Response to arguments
Applicant’s arguments with respect to the 103 rejection of claims 1-20 have been considered but are moot, in view of the new rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Elazary et al. (US 10558944 B1) – Inventory Verification Device.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUNA CHAMPAGNE whose telephone number is (571)272-7177. The examiner can normally be reached M-F 8:00-5:00.
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, Florian Zeender can be reached at 571 272-6790. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/LUNA CHAMPAGNE/
Primary Examiner, Art Unit 3627
August 7, 2026
Prosecution Insights