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 .
Status of Claims
Applicant’s submission filed 1/22/24 has been entered. Claims 16-20 are new. Claims 1-20 are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/26/25 have been considered by the examiner.
Claim Objections
Claims 11-15 have been amended and are now in proper form.
Claim identifier ---Note to Applicant
Claim 5 is labeled : “(Currently Amended)”. However, the claim language appears to be the same as the previously filed claim. Therefore, the claim should have been labeled “(Previously presented)”.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 5 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The claims recite a learning phase. It is unclear what the learning entails.
Claim 5 recites “the learning phase”. There is insufficient antecedent basis for this limitation in the claim.
Claim 15 recites– “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.”
It is unclear if the optical monitoring system is the additional system or if Applicant meant “an additional system component is taken into account, or an optical monitoring system”.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Note: The following analysis is based on the Revised Guidance titled “2019 Revised Patent Subject Matter Eligibility Guidance (Vol. 84, No. 4).
STEP 1
Are the claims directed to a process, machine, manufacture or composition of matter?
Claims 1-20 are all directed to a statutory category (e.g., a process, machine, manufacture, or composition of matter). The answer is YES.
STEP 2A. Prong 1
The claims disclose the abstract idea of determining a dimension of a product placed in a product presentation device
Exemplary claim 1 recites the following abstract concepts that are found to include “abstract idea”:
“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 that is representative of at least one dimension of the product, and
- automatic determination of at least one dimension of the product based on the detected change in the parameter.”
The remaining limitations are no more than computer elements (i.e., sensors) to be used as a tool to perform this abstract idea. (i.e. wherein the parameter is detected by means of an electronic sensory and the sensor is located in the product presentation device, and the sensor is in contact with the product during the automatic detection).
The recited limitations cover a process that, under its broadest reasonable interpretation, covers subject matter viewed as a certain method of organizing human activity with the additional recitation of generic computer components. For example, but for the “by a sensor” language, “detecting” in the context of this claim encompasses the user visually detecting the parameter representative of the dimension of the product.
The practice of detecting and determining data, is a commercial or legal interaction long prevalent in our system of commerce. The claims recite the idea of performing various conceptual steps generically resulting in the determination of a dimension of a product. As determined earlier, none of these steps recites specific technological implementation details, but instead get to this result by receiving, selecting and determining data. Thus, the claims are directed to a certain method of organizing human activity
STEP 2A, Prong 2
Are there additional elements or a combination of elements in the claim that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception?
The claim recites one additional element: that the determination of the dimension is carried out by means of artificial intelligence.
The artificial intelligence in the steps is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing data. This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component.
Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim is directed to an abstract idea.
STEP 2B
The next issue is whether the claims provide an inventive concept because the additional elements recited in the claims provide significantly more than the recited judicial exception. Taking the claim elements separately, the function performed by the sensor at each step of the process is purely conventional. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using an artificial intelligence to perform the steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Considered as an ordered combination, the computer components of Applicants' claims add nothing that is not already present when the steps are considered separately. The claimed invention does not focus on an improvement in computers as tools, but rather certain independently abstract ideas that use computers as tools. {Elec. Power, 830 F.3d at 1354). (Step 2B: NO).
There is no indication that indication that the sensor or artificial intelligence is anything other than a generic, off-the-shelf computer component, and the Symantec, TLI, and OIP Techs. Court decisions cited in MPEP 2106.05(d)(II) indicate that mere collection or receipt of data over a network is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is here).
The dependent claims when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea. The claims provide minimal technical structure or components for further consideration either individually or as ordered combinations with the independent claims. As such, additional recited limitations in the dependent claims only refine the identified abstract idea further. Further refinement of an abstract idea does not convert an abstract idea into something concrete.
Accordingly, a conclusion that the collecting step is well-understood, routine, conventional activity is supported under Berkheimer Option 2.
See MPEP 2106.05(d)(II) The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350,1355,112 USPQ2d 1093,1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hoteis.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result-a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306,1334,115 USPQ2d 1681,1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363,115 USPQ2d at 1092-93.
The claims are ineligible.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable Swafford et al. (US 20190279149 A1), in view of Adato et al. (US 20190213390 A1).
Re-claim 1, Swafford 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 that is representative of at least one dimension of the product,
(see e.g. [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.
[0153] As the spring uncoils, the amount of tension or pressure within the remaining coil of the spring increases. By measuring the tension of the spring, the length of the spring that is uncoiled can be determined.
[0154] The spring tension measuring device can incorporate a processing device or can transmit the information it measures to a microprocessor or other processing device. With a previous understanding of how the tension on the spring relates to the length of the spring, the processing device can determine the amount or length of spring that is uncoiled. For example, if the coil spring has a fixed spring constant, “k”, then the formula F=−kX can be used to calculate the length of spring that is uncoiled. This information can be used to determine the distance between the front of the shelf and the pusher. Understanding the dimensions of the products, the computing device can then determine the number of products in a facing.
- automatic determination of at least one dimension of the product based on the detected change in the parameter.
(see e.g. [0108] Then, in step 440, the store computer 90 calculates the amount of product on the shelf based on the position of the pusher 25. The store computer 90 also updates the inventory list at this point. In an embodiment where multiple facings have the same product, the total amount of product on all of the facings that have that product can be calculated. In an embodiment, the calculation of product in a facing can be accomplished through the use of a database of products and the relevant dimensions of a product, and the position of the pusher. In another embodiment, the number of products placed in the facing can be provided during setup of the controller 55 for that product. 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.
[0176] Additionally, without departing from this invention, the thickness of the product 910 may be determined by the control module 940 after taking a number of different readings from the system, such as a smart or learning system for determining the thickness of the product 910.)
The Examiner notes that Swafford et al. anticipate the controller 155 automatically detecting the change and an automatically determine the dimension of the product. One of ordinary skill in the art would have recognized that the results of the combination were predictable.
Swafford et al. do not explicitly teach the following limitations.
However, Adato et al. teach --wherein the parameter is detected by means of an electronic sensor and the sensor is located in the product presentation device, and the sensor is in contact with the product during the automatic detection; and
[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.
[0209] For example, detection elements such as pressure sensitive pads may be used to detect a product base shape and size (e.g., ring, pattern of points, asymmetric shape, base dimensions, etc.). Such a base shape and size may be used (optionally, together with one or more weight signals) to identify a particular product. The signals may also be used to identify and/or distinguish product types from one another.
[0186] With reference to FIG. 8A and consistent with the present disclosure, a store shelf 800 may include a plurality of detection elements, e.g., detection elements 801A and 801B. In the example of FIG. 8A, detection elements 801A and 801B may comprise pressure sensors and/or other type of sensors for measuring one or more parameters (such as resistance, capacitance, or the like) based on physical contact (or lack thereof) with products, e.g., product 803A and product 803B.
[0597] It is contemplated that pressure sensors may be used in conjunction with image processing, as disclosed above and throughout the disclosure, to detect vacant spaces. For example, pressure sensors may detect a plurality of products as described above and image processing may provide the dimension of the products by detecting products displayed on the shelf containing the pressure sensors.)
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 Swafford et al., and include the steps cited above, as taught by Adato et al., in order to enable the identification of a particular product (see e.g. ([0125)].
Re-claim 2, Swafford et al. teach – The method according to Claim 1, wherein the sensor - can either be positioned in a variable location - or is permanently located.
(see e.g. [0063-0067] As depicted FIG. 1a, a sensor assembly 30 can be mounted to the underside of the floor 24 over which the pusher 25 travels or to the shelf 5 and is configured to read the indicia strip 21. The sensor assembly 30 can be located at any position along the floor 24 and preferably near the coil spring 20. --- In another alternative embodiment, the sensor assembly 30 may be mounted within or on the pusher 25 and configured to read the indicia strip 21.)
Re-claims 3,10, Swafford et al. teach --sensor-equipped display management systems. The display management system may further have a sensor that outputs motion data in response to movement of the mechanism (see e.g. [abstract, [0012]) but do not explicitly teach the specific types of sensors claimed.
However, Adato et al. teach -- The method according to claim 1, wherein the sensor comprises 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 a sensor 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.).
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 Swafford et al., and include the steps cited above, as taught by Adato et al., so that the system may acquire different details relative to the plurality of products via electrical signals (see e.g. ([0116, 0117)].
Re-claims 4, 5, Swafford et al. do not teach the limitations as claimed.
However, 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 learning phase 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.)
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 Swafford et al., and include the steps cited above, as taught by Adato et al., in order to increase shelf accuracy in the product models (see e.g. [0269] ).
Re-claim 6, Swafford et al. teach-- 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. 0108] Then, in step 440, the store computer 90 calculates the amount of product on the shelf based on the position of the pusher 25. The store computer 90 also updates the inventory list at this point. In an embodiment where multiple facings have the same product, the total amount of product on all of the facings that have that product can be calculated. In an embodiment, the calculation of product in a facing can be accomplished through the use of a database of products and the relevant dimensions of a product, and the position of the pusher. In another embodiment, the number of products placed in the facing can be provided during setup of the controller 55 for that product. 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.
[0176] Additionally, for the shelf system 900 illustrated in FIG. 14, the number of products aligned on the shelf could be measured. In such an embodiment, the position of the pusher 925 could be used to determine the amount of product 910 on the shelf without the need to manually count the product. For example, the light transceiver 932 transmits the light signal 935 to the pusher 925 or the product 910. The light signal 935 may then be reflected back to the light transceiver 932 to determine the location of the pusher 925 by measuring and calculating the time to receive the light signal 935 at the light transceiver 932. When one product is removed, for example by a purchaser, the time to receive the light signal 935 back at the light transceiver 932 increases a particular amount. Based on the dimensions of the product 910, specifically the thickness of the product, the control module can calculate how many products have been removed from the shelf by an algorithm of how fast the light signal is traveling back to the light transceiver 932. The control module also can calculate the number of products that remain on the shelf in front of the pusher using in part information regarding the shelf dimensions, including the shelf depth. )
Note: Adato et al. also teach the limitation in at least )0209] –(In another example, detection elements such as light detectors may be used to detect a product based on a pattern of light readings indicative of a product blocking at least part of the ambient light from reaching the light detectors. Such pattern of light readings may be used to identify product type and/or product category and/or product shape.)
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, contact information when the plurality of detection elements includes contact sensors, light readings when the plurality of detection elements includes light detectors (for example, from light detectors configured to be placed adjacent to (or located on) a surface of a store shelf configured to hold products, as described above), and so forth.)
Re-claim 7, Swafford et al. teach-- 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. abstract ---In one aspect, the systems and methods may be utilized to calculate a number of products removed from a display management system based upon motion of one or more mechanisms within the display management system).
Re-claim 8, Swafford et al. do not teach the limitations as claimed.
However, 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).
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 Swafford et al., and include the steps cited above, as taught by Adato et al., in order to identify particular products associated with a product model(see e.g. ([244]).
Re-claim 9, Swafford 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. [0230] In particular, processor 2404 may calculate a distance moved by pusher 1804, and execute one or more processes to consult a lookup table (stored, for example, in memory 2402) for a depth dimension associated with a plurality of products held within the display management system 1800. )
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).
(see e.g. [0231] In another example, the display management system controller device 2400 may infer a depth dimension of a product type stored within a display management system 1800. In particular, without having information available within a lookup table stored in memory 2402, processor 2404 may determine a depth dimension of a product based upon one or more discrete motions of the pusher 1804. Specifically, after repeated instances of products being removed from the display management system 1800, processor 2404 may execute one or more processes to recognize a consistent distance moved by pusher 1804, and from this recognized distance, infer a depth dimension of a product to be utilized in determining a number of products removed from the display management system 1800 in response to future movements of pusher 1804.)
Re-claim 11, Swafford 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. [0245] Further, process 2500 may calculate a number of products removed from the display management system. In particular, processor 2404 may execute one or more processes to infer, or lookup, from a lookup table stored within memory 2402, a depth of a product. Using this information, processor 2404 may compare a depth of a product to a distance moved by, in one example, a pusher 1804)
--wherein the inventory monitoring method consists of 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. [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.)
-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. [0108] Then, in step 440, the store computer 90 calculates the amount of product on the shelf based on the position of the pusher 25. The store computer 90 also updates the inventory list at this point. In an embodiment where multiple facings have the same product, the total amount of product on all of the facings that have that product can be calculated. In an embodiment, the calculation of product in a facing can be accomplished through the use of a database of products and the relevant dimensions of a product, and the position of the pusher. In another embodiment, the number of products placed in the facing can be provided during setup of the controller 55 for that product. 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.
[0176] Additionally, without departing from this invention, the thickness of the product 910 may be determined by the control module 940 after taking a number of different readings from the system, such as a smart or learning system for determining the thickness of the product 910.)
[0086] The real time product information may make it possible to provide a more responsive inventory system so as to lower the amount of inventory in the store and therefore reduce the cost of inventory.)
Swafford et al. do not explicitly teach the following limitations.
However, Adato et al. teach --wherein the parameter is detected by means of an electronic sensor, the sensor is located in the product presentation device, and the sensor is in contact with the product during the automatic detection;
(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.
[0209] For example, detection elements such as pressure sensitive pads may be used to detect a product base shape and size (e.g., ring, pattern of points, asymmetric shape, base dimensions, etc.). Such a base shape and size may be used (optionally, together with one or more weight signals) to identify a particular product. The signals may also be used to identify and/or distinguish product types from one another.
[0186] With reference to FIG. 8A and consistent with the present disclosure, a store shelf 800 may include a plurality of detection elements, e.g., detection elements 801A and 801B. In the example of FIG. 8A, detection elements 801A and 801B may comprise pressure sensors and/or other type of sensors for measuring one or more parameters (such as resistance, capacitance, or the like) based on physical contact (or lack thereof) with products, e.g., product 803A and product 803B.
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 Swafford et al., and include the steps cited above, as taught by Adato et al., in order to enable the identification of a particular product (see e.g. ([0125)].
Re-claim 12, Swafford 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. [0174] Another condition that may be communicated is a low product condition. For example, the microcomputer may determine a low product condition if any pusher location is empty of product packages or less than a predetermined number of product packages are still being urged by the pusher 925.)
[0176] The control module also can calculate the number of products that remain on the shelf in front of the pusher using in part information regarding the shelf dimensions, including the shelf depth. Additionally, the system can be used in an inventory management mode to help the retailer determine the number of products for inventory purposes and restocking in low-stock or no-stock situations.).
Re-claim 13, Swafford et al. teach -- 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.).
Re-claim 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. )
Re-claim 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.)
Re-claims 16, 17, Swafford et al. do not explicitly teach the limitation as claimed.
However, 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. )
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 Swafford et al., and include the steps cited above, as taught by Adato et al., in order to determine a type of event associated with the change. (see e.g. [0222]).
Re-claim 18, Swafford 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. )
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 Swafford et al., and include the cm or sub-cm range. No unpredictable results are foreseen.
Re-claim 19, Swafford et al. teach -- The method of monitoring inventory according to Claim 11, wherein the dimension is a depth of the product.
see e.g. [0230] In particular, processor 2404 may calculate a distance moved by pusher 1804, and execute one or more processes to consult a lookup table (stored, for example, in memory 2402) for a depth dimension associated with a plurality of products held within the display management system 1800. )
(see e.g. [0231] In another example, the display management system controller device 2400 may infer a depth dimension of a product type stored within a display management system 1800. In particular, without having information available within a lookup table stored in memory 2402, processor 2404 may determine a depth dimension of a product based upon one or more discrete motions of the pusher 1804. Specifically, after repeated instances of products being removed from the display management system 1800, processor 2404 may execute one or more processes to recognize a consistent distance moved by pusher 1804, and from this recognized distance, infer a depth dimension of a product to be utilized in determining a number of products removed from the display management system 1800 in response to future movements of pusher 1804.)
Re-claim 20, Swafford et al. teach --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.).
Response to arguments
Applicant’s arguments with respect to claims 1-15 have been considered but are not persuasive.
Applicant’s remark:
Amended claims 4 and 5 do not recite a "learning period" or "learning phase." As such, the rejection is moot.
Examiner’s response:
Claim 5 still recites a learning phase. The rejection is maintained for claim 5.
Applicant’s remark:
Applicant respectfully submits that the above assertions in the Office Action are unreasonable, as one of ordinary skill in the art would not consider determining a dimension of a product to be a commercial or legal interaction. Determining a dimension of a product by a sensor is not economic behavior, nor is it an agreement, obligation or exchange of value, nor is it a legal relationship such as a contact, sale, or transaction. Determining the dimension of an object by the use of a sensor is a technical operation that involve determining a physical characteristic. The fact that the object is claimed as a product does not transform this these technical features into a commercial or legal interaction.
In view of the above, Applicant respectfully submits that the Office Action failed to establish that the claims recite a judicial exception under Prong 1 of Step 2A of the USPTO's Alice Mayo framework.
Further, amended claim 1 recites, among other things, "the sensor is in contact with the product during the automatic detection." Applicant respectfully submits that amended claim 1 recites additional technical features that integrate any alleged abstract idea into a practical application.
Examiner’s response:
As stated in the office action, the abstract idea is “determining a dimension of a product placed in a product presentation device “.
The Examiner never stated that determining a dimension of a product by a sensor was economic behavior.
Under step 2A, prong 2, the sensor is used, as an additional element, to determine the dimension of the product. When considered, the sensor is used as a tool and is recited at a high level of generality performing a generic sensor function, even when in contact with the product. Therefore, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Applicant’s remark:
In Thales Visionix, Inc. v. United States, 850 F.3d 1343 (Fed. Cir. 2017), the Federal Circuit held that claims reciting a system that is configured to determine an orientation of an object relative to a moving reference frame using an unconventional sensor configuration were not directed to an abstract idea. Similarly, in the present application, Applicant respectfully submits that the unconventional sensor configuration integrates any alleged abstract idea into a practical application.
Examiner’s response:
The sensor claimed in the current application is a generic sensor performing generic functions (measuring and sending).
Applicant’s remark:
Thus, Applicant respectfully contends that Swafford fails to disclose or suggest at least "the sensor is in contact with the product during the automatic detection" as claimed.
Examiner’s response:
The Examiner agrees with Applicant that Swafford fails to disclose or suggest the new limitation. However, Adato et al. teach the limitation as claimed.
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.
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/LUNA CHAMPAGNE/
Primary Examiner, Art Unit 3627
February 24, 2026