soDETAILED ACTION
This is a Non-Final Action in response to Amendment Submitted on 05/27/2025. Claims 1-13, 15-19, 27 and 36 are pending. The effective filling date is 01/01/2020.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 on 05/27/2025 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/25/2022 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claims 1-13,15-19, 27 and 36 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1- Claims 1-13,15-19, 27 and 36 are directed to a system, and is patent eligible subject matter. Claims 1-13,15-19, 27 and 36 pass step 1.
Step 2A, Prong 1-The independent claim 1, (and similarly claims 10 and 27) recite:
a. a plurality of weighing assemblies in contact with a shelf and jointly operable to measure a combined weight of a shelf and of products arranged thereupon (additional element evaluated in Prong 2);
b. one or more computer processors (additional element evaluated in Prong 2); and
c. a non-transient computer-readable storage medium comprising program instructions, which when executed by the one or more computer processors (additional element evaluated in Prong 2), cause the one or more computer processors to carry out the following steps:
i. monitoring weight measurement data corresponding to the shelf and a plurality of non-homogeneous products arranged thereupon (monitoring is an observation or evaluation, and is a concept performed in the human mind and grouped as a mental process, see MPEP 2106.04(a)(2)(III) Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. A discussion of concepts performed in the human mind, as well as concepts that cannot practically be performed in the human mind and thus are not "mental processes", is provided below with respect to point A; linking to a system does not automatically exclude the system as a mental process, see MPEP 2106.04(a)(2)(III)(D) Examples of product claims reciting mental processes include: A wide-area real-time performance monitoring system for monitoring and assessing dynamic stability of an electric power grid – Electric Power Group, 830 F.3d at 1351 and n.1, 119 USPQ2d at 1740 and n.1), said weight measurement data transmitted by the plurality of weighing assemblies as respective streams of weight measurement data-points (additional element evaluated in Prong 2);
ii. responsively to a change over time in the values of said weight measurement data-points and contingent upon said values reaching respective steady states according to a stability-tracking rule, determining a set of weight-event parameters of a weight event, the determined set of weight-event parameters comprising one or more products and a product-action respective of each one of the one or more products (making a determination based on changes in data is a judgement, and is a concept performed in the human mind and grouped as a mental process, see MPEP 2106.04(a)(2)(III) Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. A discussion of concepts performed in the human mind, as well as concepts that cannot practically be performed in the human mind and thus are not "mental processes", is provided below with respect to point A; linking to a system does not automatically exclude the system as a mental process, see MPEP 2106.04(a)(2)(III)(D) Examples of product claims reciting mental processes include: Computer readable storage media comprising computer instructions to implement a method for determining a price of a product offered to a purchasing organization – Versata, 793 F.3d at 1312-13, 115 USPQ2d at 1685); and
iii. performing at least one of:
(A) recording information about the results of the determining in a non-transient, computer-readable medium (recording/storing information is something that may be accomplished in the human mind, and therefore would be a mental process. See MPEP 2106.04(a)(2)(III)(C)(3) 3. Using a computer as a tool to perform a mental process. An example of a case in which a computer was used as a tool to perform a mental process is Mortgage Grader, 811 F.3d. at 1324, 117 USPQ2d at 1699. The patentee in Mortgage Grader claimed a computer-implemented system for enabling borrowers to anonymously shop for loan packages offered by a plurality of lenders, comprising a database that stores loan package data from the lenders, and a computer system providing an interface and a grading module), and
(B) displaying information about the results of the determining on a display device, wherein the weight measurement data-points comprise at least one type of data selected from the group comprising calculated weights and voltage inputs thereto (displaying information is another way of conveying information, when a computer displays information it is being used as a tool to do the same thing a human mind could do by simply stating the results, or writing down the results on a piece of paper, See MPEP 2106.04(a)(2)(III)(A) a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)).
When viewed alone and in ordered combination, these abstract idea limitations of claims 1, 10 and 27 recite abstract ideas.
Step 2A, Prong 2-This judicial exception is not integrated into a practical application because the additional elements fail to be more than tool to implement the abstract idea. (MPEP 2106.05(f)) The additional elements include weighing assemblies, computer processors, non-transient computer-readable storage medium, data transmitted by the plurality of weighing assemblies.
Weighing assemblies are being used to weight the products on a shelf, and data transmission by the weighing assemblies are used to obtain and transmit the information about the weight. The weighing assemblies is not more than a tool to perform the weighing of items on shelved, and perform the ordinary function of the tool. See MPEP 2106.05(f)(2) Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
Computer processors and non-transient computer-readable storage medium are tools used to store the abstract idea instructions, and perform the instruction on a computer. The processor and storage medium is not described or integrated in a way that is more meaningful as a technological placeholder to complete a process that would ordinarily be accomplished by a person, either on paper or in their mind. See MPEP 2106.05(f)(2) Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
When viewed alone and in ordered combination, these additional elements of claims 1, 10 and 27 do not integrate the abstract idea into a practical application.
Step 2B- The independent claims 1, (and similarly claims 10 and 27) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the use of computer elements in their ordinary capacity, such as using a weighing assembly to weigh products, processing information a processor, and storing information on a storage medium, the element do not provide significantly more than the abstract idea. See MPEP 2106.05(f).
When viewed alone and in ordered combination, these additional elements of claims 1, 10 and 27 do not provide significantly more than the abstract idea.
Dependent claims- Dependent claims 2-9, 11-13,15-19 and 36 further discuss the receiving information about additional data points used for determination on the processor, this includes analyzing information to make an estimate. Making a determination is an abstract idea, a mental process, that can be performed in the human mind. See MPEP 2106.04(a)(2(III). The determination on a processor, additionally uses the processor as a tool to implement the abstract idea, and does not integrate the idea into a practical application. See MPEP 2106.05(f).
When viewed alone and in ordered combination, these dependent claims do not add additional elements that integrate the abstract idea into a practical application or provide significantly more.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-13,15-19, and 27 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 10,198,710 B1 Hahn et al. ("Hahn").
Regarding claim 1, Hahn teaches a system for conducting a retail transaction (Hahn Abstract, inventory of items for sale), comprising:
a. a plurality of weighing assemblies in contact with a shelf and jointly operable to measure a combined weight of a shelf and of products arranged thereupon (Hahn Col. 7, Lns. 28-36, each shelf is lined with weight sensors to determine the combined weight of the shelf based on the number of products);
b. one or more computer processors (Hahn Col. 29, Lns. 41-45, the system includes hardware processors); and
c. a non-transient computer-readable storage medium comprising program instructions (Hahn Col. 29, Lns. 41-45, hardware includes memory to perform functions), which when executed by the one or more computer processors, cause the one or more computer processors to carry out the following steps:
i. monitoring weight measurement data corresponding to the shelf and a plurality of non-homogeneous products arranged thereupon, said weight measurement data transmitted by the plurality of weighing assemblies as respective streams of weight measurement data-points (Hahn Col. 7, Lns. 5-36, each shelf has lanes that may contain different inventory items, and each shelf is lined with weight sensors to determine the combined weight of the shelf based on the number of products);
ii. responsively to a change over time in the values of said weight measurement data-points and contingent upon said values reaching respective steady states according to a stability-tracking rule (Hahn Col. 6, Lns. 23-36, an event triggers the determinations, and it must be more than a noise vibration that occurs with simple movement, but above a certain threshold to start the determinization), determining a set of weight-event parameters of a weight event, the determined set of weight-event parameters comprising one or more products and a product-action respective of each one of the one or more products (Hahn Col. 6, Lns. 40-46, based on the change in weight the product removed from the shelf can be determined; Col. 33, Lns. 24-52, creating a baseline for noise, and when above a threshold, the inventory item is recorded); and
iii. performing at least one of:
(A) recording information about the results of the determining in a non-transient, computer-readable medium (Hahn Col. 32, Lns. 6-12, tracking and recording all inventory items and movement), and
(B) displaying information about the results of the determining on a display device (Hahn Col. 30, Lns. 17-32, display device to output determination values), wherein the weight measurement data-points comprise at least one type of data selected from the group comprising calculated weights and voltage inputs thereto (Hahn Col. 20, Lns. 1-14, the change in weight is determined, and using the location and weight combination, the product is determined; Col. 26, Lns. 1-5, the electrostatic field and current is used to determine the location of the item).
Regarding claim 2, Hahn teaches the system of claim 1, wherein the program instructions, when executed by the one or more computer processors, additionally cause the one or more computer processors to carry out the following step: receiving an indication of a transaction-initiation, wherein the carrying out of the monitoring step is in response to the receiving (Hahn Col. 33, Lns. 24-40, if the disturbance is beyond a specific threshold, then the initiation process is confirmed, and the transaction is recorded; Claim 16).
Regarding claim 3, Hahn teaches the system of claim 1, wherein the stability-tracking rule includes that said respective steady states for the streams of weight measurement data-points are defined by respective response-amplitude thresholds (Hahn Col. 3, Lns. 56-65, the capacity sensors have effect field, which is the maximum range for the sensor to read the weight difference; Col. 16, Lns. 45-51, capacity different is able to determine a weight change).
Regarding claim 4, Hahn teaches the system of claim 1 , wherein applying the stability tracking rule includes estimating bias in the weight measurement data-points and compensating for said bias (Hahn Col. 48, Lns. 31-40, when an event is initiated and a capacity value is larger than a threshold, depending on the circumstances, a filter function may be applied, therefore the filter is a rule and based on the data point alters the weight measurement based on the circumstance and rule, compensating for the determined factors).
Regarding claim 5, Hahn teaches the system of claim 1, wherein the weight measurement data-points either comprise voltage inputs or solely comprise voltage inputs (Hahn Col. 20, Lns. 1-10, the location and weight of items may use capacity (voltage) data).
Regarding claim 6, Hahn teaches the system of claim 1, wherein the determining step includes estimating a joint weight-location event-based product classifier (Hahn Col. 32, Lns. 27-40, the system may also track within the store, and using the location identify the item with the known weight and location data points).
Regarding claim 7, Hahn teaches the system of claim 6, wherein estimating the joint weight-location event- based product classifier includes estimating a joint weight-location probability density (Hahn Col. 12, Lns. 38-47, value is assigned to the weight event, and give a value indicative of a probability, giving a value to the probability estimate the likelihood of the event classifier).
Regarding claim 8, Hahn teaches the system of claim 6, wherein estimating the joint weight-location event-based product classifier includes applying a statistical classification mechanism trained by weight and location information to perform a statistical inference (Hahn Col. 17 Lns. 27-Col. 18, Lns. 5, the weight data and capacity data are both processed, the processing cancels out noise that may be steady state and is not actually a an instance, and uses the processing step to give weight to specific attributes, and then uses calculation to infer if an event is true or not, the truth is based on a value above a specific threshold).
Regarding claim 9, Hahn teaches the system of claim 1 , wherein the weight measurement data-points are the only inputs to the determining step that are generated by sensors during the transaction (Hahn Col. 3, Lns. 17-20, the inventory location may include capacitance and weight sensors, this allows for the option to use one or both, therefore, the ability to only use weight sensors is an option; Col. 6, Lns. 23-28, the event data includes one or more sensors).
Regarding claim 10, Hahn teaches a system for conducting a retail transaction (Hahn Abstract, inventory of items for sale), comprising:
a. a plurality of weighing assemblies in contact with a shelf and jointly operable to measure a combined weight of a shelf and of products arranged thereupon (Hahn Col. 7, Lns. 28-36, each shelf is lined with weight sensors to determine the combined weight of the shelf based on the number of products);
b. one or more computer processors (Hahn Col. 29, Lns. 41-45, he system includes hardware processors); and
c. a non-transient computer-readable storage medium comprising program instructions (Hahn Col. 29, Lns. 41-45, hardware includes memory to perform functions), which when executed by the one or more computer processors, cause the one or more computer processors to carry out the following steps:
i. monitoring weight measurement data corresponding to the weight of the shelf and a plurality of non-homogeneous products arranged thereupon, said weight measurement data transmitted from a plurality of weighing assemblies as respective streams of weight measurement data points (Hahn Col. 7, Lns. 5-36, each shelf has lanes that may contain different inventory items, and each shelf is lined with weight sensors to determine the combined weight of the shelf based on the number of products); and
ii. responsively to a change over time in the values of said weight measurement data, determining a set of weight-event parameters of a weight event, the determined set of weight-event parameters comprising one or more products and a product-action respective of each one of the one or more products (Hahn Col. 6, Lns. 40-46, based on the change in weight the product removed from the shelf can be determined; Col. 33, Lns. 24-52, creating a baseline for noise, and when above a threshold, the inventory item is recorded), wherein the determining comprises:
A. identifying one or more supported sets of weight-event parameters in a weight-location space (Hahn Col. 32, Lns. 6-12, tracking and recording all inventory items and movement), and
B. applying a joint weight-location event-based classification function to select a set of weight-event parameters from the identified one or more supported sets (Hahn Col. 32, Lns. 27-40, the system may also track within the store, and using the location identify the item with the known weight and location data points; Col. 32-34, using neural networks and algorithms for actions steps and analysis modules).
Regarding claim 11, Hahn teaches the system of claim 10, wherein applying the joint weight-location event- based classification function includes estimating a joint weight-location probability density (Hahn Col. 12, Lns. 38-47, value is assigned to the weight event, and give a value indicative of a probability, giving a value to the probability estimate the likelihood of the event classifier).
Regarding claim 12, Hahn teaches the system of claim 10, wherein applying the joint weight-location event- based classification function includes applying a statistical classification mechanism trained by weight and location information to perform a statistical inference (Hahn Col. 17 Lns. 27-Col. 18, Lns. 5, the weight data and capacity data are both processed, the processing cancels out noise that may be steady state and is not actually a an instance, and uses the processing step to give weight to specific attributes, and then uses calculation to infer if an event is true or not, the truth is based on a value above a specific threshold).
Regarding claim 13, Hahn teaches the system of claim 10 , wherein the determining is based on product weight-distribution data retrieved from a product database (Hahn Col. 31, Lns. 58-65, the information stored on a memory is a database).
Regarding claim 15, Hahn teaches the system of claim 10, wherein the estimating of the joint weight-location probability density includes iteratively improving initial weight and/or location estimations (Hahn Col. 41, Lns. 43-59, the value may be changed based one the fit within a model, the model uses a curve fitting technique, to model the correct value).
Regarding claim 16, Hahn teaches the system of claim 10, wherein the applying of the classification function includes Bayesian hierarchical modelling (Hahn Col. 41, Lns. 43-59, the value may be changed based one the fit within a model, the model uses a curve fitting technique, to model the correct value; Bayesian model creates a curve, then uses statistics to make a change to the value; Col. 17, Lns. 27-32, statistical functions).
Regarding claim 17, Hahn teaches the system of claim 10 , wherein the weight measurement data-points are the only inputs to said determining that are generated by sensors during the transaction (Hahn Col. 3, Lns. 17-20, the inventory location may include capacitance and weight sensors, this allows for the option to use one or both, therefore, the ability to only use weight sensors is an option; Col. 6, Lns. 23-28, the event data includes one or more sensors).
Regarding claim 18, Hahn teaches the system of claim 10, wherein the determining step is carried out responsively to an absolute value of the change over time in the values of said weight measurement data exceeding a pre-determined threshold (Hahn Col. 16, Lns. 38-51, rules and conditions are used to determine what a change in weight means, even if it exceeds the threshold amount set).
Regarding claim 19, Hahn teaches the system of claim 10 , wherein the weight measurement data-points either comprise voltage inputs or solely comprise voltage inputs (Hahn Col. 20, Lns. 1-10, the location and weight of items may use capacity (voltage) data).
Regarding claim 27, Hahn teaches a system for conducting a retail transaction (Hahn Abstract, inventory of items for sale), comprising:
a. a plurality of weighing assemblies in contact with a shelf and jointly operable to measure a combined weight of a shelf and of products arranged thereupon (Hahn Col. 7, Lns. 28-36, each shelf is lined with weight sensors to determine the combined weight of the shelf based on the number of products);
b. one or more computer processors (Hahn Col. 29, Lns. 41-45, he system includes hardware processors); and
c. a non-transient computer-readable storage medium comprising program instructions (Hahn Col. 29, Lns. 41-45, hardware includes memory to perform functions), which when executed by the one or more computer processors, cause the one or more computer processors to carry out the following steps:
i. receiving an indication of a transaction-initiation (Hahn Col. 33, Lns. 24-40, if the disturbance is beyond a specific threshold, then the initiation process is confirmed, and the transaction is recorded; Claim 16);
ii. in response to said receiving, monitoring weight measurement data corresponding to the shelf and a plurality of non-homogeneous products arranged thereupon, said weight measurement data transmitted by the plurality of weighing assemblies as respective streams of weight measurement data-points (Hahn Col. 7, Lns. 5-36, each shelf has lanes that may contain different inventory items, and each shelf is lined with weight sensors to determine the combined weight of the shelf based on the number of products);
iii. responsively to a change over time in the values of said weight measurement data-points, determining a set of weight-event parameters of a weight event using at least one stability rule (Hahn Col. 6, Lns. 23-36, an event triggers the determinations, and it must be more than a noise vibration that occurs with simple movement, but above a certain threshold to start the determinization), the determined set of weight-event parameters comprising one or more products and a product- action respective of each one of the one or more products (Hahn Col. 6, Lns. 40-46, based on the change in weight the product removed from the shelf can be determined; Col. 33, Lns. 24-52, creating a baseline for noise, and when above a threshold, the inventory item is recorded); and
iv. performing at least one of:
(i) recording information about the results of the determining in a non-transient, computer-readable medium (Hahn Col. 32, Lns. 6-12, tracking and recording all inventory items and movement), and
(ii) displaying information about the results of the determining on a display device (Hahn Col. 30, Lns. 17-32, display device to output determination values).
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.
Claim 36 are rejected under 35 U.S.C. 103 as being unpatentable over Hahn in view of US 2019/0324442 A1 Cella et al. (herinafter Cella).
Regarding claim 36, Hahn teaches the system of claim 4. Hahn fails to explicitly disclose wherein estimating bias in the weight measurement data-points includes using a clustering algorithm. Cella is in the field of data collection (Abstract data collection) and teaches wherein estimating bias in the weight measurement data-points includes using a clustering algorithm (Cella [1034] weights and bias are used in a model and created by experts; [1506] the different models of neural networks include clustering). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the bias taught in Hahn with the clustering algorithm of Cella. The motivation for doing so would be to give specific factors more weight based on desired outcomes by bias the factors with more weight (Cella [1036] bias towards one goal, by using a weight).
Response to Arguments
Applicant's arguments filed 05/27/2025 have been fully considered but they are not persuasive.
Regarding 101
Applicant asserts that a determination is made in response to a change over time, and using a stability tracking rule, however, most determinations when made by a human, also has a set of parameters to make an accurate determination. Therefore, this does not show a technological improvement, but rather showcases the mathematical aspect of how the determination is accomplished. The improvement to the technical field still needs to be technical in nature. As if the improvement is a specific way to perform a calculation, this is a mathematical concept, which is an abstract idea, and if there are not details, this is a mental process to make a determination. If the improvement to the technical field is the abstract idea, then it cannot be more than the abstract idea. Transformation of raw data to parameters, or rules to perform a calculation, that if according to the Applicant are beyond human cognition, they are still calculations performed by a computer. Without additional information, the claims are directed to a black box that takes in information, and spits out information, and therefore, does not showcase how a computer processor perform any function. Stating that a computer processes the information is using the technology as a link to the abstract idea, but nothing more. Therefore, the claims remain rejected under 101.
Regarding 102:
As to claim 1:
“contingent upon said values reaching respective steady states according to a stability-tracking rule”
As pointed out by the Applicant, the Specification of the instant application [0062] points to at least four different stability tracking rules, one of which is a shock response to transaction initiation, which is further described on Pg. 7, Lns. 25-31, where the transaction initiation may be a door opening or closing, or a person reaching into the space. The definition of stability tracking rule includes numerous ways to determine if the weight is stable, and that include to remove the weight being monitored from simply initiating the transaction. Therefore, when Hahn, Col. 6, Lns. 35-36, describe a method that may determine when the weight that is being measured is outside a threshold amount, ie, too little from merely opening the door, or a person entering the space, the weight is not stable enough to record. Leaving Hahn teaching the recording of only weights once it is within a specific threshold based on some rule set about the initiation, and therefore the 102 rejection remains valid.
“monitoring weight measurement data corresponding to the shelf and a plurality of non-homogenous products arranged thereupon”
While Hahn may teach lanes, and the lanes include a singular item, the shelf itself holds multiple types of items, and the monitoring and identification are for a plurality of different object, and therefore teaches non-homogenous items arranged on a shelf, See Hahn Fig. 13, shelf 102(1) and 102(2) with six different products, and teaches weight measurement data corresponding to a shelf, and the shelf has a plurality of non-homogenous product arranged thereon. Hahn teaches the literal meaning of the claim limitation, and therefore the novelty rejection remains valid.
As to claim 10 (similarly claims 6-8):
“identifying one or more supported sets of weight-event parameters in a weight-location space, and applying a joint weight-location event-based classification function to select a set of weight-event parameters from the identified one or more supported sets”
According to the applicant [0152] the weight-event parameter is used to identify location within a space, this uses a regression model to estimate location of the item removed/added/moved. Then the weight event parameters are further limited to be from one or more supported sets, defined in the instant application as historical weight data, product positioning plan, or external data available. Under Hahn Col. 32, there is a data acquisition module, which is able to obtain information about the weight of items, in addition to the location of the item based on the planned layout of the store, and therefore is able to teach one or more supported sets of weight and location information.
The second sub step included applying a joint weight classification function, and Applicant states [0167-0168] describe using a probability density, which is a statistical inference which uses weight and location information. Additionally, in Hahn Col. 32-34, the analysis module, and the action module use this information with algorithm and neural networks to create a determination that would associate the weight and location to make the final determination. The claim must select the parameters, and therefore analysis using an algorithm performs the same selection as a probability density function.
As to claim 27, see the same “stability-tracking rule” and “non-homogenous products” and discussed in claim 1.
As to claims 2-13 and 15-19, claim 1 and 10 are not in condition for allowance, and therefore remain rejected under 102.
New claim 36 adds a clustering algorithm which is not found in Hahn, and therefore new art Cella is added.
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 2021/0148750 A1 Trakhimovich teaches shelving to monitor items (Abstract); US 2007/0210154 A1 Suto teaches weighing an item to determine its identity (Abstract); US 2010/0187306 A1 Solomon teaches inventory control (Abstract); US 2018/0330416 A1 Bonner et al. teaches weight sensor shelves (Abstract).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JESSICA E SULLIVAN whose telephone number is (571)272-9501. The examiner can normally be reached M-Th; 9:00 AM-5PM EST.
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, FAHD OBEID can be reached at (571) 270-3324. 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.
/JESSICA E SULLIVAN/Examiner, Art Unit 3627
/MICHAEL JARED WALKER/Primary Examiner, Art Unit 3627