DETAILED ACTION
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/05/24 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 § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Glaser US 2017/0323376 in view of Marder US 2018/0068256.
As to claim 1, Glaser teaches a method for identifying an inventory item, the method comprising: using two or more sensors to produce two or more outputs associated with an inventory item, [figs. 1-3; ¶ 0070-0072; ¶ 0129-0130] wherein each output includes at least one of (i) a possible identifier for the inventory item and (ii) a possible event type indicating whether the inventory item has been taken from a shelf or placed onto a shelf; [fig. 11; ¶ 0065-0068; ¶ 0084; ¶ 0088; ¶ 0120-0123; ¶ 0142-0145; ¶ 0155; ¶ 0179; ¶ 0182; ¶ 0188; ¶ 0191] generating two or more confidence levels for the inventory item, wherein each respective confidence level is based on a subset of one or more outputs selected from the two or more outputs; [¶ 0065-0068; ¶ 0152; ¶ 0197] identifying, based on the two or more confidence levels, a prediction for the inventory item including at least one of (i) a predicted identifier for the inventory item and (ii) a predicted event type for the inventory item; [¶ 0062-0068; ¶ 0104-0105; ¶ 0145; ¶ 0153-0155]
Glaser teaches collecting data from or updating supplementary data which includes inventory maps/planograms. [¶ 0079; ¶ 0139-0141; ¶ 0159] Glaser does not explicitly teach modifying, based on the prediction for the inventory item, a planogram.
Marder teaches modifying, based on the prediction for the inventory item, a planogram. [¶ 0016-0021]
It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the techniques of Marder with the teachings of Glaser allowing for improved compliance.
As to claim 2, Glaser (modified by Marder) teaches the limitations of claim 1. Marder teaches wherein the planogram is a map indicating current locations of inventory items within a shopping store. [¶ 0002; ¶ 0016]
As to claim 3, Glaser (modified by Marder) teaches the limitations of claim 1. Glaser teaches further comprising: analyzing store activity data to generate data analytic visualizations, [¶ 0035-0037; ¶ 0058-0068] wherein the store activity data includes at least one of: (i) one or more predictions for at least one inventory item and (ii) planogram data; [¶ 0062-0068; ¶ 0079; ¶ 0104-0105; ¶ 0139-0141; ¶ 0145; ¶ 0153-0155; ¶ 0159] and updating the data analytics visualizations with at least one indicator of the inventory item indicating one or more of a confidence level associated with the prediction for the inventory item or a location of the inventory item. [¶ 0107; ¶ 0111-0117; ¶ 0236-0241]
As to claim 4, Glaser (modified by Marder) teaches the limitations of claim 1. Glaser teaches further comprising notifying an employee of the inventory item requiring a manual review, in response to an indicator of the inventory item indicating one or more of a confidence level below a pre-determined threshold. [¶ 0107; ¶ 0111-0117; ¶ 0236-0241]
As to claim 5, Glaser (modified by Marder) teaches the limitations of claim 1. Glaser teaches further comprising: analyzing store activity data to generate data analytic visualizations, [¶ 0035-0037; ¶ 0058-0068] wherein the store activity data includes at least one of: (i) one or more predictions for at least one inventory item and (ii) planogram data; [¶ 0062-0068; ¶ 0079; ¶ 0104-0105; ¶ 0139-0141; ¶ 0145; ¶ 0153-0155; ¶ 0159] and updating the data analytics visualizations with at least one indicator of the store activity data, wherein the store activity data relates to shopper-inventory item interactions, loss prevention support, or employee activity. [¶ 0035-0037; ¶ 0058-0068; ¶ 0104-0105; ¶ 0145; ¶ 0153-0155]
As to claim 6, Glaser (modified by Marder) teaches the limitations of claim 5. Glaser teaches wherein the at least one indicator of the store activity data triggers a notification, to an employee of the shopping store, of the store activity data. [¶ 0107; ¶ 0111-0117; ¶ 0236-0241]
As to claim 7, Glaser (modified by Marder) teaches the limitations of claim 1. further comprising: analyzing store activity data to calculate an inventory audit comprising a count of items in the shopping store per identifier, [¶ 0084; ¶ 0139-0145] wherein the store activity data includes at least one of: (i) one or more predictions for at least one inventory item and (ii) planogram data; [¶ 0062-0068; ¶ 0079; ¶ 0104-0105; ¶ 0139-0141; ¶ 0145; ¶ 0153-0155; ¶ 0159] generating, based on the planogram and inventory audit, data analytic visualizations; [¶ 0066; ¶ 0084; ¶ 0104-0105; ¶ 0139-0145; ¶ 0153; ¶ 0230-0231] and updating the data analytic visualizations with an indicator notifying that the inventory item is out-of-stock in response to the count of items for the inventory item being equal to zero. [¶ 0059-0064; ¶ 0104-0105; ¶ 0111-0112; ¶ 0153; ¶ 0229-0231]
As to claim 8, Glaser (modified by Marder) teaches the limitations of claim 1. further comprising: analyzing store activity data to calculate an inventory audit comprising a count of items in the shopping store per identifier, [¶ 0084; ¶ 0139-0145] wherein the store activity data includes at least one of: (i) one or more predictions for at least one inventory item and (ii) planogram data; [¶ 0062-0068; ¶ 0079; ¶ 0104-0105; ¶ 0139-0141; ¶ 0145; ¶ 0153-0155; ¶ 0159] generating, based on the planogram and inventory audit, data analytic visualizations; [¶ 0066; ¶ 0084; ¶ 0104-0105; ¶ 0139-0145; ¶ 0153; ¶ 0230-0231] and updating the data analytic visualizations with an indicator notifying that the inventory item is low in stock in response to the count of items for the inventory item being less than, or equal to, a pre-determined threshold value. [¶ 0045; ¶ 0059-0064; ¶ 0104-0105; ¶ 0111-0112; ¶ 0229-0233]
As to claim 9, Glaser (modified by Marder) teaches the limitations of claim 1. further comprising: analyzing store activity data to calculate an inventory audit comprising a count of items in the shopping store per identifier, [¶ 0084; ¶ 0139-0145] wherein the store activity data includes at least one of: (i) one or more predictions for at least one inventory item and (ii) planogram data; [¶ 0062-0068; ¶ 0079; ¶ 0104-0105; ¶ 0139-0141; ¶ 0145; ¶ 0153-0155; ¶ 0159] generating, based on the planogram and inventory audit, data analytic visualizations; [¶ 0066; ¶ 0084; ¶ 0104-0105; ¶ 0139-0145; ¶ 0153; ¶ 0230-0231] and updating the data analytic visualizations with an indicator of a quantity of the inventory item, wherein the indicator is updated in response to a predicted event type for the inventory item. [¶ 0059-0064; ¶ 0104-0105; ¶ 0111-0112; ¶ 0153; ¶ 0229-0231]
As to claim 10, Glaser (modified by Marder) teaches the limitations of claim 1. further comprising: analyzing store activity data to calculate an inventory audit comprising a count of items in the shopping store per identifier, [¶ 0084; ¶ 0139-0145] wherein the store activity data includes at least one of: (i) one or more predictions for at least one inventory item and (ii) planogram data; [¶ 0062-0068; ¶ 0079; ¶ 0104-0105; ¶ 0139-0141; ¶ 0145; ¶ 0153-0155; ¶ 0159] generating, based on the planogram and inventory audit, data analytic visualizations; [¶ 0066; ¶ 0084; ¶ 0104-0105; ¶ 0139-0145; ¶ 0153; ¶ 0230-0231] updating the data analytic visualizations with an indicator of a quantity of the inventory item, wherein the indicator is updated in response to a predicted event type for the inventory item; [¶ 0045-0046; ¶ 0066; ¶ 0104-0105; ¶ 0126-0128] and updating the indicator to indicate a prediction that the inventory item will be imminently out-of-stock based on a tracked rate of change for the quantity of the inventory item. [¶ 0059-0064; ¶ 0104-0105; ¶ 0111-0112; ¶ 0153; ¶ 0229-0231]
As to claim 11, Glaser (modified by Marder) teaches the limitations of claim 1. Glaser teaches wherein a confidence level indicates a probability of a particular possible identifier for the inventory item being a correct identifier for the inventory item. [¶ 0045; ¶ 0061-0068; ¶ 0084; ¶ 0106-0108; ¶ 0155]
As to claim 12, Glaser (modified by Marder) teaches the limitations of claim 1. Glaser teaches wherein a confidence level indicates a probability of a particular possible event type for the inventory item being a correct event type for the inventory item. [¶ 0045; ¶ 0061-0068; ¶ 0084; ¶ 0088; ¶ 0106-0108; ¶ 0120-0123; ¶ 0155; ¶ 0179; ¶ 0188; ¶ 0191]
As to claim 13, Glaser (modified by Marder) teaches the limitations of claim 1. Glaser teaches wherein a confidence level indicates a probability of an event type for the inventory item being an event involving a shopper interaction with the inventory item or an employee interaction with the inventory item. [¶ 0045; ¶ 0061-0068; ¶ 0084; ¶ 0088; ¶ 0106-0108; ¶ 0120-0123; ¶ 0155; ¶ 0179; ¶ 0188; ¶ 0191]
As to claim 14, Glaser (modified by Marder) teaches the limitations of claim 1. Glaser teaches wherein a confidence level indicates a probability of an event type for the inventory item being an event involving the inventory item being taken off the shelf, wherein the event is not accounted for by a sale in the shopping store. [¶ 0059; ¶ 0181-0186; ¶ 0188; ¶ 0190-0191; ¶ 0234-0235; ¶ 0251-0253]
As to claim 15, Glaser (modified by Marder) teaches the limitations of claim 1. Glaser teaches wherein a confidence level indicates a probability of an event type for the inventory item being an event involving the inventory item being placed onto the shelf at an incorrect location. [¶ 0046; ¶ 0055; ¶ 0062-0068; ¶ 0073-0074; ¶ 0126; ¶ 0177-0178; ¶ 0234-0235]
As to claim 16, Glaser (modified by Marder) teaches the limitations of claim 1. Glaser teaches wherein a confidence level indicates a probability of an event type for the inventory item being an event involving a particular quantity of the inventory item. [¶ 0046; ¶ 0054; ¶ 0062-0066; ¶ 0105-0106; ¶ 0126; ¶ 0153; ¶ 0232-0233]
As to claim 17, Glaser (modified by Marder) teaches the limitations of claim 1. Glaser teaches wherein the generating of a confidence level for the inventory item further comprises utilizing one or more neural network models to generate the confidence level for the inventory item. [¶ 0045; ¶ 0064-0065; ¶ 0085 ¶ 0148]
As to claim 18, Glaser (modified by Marder) teaches the limitations of claim 2. Glaser teaches wherein a first confidence level for the inventory item indicates a probability of a first prediction for the inventory item being a correct prediction and a second confidence level for the inventory item indicates a probability of a second prediction for the inventory item being a correct prediction, the first prediction predicting at least one of a different identifier or a different event type for the inventory item from the second prediction. [¶ 0160-0167; ¶ 0181-0183]
As to claim 19, Glaser teaches a system including one or more processors coupled to memory, the memory being loaded with computer instructions to identify an identifying an inventory item, the instructions, when executed on the processors, [¶ 0256] implement actions comprising: using two or more sensors to produce two or more outputs associated with an inventory item, [figs. 1-3; ¶ 0070-0072; ¶ 0129-0130] wherein each output includes at least one of (i) a possible identifier for the inventory item and (ii) a possible event type indicating whether the inventory item has been taken from a shelf or placed onto a shelf; [fig. 11; ¶ 0065-0068; ¶ 0084; ¶ 0120-0123; ¶ 0142-0145; ¶ 0155; ¶ 0179; ¶ 0182; ¶ 0188; ¶ 0191] generating two or more confidence levels for the inventory item, wherein each respective confidence level is based on a subset of one or more outputs selected from the two or more outputs; [¶ 0065-0068; ¶ 0152; ¶ 0197] identifying, based on the two or more confidence levels, a prediction for the inventory item including at least one of (i) a predicted identifier for the inventory item and (ii) a predicted event type for the inventory item [¶ 0062-0068; ¶ 0104-0105; ¶ 0145; ¶ 0153-0155]
Glaser teaches collecting data from or updating supplementary data which includes inventory maps/planograms. [¶ 0079; ¶ 0139-0141; ¶ 0159] Glaser does not explicitly teach modifying, based on the prediction for the inventory item, a planogram.
Marder teaches modifying, based on the prediction for the inventory item, a planogram. [¶ 0016-0021]
It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the techniques of Marder with the teachings of Glaser allowing for improved compliance.
As to claim 20, Glaser teaches a non-transitory computer readable storage medium impressed with computer program instructions for identifying an identifying an inventory item, the instructions, when executed on a processor, [¶ 0256] causing the processor to implement a method comprising: using two or more sensors to produce two or more outputs associated with an inventory item, [figs. 1-3; ¶ 0070-0072; ¶ 0129-0130] wherein each output includes at least one of (i) a possible identifier for the inventory item and (ii) a possible event type indicating whether the inventory item has been taken from a shelf or placed onto a shelf; [fig. 11; ¶ 0065-0068; ¶ 0084; ¶ 0120-0123; ¶ 0142-0145; ¶ 0155; ¶ 0179; ¶ 0182; ¶ 0188; ¶ 0191] generating two or more confidence levels for the inventory item, wherein each respective confidence level is based on a subset of one or more outputs selected from the two or more outputs; [¶ 0065-0068; ¶ 0152; ¶ 0197] identifying, based on the two or more confidence levels, a prediction for the inventory item including at least one of (i) a predicted identifier for the inventory item and (ii) a predicted event type for the inventory item; [¶ 0062-0068; ¶ 0104-0105; ¶ 0145; ¶ 0153-0155]
Glaser teaches collecting data from or updating supplementary data which includes inventory maps/planograms. [¶ 0079; ¶ 0139-0141; ¶ 0159] Glaser does not explicitly teach modifying, based on the prediction for the inventory item, a planogram.
Marder teaches modifying, based on the prediction for the inventory item, a planogram. [¶ 0016-0021]
It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the techniques of Marder with the teachings of Glaser allowing for improved compliance.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANNER HOLDER whose telephone number is (571)270-1549. The examiner can normally be reached M-F 7:30-4.
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/ANNER HOLDER/Primary Examiner, Art Unit 2483