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 03/03/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings filed on 03/03/2025 are accepted 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 1-10 are rejected under 35 USC 101. The claimed invention is directed to non-statutory subject matter because claims 1-10 are directed to an abstract idea without significantly more.
The claims 1-10 recite recognizing, by means of a product recognition unit, a first product put on a checkout counter comprise the step of: identifying the size and shape of the first product, acquiring the sectional images of the first product, and identifying the size and shape of the first product based on the sectional images; measuring the weight; and measuring the temperature; selecting, based on the recognition result for the first product, at least one trained model for identifying the first product comprises the steps of calculating a first reference for the first product, and selecting the at least one trained model based on the first reference; acquiring a first image of the first product by means of a product identification unit; identifying the first product, based on the at least one trained model and the first image comprise the steps of calculating second images, comparing the first image with the second images, and identifying the first product as a second product having the highest similarity, determining whether the sectional images include identification codes, recognizing the identification codes and determining the first product, and transmitting a notification if there is no product having the degree of similarity greater than a predetermined degree of similarity.
Claims 1-10 recite recognizing, measuring, selecting, calculating, identifying, comparing, determining, and transmitting steps as drafted, are processes that under broadest reasonable interpretation, cover performance of managing commercial interactions and fundamental economic practices and cover performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting “an unmanned store product checkout device, a memory in which at least one program is recorded; and a processor for executing the program, the program comprising commands for executing the steps of”, nothing in the claim element precludes the steps from practically being performed by organizing human activity for commercial interactions and fundamental economic practices and being performed in the mind. For example, but for “the unmanned store product checkout device, the memory, and the processor” in the context of these claims encompasses a person manually recognizes a first product by identifying the size and shape of the first product based on the sectional images, measuring the weight; and measuring the temperature, calculates a first reference for the first product, selects at least one trained model based on the first reference, calculates second images, compare the first images and the second image, identifies the first product as a second product having the highest similarity and the at least one trained model, determines whether the sectional images include identification codes, recognizes the identification codes, determines the first product, and transmits a notification if there is no product having the degree of similarity greater than a predetermined degree of similarity. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by managing commercial interactions and fundamental economic practices but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because acquiring step is recited at a high level of generality (i.e., as a general means of acquiring image data) and amounts to mere data gathering, which is a form of insignificant extra-solution activity. This judicial exception is not integrated into a practical application because the claims as a whole merely describe how to generally “apply” the concept of recognizing, measuring, selecting, acquiring, calculating, identifying, comparing, determining, and transmitting in a computer environment. The claimed computer components such as the unmanned store product checkout device, the memory, and the processor are recited at a high level of generality and are merely invoked as tools to perform recognizing, measuring, selecting, acquiring, calculating, identifying, comparing, determining, and transmitting steps. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. 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 claims 1-10 are directed to an abstract idea.
The claims 1-10 do 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 elements of using the unmanned store product checkout device, the memory, and the processor to perform recognizing, measuring, selecting, acquiring, calculating, identifying, comparing, determining, and transmitting steps amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Therefore, the claims do not amount to significantly more than the recited abstract idea (Step 2B: NO). The claims 1-10 are not patent eligible.
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 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 1-10 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Patent No. 11,393,253 to Maron et al.
With regard to claims 1 and 9, Maron discloses a device for product checkout at an unmanned store, comprising:
a memory in which at least one program is recorded (fig, 5, memory 516); and
a processor for executing the program, the program comprising commands for executing the steps of (fig. 5, processor 502):
recognizing, by means of a product recognition unit, a first product put on a checkout counter (col. 7, lines 63- col. 8, line 1, FIG. 1 illustrates an example environment 100 of a materials handling facility 102 that includes an item-identifying tote 104 to identify items 106 placed in, and removed from, the tote 104 by a user 108.);
selecting, based on the recognition result for the first product, at least one trained model for identifying the first product (col. 29, lines 29-52, If the item has been identified, then at an operation 620 the system may store this identification data indicating the item identifier of the common item. If not, or after storing the identification data, at an operation 622 the system may analyze the trajectory data and/or the identification data and, at an operation 624, may determine an item identifier of the common item and an action taken with respect to the common item. As described above, this may include inputting the trajectory data and/or the item-identification data into a decision classifier, configured to output an identifier of an item, an action taken with respect to the item, and confidence-level data indicating a confidence level associated with this determination);
acquiring a first image of the first product by means of a product identification unit (col. 3, lines 1-5, According to the techniques described herein, an item-identifying tote (or “smart tote”) may include one or more cameras coupled to the tote to generate image data representing items that a user places in the tote, and/or removes from the tote.); and
identifying the first product, based on the at least one trained model and the first image (col. 3, lines 5-21, The tote may include one or more components (e.g., software component(s), hardware processor(s), etc.) that analyze the image data to determine an item identifier for the item(s) placed in the tote, or removed from the tote, and update a virtual item listing for the user of the tote.).
With regard to claim 2, Maron discloses the step of recognizing the first product comprises the steps of : identifying the size and shape of the first product by means of cameras; measuring the weight of the first product by means of a weight sensor; and measuring the temperature of the first product by means of a thermographic camera (col. 5, lines 41-58, col. 6, lines 27-30, and col. 14, lines 59-col. 15, lines 4, the item-identification component may use the image data and/or other data (e.g., weight data, etc.) to identify the item that was placed into or removed from the tote. In other instances, the item-identification component may use other visual indicia of the item interest (e.g., shape, color, etc.) to identify the item. Further, the decision component may make the determination of these actions with reference to the trajectory data, weight data acquired by one or more weight sensors of the tote, and/or additional sensor data. The facility sensors 212 may include imaging sensors 214 (e.g., cameras), weight sensor(s) 218, and/or other sensors (e.g., temperature sensors)).
With regard to claim 3, Maron discloses the step of identifying the size and shape of the first product comprises the steps of: acquiring the sectional images of the first product by means of the cameras; and identifying the size and shape of the first product, based on the sectional images of the first product (col. 5, lines 41-58 and col. 14, lines 59-col. 15, lines 4, In one example, the item-identification component analyzes one or more frames of the image data depicting the item-of-interest to identify a barcode or other identifier of the item and may use the barcode to determine the item identifier of the item. In other instances, the item-identification component may use other visual indicia of the item interest (e.g., shape, color, etc.) to identify the item.).
With regard to claim 4, Maron discloses the step of selecting the at least one trained model comprises the steps of: calculating a first reference corresponding to the recognition result for the first product; and selecting the at least one trained model from a plurality of trained models, based on the first reference (col. 22,lines 33-61, and col. 29, lines 29-52, For example, the item-identification component may use visual characteristics of the respective regions-of-interest, weight data, and/or any other type of sensor data to identify one or more items. In the illustrated example, the item-identification component has identified a first item (“ABC chips”) 408(1) (common to the regions-of-interest 406(1)-(2)) from the first image frame 402(1) and a second item (“XYZ Peanuts”) 408(2) (common to the regions-of-interest 404(1)-(N)) from the second image frame 402(2). The item-identification component may then provide this item-identification data to the decision component 140, as described above.).
With regard to claim 5, Maron discloses the step of identifying the first product comprises the steps of: calculating second images corresponding to the recognition result by means of the at least one trained model (col. 11, lines 21-42, For example, upon identifying the region-of-interest corresponding to the first bounding box 132(1)); comparing the first image with the second images by means of image matching (col. 11, lines 21-42, the segmentation component 130 may compare visual characteristics of this portion of the frame to visual characteristics of previously identified regions-of-interest); and identifying the first product as a second product having the highest similarity under the result of the image matching (col. 11, lines 21-42, determine whether the regions-of-interest represent a common item. For example, the segmentation component may compute a similarity between regions of interest in order to determine whether the regions-of-interest represent a common item. If the similarity is greater than a threshold, then the region-of-interest corresponding to the bounding box 132(1) may be associated with that previously identified region-of-interest.).
With regard to claim 6, Maron discloses further comprising the steps of: determining, after the step of acquiring the sectional images of the first product, whether the sectional images include identification codes for the first product; and recognizing, if it is determined that the identification codes are included, the identification codes and determining the first product as a third product corresponding to the identification codes, wherein the identification codes are located on the outer wrapped surface of a product and include identification information for the product (col. 5, lines 41-58, the item-identification component analyzes one or more frames of the image data depicting the item-of-interest to identify a barcode or other identifier of the item and may use the barcode to determine the item identifier of the item. Furthermore, a conditional limitation is a claim feature that depends on a certain condition being present. For example, when or if condition X is present, feature Y is implemented or has effect. Without condition X, feature Y may be dormant or have no effect. The claim recites if it is determined that the identification codes are included (condition A), determining the first product (step A). When the condition A is not occurred, steps A is not invoked. Therefore, this limitation has no patentable weight).
With regard to claim 7, Maron discloses further comprising the step of transmitting, after the step of comparing the first image with the second images by means of the image matching, a notification that it is necessary to check the first product to a manager terminal if there is no product having the degree of similarity greater than a predetermined degree of similarity to the first product under the result of the image matching (col. 11, lines 36-42 and col. 18, lines 46-60, If, however, the item-of-interest in the first bounding box 132(1) is not determined to correspond to a previously identified region-of-interest (e.g., because no comparison resulted in a similarity that is greater than the threshold), then the segmentation may generate a new identifier for that region-of-interest. For instance, if the item-identification component 534 is unable to determine an item identifier 462 for an item 106 shown in the image data 532, the user-interface component 556 may receive inquiry data 544 generated by an inquiry component 560 to prompt a user 108 or a human associate at the facility 102 for feedback to help identify the item 106, and/or other information (e.g., if multiple items were placed in the tote 104). Furthermore, a conditional limitation is a claim feature that depends on a certain condition being present. For example, when or if condition X is present, feature Y is implemented or has effect. Without condition X, feature Y may be dormant or have no effect. The claim recites if there is no product having the degree of similarity greater than a predetermined degree of similarity (condition A), transmitting a notification (step A). When the condition A is not occurred, steps A is not invoked. Therefore, this limitation has no patentable weight).
With regard to claim 8, Maron discloses the trained model comprises trained models for a plurality of references for the products sorted according to the sizes, shapes, weights, and temperatures of products, and if a specific product passes through the product recognition unit, training comprises supervised training capable of identifying that the specific product is a right product (col. 3, lines 30-52 and col. 14, lines 59-col. 15, lines 4, For example, the activity classifier may have been trained, using supervised learning, by inputting, into the classifier, positive examples of the predefined activity. For example, feature data associated with image data representing users putting items into and removing items from totes, along with labels indicating that the image data represents the predefined activity, may have been input to the activity classifier during training. A conditional limitation is a claim feature that depends on a certain condition being present. For example, when or if condition X is present, feature Y is implemented or has effect. Without condition X, feature Y may be dormant or have no effect. The claim recites if a specific product passes through the product recognition unit (condition A), training (step A). When the condition A is not occurred, steps A is not invoked. Therefore, this limitation has no patentable weight).
With regard to claim 10, Maron discloses A system for product checkout at an unmanned store, comprising:
a product recognition unit for identifying the size and shape of a product by means of cameras, measuring the weight of the product by means of a weight sensor, and measuring the temperature of the product by means of a thermographic camera (col. 5, lines 41-58, col. 6, lines 27-30, and col. 14, lines 59-col. 15, lines 4);
a product identification unit for acquiring the image of the product (col. 3, lines 1-5); and
an unmanned store product checkout device for receiving the recognition result for the product from the product recognition unit, receiving the image of the product from the product identification unit, and identifying the product (col. 3, lines 5-21).
Conclusion
Please refer to form 892 for cited references.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ARIEL J YU whose telephone number is (571)270-3312. The examiner can normally be reached 11AM - 7PM (M-F).
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, Obeid Fahd A can be reached on 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.
/ARIEL J YU/Primary Examiner, Art Unit 3627
Prosecution Insights