DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 07/15/2026 has been entered.
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 .
Disposition of Claims
Claims 1-10 are pending in the instant application. Claims 6-7, and 9-10 have been cancelled. No claims have been added. Claims 1-5 and 8 have been amended. Claims 1-5 and 8 are rejected herein. The rejection of the pending claims is hereby made non-final.
Response to Remarks
101
Regarding the rejection of the pending claims under 35 USC 101, the examiner has considered Applicant’s arguments and amendments but does not find them to be persuasive. If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement (see at least MPEP 2106.05(a) Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316, 120 USPQ2d 1353, 1359 (Fed. Cir. 2016). The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018)). Thus, it is important for examiners to analyze the claim as a whole when determining whether the claim provides an improvement to the functioning of computers or an improvement to other technology or technical field.
Examples that the courts have indicated may show an improvement in computer-functionality:
i. A modification of conventional Internet hyperlink protocol to dynamically produce a dual-source hybrid webpage, DDR Holdings, 773 F.3d at 1258-59, 113 USPQ2d at 1106-07;
ii. Inventive distribution of functionality within a network to filter Internet content, BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1350-51, 119 USPQ2d 1236, 1243 (Fed. Cir. 2016);
iii. A method of rendering a halftone digital image, Research Corp. Techs. v. Microsoft Corp., 627 F.3d 859, 868-69, 97 USPQ2d 1274, 1380 (Fed. Cir. 2010);
iv. A distributed network architecture operating in an unconventional fashion to reduce network congestion while generating networking accounting data records, Amdocs (Israel), Ltd. v. Openet Telecom, Inc., 841 F.3d 1288, 1300-01, 120 USPQ2d 1527, 1536-37 (Fed. Cir. 2016);
v. A memory system having programmable operational characteristics that are configurable based on the type of processor, which can be used with different types of processors without a tradeoff in processor performance, Visual Memory, LLC v. NVIDIA Corp., 867 F.3d 1253, 1259-60, 123 USPQ2d 1712, 1717 (Fed. Cir. 2017);
vi. Technical details as to how to transmit images over a cellular network or append classification information to digital image data, TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614-15, 118 USPQ2d 1744, 1749-50 (Fed. Cir. 2016) (holding the claims ineligible because they fail to provide requisite technical details necessary to carry out the function);
vii. Particular structure of a server that stores organized digital images, TLI Communications, 823 F.3d at 612, 118 USPQ2d at 1747 (finding the use of a generic server insufficient to add inventive concepts to an abstract idea);
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viii. A particular way of programming or designing software to create menus, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1241, 120 USPQ2d 1844, 1854 (Fed. Cir. 2016);
ix. A method that generates a security profile that identifies both hostile and potentially hostile operations and can protect the user against both previously unknown viruses and "obfuscated code," which is an improvement over traditional virus scanning. Finjan Inc. v. Blue Coat Systems, 879 F.3d 1299, 1304, 125 USPQ2d 1282, 1286 (Fed. Cir. 2018);
x. An improved user interface for electronic devices that displays an application summary of unlaunched applications, where the particular data in the summary is selectable by a user to launch the respective application. Core Wireless Licensing S.A.R.L., v. LG Electronics, Inc., 880 F.3d 1356, 1362-63, 125 USPQ2d 1436, 1440-41 (Fed. Cir. 2018);
xi. Specific interface and implementation for navigating complex three-dimensional spreadsheets using techniques unique to computers; Data Engine Techs., LLC v. Google LLC, 906 F.3d 999, 1009, 128 USPQ2d 1381, 1387 (Fed. Cir. 2018); and
xii. A specific method of restricting software operation within a license, Ancora Tech., Inc. v. HTC America, Inc., 908 F.3d 1343, 1345-46, 128 USPQ2d 1565, 1567 (Fed. Cir. 2018).
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It is important to note that in order for a method claim to improve computer functionality, the broadest reasonable interpretation of the claim must be limited to computer implementation. That is, a claim whose entire scope can be performed mentally, cannot be said to improve computer technology. Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 120 USPQ2d 1473 (Fed. Cir. 2016). As currently recited, the pending claims recite inventory control and management, which can be performed mentally, in conjunction with a generic computing device and common computer elements. For at least the reasoning provided above, the rejection of the pending claims under 35 USC 101 is hereby maintained. The examiner suggests amending the claim language to implement the machine learning element into the identification of the change detection. Appropriate correction and/or clarification is required.
103
Applicant’s arguments and amendments have been considered by the examiner and have been addressed in the new grounds of rejection presented below.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “image information input receipt processing unit”, “outer packing change detection processing unit” in claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 therefore, subject to the conditions and requirements of this title.
Claims 1-5 and 8 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards an abstract idea without significantly more or transforms the abstract idea into a practical application.
The independent claim 1 is directed towards a system for detecting changes in item packaging on a retail display shelf, under broadest reasonable interpretation, are directed towards an abstract idea through the system merely gathering data (image data), generally analyzing the data (comparing changes between timestamped images), and determining results based on the analysis (determining whether or not a packaging change has occurred based on said comparison). All the elements are directed towards the abstract idea of “certain method of organizing human activity” where the system is merely determining whether the packaging of an item has been changed based on image comparison. The analysis techniques are merely generic analysis techniques that are applying a general purpose business method to a computer. That general business method being the fundamental economic principle of monitoring theft or tampering within a retail setting. The independent claims are merely providing this business practice using generic analysis techniques (comparing, matching, and other various criterion) that are performed in determining item tampering.
Step 2(a)(II) analysis considers the additional elements of the independent claim as to whether or not they are directed towards a practical application. The additional elements of “image information input receipt processing unit”, and an “outer packing change detection processing unit” are merely applying the abstract idea using the computer/system as a tool for the abstract idea (MPEP 2106.05(f)).
Step 2(b) analysis considers the additional elements in regard to being significantly more than the abstract idea identified. The additional elements of “image information input receipt processing unit”, and an “outer packing change detection processing unit are additional elements that are not significantly more than the abstract idea as they recite general links to a field of use and merely using the additional elements as a tool to implement the abstract idea (MPEP 2106.05(f) and 2106.05(h)).
The examiner submits that the machine learning as recited in the pending claims is a generic recitation of an analysis and determination technique that provides no indication of what specific technique is being used within the system. Machine learning is merely an umbrella term that includes specific and non-specific (generic) techniques, and because broadest reasonable interpretation includes these generic techniques, then the determination using machine learning is a generic analysis technique.
Merely having the determination based on training the model does not preclude the technique to be indicative of applying the additional element in some meaningful way.
Machine learning includes generic and specific techniques, and without further discussion provided in the originally filed specification, then the broadest reasonable interpretation like that of exemplary independent claim 1 is merely generic analysis techniques that are not indicative of a practical application or significantly more than the abstract idea identified.
Similarly recited independent claim 8, as well as dependent claims 2-5 are directed towards an abstract idea without significantly more or transformative into a practical application. Therefore, claims 1-5 and 8 are rejected under 35 U.S.C. 101 for being directed towards non-statutory subject matter.
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.
Claims 1-5 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Trivelpiece et al (US 2019/0080277) in view of Fisher et al (US 2023/0079388).
Regarding claim 1, the prior art discloses an information processing system that detects a change in outer packing of goods displayed on a display shelf, the information processing system comprising:
A processor; and
A memory storing instructions that, when executed by the processor, cause the processor to function as:
an image information input receipt processing unit configured to receive an input of image information obtained by imaging the display shelf (see at least paragraph [0061] to Trivelpiece et al, wherein image capture devices comprise at least one visual camera, at least one 3D camera, and at least one thermal camera); and an outer packing change detection processing unit configured to detect the change in the outer packing of the goods that appears in the image information, wherein the outer packing change detection processing unit determines whether the goods displayed on the display shelf at a current time and the goods displayed on the display shelf before the current time are identical goods (see at least paragraph [0055] to Trivelpiece et al, wherein sing a machine learning algorithm to learn different states and/or conditions of the piece of display equipment and/or inventory based on the tracked changes detected in 624. The machine learning algorithm can include, but is not limited to, a supervised learning algorithm, an unsupervised learning algorithm, and/or a semi-supervised algorithm. Each of the listed machine learning algorithms are well known in the art, and therefore will not be described herein. Any known or to be known machine learning algorithm can be used herein without limitation. The display equipment states and/or conditions include, but are not limited to, fully stocked state/condition, partially stocked state/condition, empty state/condition, restocking condition/state, clean state/condition, dirty/cleanup state/condition, normal state/condition, and/or possible theft condition/state. The inventory states and/or conditions include, but are not limited to, properly located, misplaced, original packaging, new packaging, undamaged and/or damaged), and in a case where the goods displayed are identical goods, determines whether there is the change in the outer packing of the goods that appears in the image information between the current time and before the current time (see at least paragraph [0051] to Trivelpiece et al, further comprising identifying objects represented in the images; determining the colors, shapes, patterns, heat signatures, and/or other characteristics of the identified objects; determining (if possible) an item identification code (e.g., if a barcode is visible); comparing the determined item identification code, colors, shapes, patterns, heat signatures and/or other item characteristics to that contained in the pre-stored item related information; selecting an item type based on results of the comparing; and comparing the selected item type to that specified in the display related information).
Trivelpiece et al does not appear to explicitly disclose wherein the image information is associated with a current time and a time before, and wherein, with regard to image information of the goods in a place area to be processed, inputting image information of the goods in the place area of the current time and image information of the goods in the place area before the current time into a learning model in which a weighting coefficient between neurons of each layer of a neural network including a large number of intermediate layers is optimized, and determining whether there is the change in the outer packing of the goods in the place area based on an output value of the learning model, the learning model giving image information of a plurality of goods and information of a result of whether there is a change in the outer packing of the goods, as correct data, to cause performing of machine learning; and in a case where the change in the outer packing is detected, the outer packing change detection processing unit causes storage of new sample information of the goods or causes relearning of the learning model using the image information including a changed outer packing.
However, Fisher et al discloses systems and methods for trigger based updates to camograms for autonomous checkout in a cashier-less shopping, the image information is associated with a current time and a time before (see at least paragraph [0014] to Fisher et al, wherein the implementing of the trained location detection model includes, inputting, to the trained location detection model, (i) a portion of a first image of the area of real space (a) matching a location of a cell in which the inventory item is located in a map of the area of real space and (b) obtained in a previous time interval and (ii) a portion of a second image of the area of real space (a) matching the location of the cell and (b) obtained in a current time interval), and wherein, with regard to image information of the goods in a place area to be processed, inputting image information of the goods in the place area of the current time and image information of the goods in the place area before the current time into a learning model (see at least paragraph [0129] to Fisher et al) in which a weighting coefficient (see at least paragraph [0095] to Fisher et al) between neurons of each layer of a neural network including a large number of intermediate layers is optimized (see at least paragraph [0286] to Fisher et al) , and determining whether there is the change in the outer packing of the goods in the place area based on an output value of the learning model, the learning model giving image information of a plurality of goods and information of a result of whether there is a change in the outer packing of the goods, as correct data, to cause performing of machine learning (see at least paragraphs [0067] and [0068] to Fisher et al); and in a case where the change in the outer packing is detected, the outer packing change detection processing unit causes storage of new sample information of the goods or causes relearning of the learning model using the image information including a changed outer packing(see at least paragraph [0131] to Fisher et al).
The examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). The examiner submits that the combination of the teaching of the machine learning inventory management system and method, as disclosed by Trivelpiece et al and the systems and methods for trigger based updates to camograms for autonomous checkout in a cashier-less shopping as taught by Fisher et al, in order to accurately monitor the display state of goods displayed on a display rack (see at least paragraph [0004] to Fisher et al) could have been readily and easily implemented, with a reasonable expectation of success. As such, the aforementioned combination is found to be obvious to try, given the state of the art at the time of filing.
Regarding claim 2, the prior art discloses the information processing system according to claim 1, wherein the outer packing change detection processing unit compares the current time with before the current time with regard to goods identification information of the goods identified from the image information, or the goods displayed on the display shelf or an IC tag attached to a product tag, and determines whether the goods displayed on the display shelf at the current time and the goods displayed on the display shelf before the current time are identical goods (see at least paragraphs [0051] and [0053] to Trivelpiece et al, wherein changes in item types can be identified by: identifying objects represented in the images; determining the colors, shapes, patterns, heat signatures, and/or other characteristics of the identified objects; determining (if possible) an item identification code (e.g., if a barcode is visible); comparing the determined item identification code, colors, shapes, patterns, heat signatures and/or other item characteristics to that contained in the pre-stored item related information; selecting an item type based on results of the comparing; and comparing the selected item type to that specified in the display related information).
Regarding claim 3, the prior art discloses the information processing system according to claim 2, wherein the outer packing change detection processing unit compares goods identification information recognized from a place or a face of the image information, or an area of a product tag in current processing with goods identification information recognized from a place or a face of the image information, or an area of a product tag in processing before the current processing, and determines whether the goods displayed on the display shelf at the current time and the goods displayed on the display shelf before the current time are identical goods (see at least paragraphs [0051] and [0053] to Trivelpiece et al, wherein changes in item types can be identified by: identifying objects represented in the images; determining the colors, shapes, patterns, heat signatures, and/or other characteristics of the identified objects; determining (if possible) an item identification code (e.g., if a barcode is visible); comparing the determined item identification code, colors, shapes, patterns, heat signatures and/or other item characteristics to that contained in the pre-stored item related information; selecting an item type based on results of the comparing; and comparing the selected item type to that specified in the display related information).
Regarding claim 4, the prior art discloses the information processing system according to claim 2, wherein the outer packing change detection processing unit compares image information of the product tag of the image information in current processing with image information of the product tag of image information in processing before the current processing, and determines whether the goods displayed on the display shelf at the current time and the goods displayed on the display shelf before the current time are identical goods (see at least paragraph [0049] to Trivelpiece et al, wherein images captured by the first and/or second image capture devices are analyzed in 618 to learn changes in item packaging. Read item identification codes, POS transaction information, and/or display related information is(are) also used here. If such a change is detected (e.g., in size, color, shape, pattern, etc.), then the corresponding item related information is updated to reflect the detected changes in item packaging, as shown by 620).
Regarding claim 5, the prior art discloses the information processing system according to claim 2, wherein the outer packing change detection processing unit compares association between the goods displayed and the product tag in current processing with association between the goods displayed and the product tag in processing before the current processing, and detects whether the goods displayed on the display shelf at the current time and the goods displayed on the display shelf before the current time are identical goods (see at least paragraph [0023] to Trivelpiece et al, wherein the captured images can be correlated to item identification information obtained from product labels (e.g., barcodes) and/or product markers (e.g., Radio Frequency Identification (“RFID”) tags). This correlation facilitates the machine learning feature of the present solution to know and report product identification codes (e.g., Universal Product Codes (“UPCs”) as well).
Claim 8 contains recitations substantially similar to those addressed above and, therefore, are likewise rejected.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
The examiner has considered all references listed on the Notice of References Cited, PTO-892.
The examiner has considered all references cited on the Information Disclosure Statement submitted by Applicant, PTO-1449.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TALIA F CRAWLEY whose telephone number is (571)270-5397. The examiner can normally be reached on Monday thru Thursday; 8:30 AM-4:30 PM 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 A Obeid 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.
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/TALIA F CRAWLEY/ Primary Examiner, Art Unit 3627
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