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 .
Response to Amendment
The Amendment filed 04/22/2026 in response to the Non-Final Office Action mailed 03/09/2026 has been entered.
Claims 1-3, 5-14 and 16-20 are currently pending in U.S. Patent Application No. 18/641,709 and an Office action on the merits follows.
Response to Claim Objections
In view of the foregoing amendments to e.g. claims 3 and 14 correcting ‘tile’ to read ‘tilt’, those claim objection(s) previously set forth for minor informalities/typographical errors, are withdrawn.
Response to 35 USC § 101 Rejections
Applicant's arguments filed 04/22/2026 and regarding subject matter eligibility analysis have been fully considered but they are not persuasive. Applicant argues firstly, at Prong Two of 2A, that the claim(s) feature some (but none explicitly identified) ‘additional elements’ serving to realize a technical improvement to the functioning of the refrigerator itself (MPEP 2106.05(a)) and/or the “analyzing”, drawn to the exception in the Examiner’s analysis, constitutes a technical improvement. Applicant secondly argues at 2B, that the Examiner erred in allegedly drawing ‘additional elements’ (again none are explicitly identified) to only that which is “well-understood, routine, conventional” (WURC) (MPEP 2106.05(d)).
To Applicant’s second argument, Examiner disputes Applicant’s finding that the Examiner’s analysis relied upon identifying any additional elements as WURC. Instead it is asserted that there are few ‘additional elements’, for consideration at 2B, and those present fail the test at 2B for the same reason(s) that they fail to serve for integration at Prong Two of 2A, notably because they at best generally link to a field of use (MPEP 2106.05(h)) or involve structural elements that do not serve for integration in view of the considerations of 2106.05(f) and/or 2106.05(b). As MPEP § 2106.05(d), the Berkheimer memo from 2018, etc., make clear, any finding with respect to what is WURC would be a factual finding requiring supporting evidence. Given this evidentiary burden, the Examiner’s analysis steers clear of such an assertion/grounds. It may very well be the case that “a cabinet defining a chilled chamber” and “a door being rotatably hinged to the cabinet” are indeed ubiquitous to most if not all refrigerators, but it is not required that they be so, in order for them to fail to serve for integration at Prong Two of 2A, and “significantly more” at 2B, because analysis may alternatively find they at most serve to generally link to a field of use (one involving refrigerators). Such an analysis would be very consistent with jurisprudence from the courts over the course of the last few years, and e.g. Recentive Analytics, Inc., v. Fox Corp., Appeal No. 2023-2437, (Fed. Cir. Apr. 18, 2025) available at https://www.cafc.uscourts.gov/opinions-orders/23-2437.OPINION.4-18-2025_2500790.pdf, with the assertion being that Applicant’s claims seek to monopolize the application of one or more classes of machine learning to a ‘new’ field-of-use. Even if it may be asserted that an implementation of YOLO for object detection routinely/ conventionally considers an aspect ratio related IoU loss, the analysis previously drew the ‘analysis’ at large under the mental processes Abstract Idea grouping, i.e. the exception, and accordingly any object detection, as part thereof and as may still be performed visually/mentally, is not an ‘additional element’ for consideration at 2B. Accordingly, any findings regarding whether machine learning models routinely/conventionally account for aspect ratio in an object detection (which they arguably do), is moot. To any implied assertion that a rejection to the claims under 35 U.S.C § 103, tacitly acknowledges involved elements are not WURC, Examiner respectfully disagrees and notes that even if Section 101 is intended to serve as a threshold inquiry, the MPEP makes clear that questions regarding obviousness and eligibility are distinct/considered “fully apart” (MPEP 2106.05 sub-section I and 2106.05(d)), and the Examiner does not have the luxury of a piecemeal analysis opting for a rejection under 35 USC § 101 in lieu of analysis under 103 (MPEP 707.07(g)). In summary regarding 2B analysis, proper analysis at 2B should not find the claims patent-eligible, because the purported inventive concept is the application of the exception (MPEP 2106.05(f) as distinguished from the requirements of 2106.05(a)), and the provided analysis does not rely on any finding that certain “additional elements” (distinct from those drawn under the exception) are merely WURC.
To Applicant’s first argument regarding Prong Two of 2A, Examiner respectfully disagrees with Applicant’s competing analysis that asserts “analyzing the one or more images… to identify a tilted item”, drawn to the exception, is the limitation by which the ‘practical application’ that is “autonomously detecting and reporting potentially unstable items” is realized, and wherein such a detection automation is a technical improvement to the functioning of the refrigerator appliance itself. Firstly, the analyzing is by definition not an “additional element” because it is drawn to the exception at Prong One, under the argument that such an analysis/identifying, despite the ‘use’ of a machine learning model, may be performed visually/mentally. Identifying a tilted item, on the basis of an object detection, that is further based on a detected aspect ratio, may also/similarly be practically performed visually/mentally. The most recent PEG Examples 47-49 are consistent with such an analysis, and reference can be made to Example 47 claim 2 step(s) (d) and (e), both drawn under the mental processes grouping, and wherein ‘using the trained ANN’ failed to preclude that Prong One finding, and failed to serve for integration (see Prong Two analysis of Ex. 47 claim 2 at page 8 of the linked document - https://www.uspto.gov/sites/default/files/documents/2024-AI-SMEUpdateExamples47-49.pdf ). Because the analyzing is not a so-called ‘additional element’, it is not considered at such when evaluating (MPEP 2106.04(d) II.) “Whether the Additional Elements Integrate the Judicial Exception into a Practical Application”. Secondly, even if the improvement that is a detection automation were realized by limitations precluded from being drawn under the Abstract Ideas grouping and accordingly serving as ‘additional element(s)’, the MPEP identifies examples of limitations that the courts have found suggest an improvement to a technical field in MPEP §§ 2106.04(d)(1) and 2106.05(a), as distinguished from an equivalent to reciting the words “apply it” (or an equivalent) – MPEP § 2106.05(f), and Examiner would assert that an ‘automated detection’ reads more akin to the later. More specifically, because of that second prong/consideration in MPEP 2106.05(f), (2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process, and that rationale presented therein, “Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015)”. Additionally, as identified in the Non-Final Office Action at page 6 and with reference to 2106.05(a) “the judicial exception alone cannot provide the improvement. 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)”. A machine learning model based object detection (implementing e.g. YOLO with an aspect ratio based IOU loss), as may otherwise be performed visually/ mentally and as applied in the context/field-of-use that is e.g. a smart refrigerator, does not constitute an improvement to the functioning of the refrigerator. In other words, the claimed invention does not change or improve operations integral to the way refrigerators operate (as in Enfish), but instead serves as a mere automation of a detection otherwise and traditionally/ historically performed visually/mentally, thereby at most serving in a change to user experience. Recent jurisprudence from the courts appears to further support such a finding, and one example may be e.g. TJTM Technologies, LLC, v. Google LLC., Appeal No. 2025-1218, (Fed. Cir. May 5, 2026) available at https://www.cafc.uscourts.gov/opinions-orders/25-1218.OPINION.5-5-2026_2688177.pdf . Of note is page 5, describing how an improvement to user experience, by means of an automating, does not amount to a technical/ technological improvement under the Federal Circuit’s precedent. USAA’s January 14, 2026 petition for a writ of certiorari, available https://www.supremecourt.gov/DocketPDF/25/25-853/391754/20260114162543209_USAA%20--%20Cert.%20Petition.pdf , at e.g. page 17, invites the Supreme Court to grant review/clarify what precisely constitutes an Abstract Idea, and patent-eligibility as it relates to user experience as compared to computer functionality, however the Supreme Court denied the petition on May 18, 2026 (order at page 7) ( https://www.supremecourt.gov/orders/courtorders/051826zor_h315.pdf ). That same order also denied petition(s) associated with Rideshare Displays v Lyft (order at page 4), see Rideshare Displays, Inc v. Lyft, Inc., No. 23-2033, (Fed. Cir. September 29, 2025). Rideshare is relevant also in view of page 14 of the Federal Circuit’s decision, disagreeing with the Board’s step two conclusion and finding that claims directed to improving a user’s experience may not necessarily fundamentally alter or improve the way the technology itself functions. Corresponding rejections to the claims are maintained and reproduced below.
Response to 35 USC § 103 Rejections
Applicant's arguments filed 04/22/2026, and with respect to Claims 1-3, 5-14 and 16-20 as amended now requiring a tiltable object detection wherein the tiltable object is characterized by an aspect ratio over a specific threshold, have been considered but are moot because the new ground(s) of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Regarding any obvious nature of the limitation in question however, Examiner previously cited (PTO-892 mailed 03/09/2026, page 2, Citation No. V) Khanam et al. “What is YOLOv5: a deep look into the internal features of the popular object detector” (30 July 2024), which describes how YOLOv5 considers an aspect ratio in that IOU loss for object detection – see page 4, Fig. 2, and Loss Calculation section 3.5. Also at page 4, Khanam discloses “The YOLOv3 PyTorch repository introduced a novel approach to anchor box generation, employing K-means clustering and genetic algorithms to derive anchor box dimensions directly from the distribution of bounding boxes within a given dataset. This methodology is particularly critical for custom object detection tasks, as the scale and aspect ratios of objects often diverge significantly from those commonly found in standard datasets like COCO”. While Khanam was published after Applicant’s EFD, the architecture described therein is understood to have been made publicly available in 2020. Support for the limitation in question appears limited to Applicant’s Specification at [0058] of the corresponding pgpub, which discloses [0058] “According to an example embodiment, an object detection model (e.g., such as the YOLO model described above) may be used to identify various objects 302 within fresh food chamber 122 and determine whether the item is a tiltable item (e.g., an item that may topple over if placed at a certain angle). For example, the tiltable items may include bottles or other tall items that have an aspect ratio over a specific threshold.” (emphasis added). It should also be mentioned that this is the only disclosure located by the Examiner that describes any aspect ratio and/or ‘specific threshold’. The aspect ratio as recited however appears to be a characteristic associated with the object class, as part of its detection, and one that would be considered by that IoU loss (and associated ground truth aspect ratio/bounding box) for an implementation involving the YOLO architecture. Stated differently, the recited limitation, if interpreted so as to explicitly require a consideration of aspect ratio as distinguished from a limitation that may be satisfied in a particular context given the nature of the object being detected, at most involves a consideration of aspect ratio that would be met with an application of e.g. the YOLO architecture, and this feature is not e.g. an improvement to any model itself. Newly cited Bochkovskiy et al., “Yolov4: Optimal speed and accuracy of object detection” further evidences the manner in which YOLO architectures, made publicly available at least as of 2020, and/or recognized/known alternatives involving a bounding box regression and IoU loss(es), consider aspect ratios and corresponding ground truth bounding boxes as part of object detection. While Applicant’s remarks at page 9 of 11 assert that Adato et al. (US 2019/0215424 A1) is entirely silent as to any assessment of object aspect ratio, Examiner disagrees because Adato disclosure previously identified at page 10 of the Non-Final Office action discloses bounding box detection, supervised training, and object characteristics such as size and shape, among others. It stands to reason that an aspect ratio is at the minimum suggested in the broader ‘size and shape’ disclosure in Adato, particularly for those embodiments of Adato concerning object orientation, and in view of [0659] in particular which concerns bounding boxes defined relative to minor and major axes of the object, facilitating a determination of object orientation. While the Examiner would assert that Adato at the minimum suggests an assessment of aspect ratio in that bounding box detection disclosed, more explicit aspect ratio and threshold associated with an IoU disclosure of Bochkyoyskiy is relied upon in the proposed combination(s) presented below.
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 1-3, 5-14 and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, in particular an Abstract Idea falling under the (c) mental processes grouping (concepts performable in the human mind including an observation, evaluation, judgement, opinion), not ‘integrated into a practical application’ at Prong Two of Step 2A and without ‘significantly more’ at Step 2B.
Step 1: The claim(s) in question are directed to a computer implemented method for identifying a tilted item from one or more images associated with a chilled chamber/inside of a refrigerator. (Step 1: Yes).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim. Representative claim(s) 1/12 explicitly recite(s) “analyzing the one or more images… to identify a tilted item” falling under the mental processes grouping (concepts performable in the human mind including an observation, evaluation, judgement, opinion). Examiner notes that while the ‘analyzing’ recited is preformed “using one or more machine learning image recognition processes” this use is an additional element that does not preclude drawing the analysis/ analyzing under the mental processes grouping. Reference may be made to the 2024 PEG, Example 47 claim 2, wherein using an ANN did not preclude that anomaly detection and analysis of step(s) (d) and (e) from being drawn under the mental processes grouping at Prong One. See pages 6-7 of:
https://www.uspto.gov/sites/default/files/documents/2024-AI-SMEUpdateExamples47-49.pdf
Dependent claims are similarly analyzed, and measuring/calculating a tilt angle relative to a horizontal line/plane/surface further falls under the mental and/or mathematical operations (MPEP 2106.04(a)(2)(C)) grouping(s). (Step 2A, Prong One: Yes).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by (1) identifying whether there are any ‘additional elements’ recited in the claim beyond the judicial exception, and (2) evaluating those ‘additional elements’ individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). Examiner notes for consideration at Prong Two of 2A that MPEP 2106.05(a), (b), (c), and (e) generally concern elements that may be indicative of integration, whereas 2106.05(f), (g), and (h) generally concern elements that are not likely indicative of integration. As an additional note, ‘additional elements’ are generally limitations excluded from interpretation under the Abstract Idea groupings, and may comprise portions of limitations otherwise identified as falling under those Abstract Idea groupings of the 2019 PEG (e.g. any detection/determination/recognition that may be made mentally accompanied by the use of a neural network and/or generic computer hardware considered under the ‘apply it’ considerations of 2106.05(f)). Any ‘providing’/outputting broadly, and ‘collection’ of data (i.e. image acquisition(s)), be they images for training any learning model and/or data/images visually observable/ evaluated by a user/operator, also fail(s) to integrate at least in view of MPEP 2106.05(g) (extra-solution data gathering/output) and/or 2106.05(h) as ‘generally linking’ the exception to a field of use involving machine learning and/or imagery so acquired. Examiner also notes with respect to 2106.05(a), that ‘functioning of a computer’ (see fact pattern of Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336, 118 USPQ2d 1684, 1689 (Fed. Cir. 2016)) does not constitute operations that a general purpose computer may be programmed/configured to perform, since functioning of a computer instead concerns functions integral to the way computers operate (e.g. memory read-write for Enfish and virus scanning for Finjan). Regarding the claim(s) ‘as a whole’, the requirement for considering the claim as a whole stems from the fact that the judicial exception alone cannot provide the improvement, and any ‘additional elements’ are not evaluated in a vacuum separate from the weight of those directed to the exception. Consideration must be given to the degree/extent to which the apparent/disclosed improvement, as it is realized in recited claim language, is to the exception itself or otherwise distinct from it and captured by those limitations clearly serving as ‘additional elements’ after analysis at Prong One, in addition to how the ‘additional elements’ weigh in comparison to those limitations directed to the exception.
Reference may be made to the 08/04/2025 memo affirming analysis set forth in the 2024 PEG (https://www.uspto.gov/sites/default/files/documents/memo-101-20250804.pdf) and consistent with guidance to date. The most recent SME Memo(s) are available at: https://www.uspto.gov/patents/laws/examination-policy/subject-matter-eligibility and more specifically: https://www.uspto.gov/sites/default/files/documents/memo-desjardins.pdf
For the case of Desjardins, the claim(s) explicitly recited a limitation not drawn under/subsumed by the identified exception at Prong One, and realizing an improvement to the technical field of machine learning (serving for integration accordingly in view of 2106.05(a) – reciting an improvement to the way machine learning models are trained). The instant claims however read much more akin to an instance of ‘applying’ machine learning techniques to perform an identifying that is otherwise/ conventionally/traditionally performed visually/mentally, and generally linked (MPEP 2106.05(h) to a field-of-use wherein the imagery concerns the contents/items within a refrigerator. While it might be argued the claim(s) concern adding some function/feature to the refrigerator, this change is not an optimization/improvement to operations integral to the functioning of the refrigerator itself (much the way adding a function that is, e.g. mitigating settlement risk, to the generic computer recited in Alice, was not an improvement to the computer). See also those remarks/considerations presented in the response to remarks above, and jurisprudence distinguishing improvements involving user experience, from changes to computer/device functionality. Even if identifying a tilted item is in itself useful/ practical – the utility of the exception itself does not serve for integration into a ‘practical application’ (see MPEP 2106.04(d)). Applicant is also encouraged to consider those three prongs of MPEP 2106.05(b) – as they serve to suggest that the process recited is not an improvement to processes integral to the way refrigerators operate (instead the refrigerator’s involvement is field-of-use). Also identified therein is the manner in which a claim that passes the Machine or Transformation test (as a refrigerator per se might) may still fail the Alice-Mayo test and would be ineligible accordingly. The additional element that is providing that final notification, as well as that obtaining/image acquisition, fail to serve for integration in view of MPEP 2106.05(g) and no additional elements outside of those directed to the exception itself, appear to explicitly/ specifically capture/recite any disclosed improvement in refrigerator technology (MPEP 2106.05(a)). With reference to MPEP 2106.05(a):
It is important to note, the judicial exception alone cannot provide the improvement. 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))
Even when viewed in combination, the ‘additional elements’ present do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: No; Revised Step 2A: Yes [Wingdings font/0xE0] Step 2B).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to ‘significantly more’ than the recited exception, i.e., whether any ‘additional element’, or combination of additional elements, adds an inventive concept to the claim. The considerations of Step 2A Prong 2 and Step 2B overlap, but differ in that 2B also requires considering whether the claims feature any “specific limitation(s) other than what is well-understood, routine, conventional activity in the field” (WURC) (MPEP 2106.05(d)). Such an additional element, if specifically recited however, must still be excluded from interpretation under any of the Abstract Idea groupings (otherwise it is not an ‘additional element’, by definition). Step 2B further requires a re-evaluation of any additional elements drawn to extra-solution activity in Step 2A (e.g. gathering video/image(s)) – however no limitations appear directed to any novel collection per se. For at least the case of representative claim 1, both the obtaining and final providing are generically recited, if not WURC. Applicant may consider Longitude Licensing Ltd. v. Google LLC, No. 24-1202, (Fed. Cir. April 30, 2025) (available at https://www.cafc.uscourts.gov/opinions-orders/24-1202.OPINION.4-30-2025_2506816.pdf) (see e.g. pages 7-9). While it is the MPEP that governs Examination and not necessarily case law, this opinion and those referenced therein (e.g. Recentive v Fox – referenced in the response to remarks above) serve to illustrate the manner in which claims that seek to apply broad classes of machine learning to a ‘new’ field of use are not likely to be determined eligible/enforceable. (Step 2B: No).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-3, 5-14 and 16-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Specifically, claim(s) 1/12 as amended now require one or more “machine learning processes specifically trained to distinguish between stable and unstable tilted orientations of items”. While it is conceivable that the limitations in question meet a broader enablement requirement (distinguished from the issue of new matter) since in theory any of two distinguishable classes (e.g. stable tilted vs unstable tilted), may be distinguished for the case of a supervised training wherein positive and/or negative samples of each class are provided and labeled as such, the claims as amended add new matter. See MPEP 608.04(a), and additionally MPEP §§ 2163.06 and 2163.07. The limitations in question are not supported by original disclosure in view of the manner in which there is no disclosure serving to distinguish stable and unstable tilted orientations, what factors are necessarily considered in each instance, and/or how any model may be ‘specifically trained’ so as to distinguish between the two. At best, Applicant’s specification at [0003] appears to draw a tilted state and an unstable state as synonymous, falls entirely silent regarding examples of a stable tilted orientation, and the training in [0056], at best discloses acquiring non-specific training samples from a manufacturer, other remote source, or ‘any other suitable manner’. Dependent claims fail to comply with written description requirements similarly, in view of that new matter identified for the case of claim(s) 1/12 as amended, are rejected accordingly.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-3, 5-14 and 16-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim(s) 1/12 recite(s) the limitation, “the tiltable item having an aspect ratio over a specific threshold”, wherein the term “specific threshold” is a relative term which renders the claim indefinite. The threshold in question is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree (see [0058], no examples of such a threshold are provided/disclosed, instead referencing the relative qualifier ‘tall’), and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention accordingly. Reference may be made to MPEP 2173.05(b), subsection(s) I and II, describing how reference to an object that is variable may render a claim indefinite, and how an identification of a claim term as one of degree may not be sufficient alone to conclude the claim is indefinite, but that the claim may be indefinite if the specification fails to provide clear examples or teachings that can be used to ascertain the measure in question. What aspect ratios and specific threshold(s) characterize an object that is tiltable as distinguished from one that is not? What is considered a tall object in the context of items typically stored in a refrigerator? Applicant’s disclosure fails to provide objective boundaries for the limitation in question, and accordingly the recited language fails to serve the notice function required by 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph, in providing clear warning to others as to what constitutes infringement. While the language in question may be amendable to construction, e.g. interpretation such that considering an aspect ratio threshold, as part of e.g. a loss term and associated ground truth object bounding box (‘specific’ in the sense that it is specified/predefined/known for the purposes of training the model) for an implementation of YOLO architecture, the Office does not interpret claims in the same manner as the courts, and interpretation during prosecution may effectively result in a lower threshold for ambiguity (beneficial for those reasons disclosed in MPEP 2173.02 subsection I).
Dependent claim(s) 2-3 5-11, 13-14 and 16-20 inherit and fail to cure that/those deficiencies identified above for the case of independent claim(s) 1/12, and are rejected accordingly.
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 of this title, 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.
1. Claims 1, 5, 9-12, 16 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wetzl et al. (US 2019/0390898 A1) in view of Adato et al. (US 2019/0215424 A1) and Bochkovskiy et al., “Yolov4: Optimal speed and accuracy of object detection”.
As to claim 1, Wetzl discloses a method of operating a refrigerator appliance (refrigerator appliance 12, Abs “in particular a domestic refrigerator, having an improved determining routine”), the refrigerator appliance comprising a chilled chamber (Fig. 3, interior 28, [0005], etc.,), a door to provide selective access to the chilled chamber (Fig. 3), and a camera assembly for monitoring the chilled chamber (detector(s) 30/32, [0005] “Preferably, the detection element is configured as a laser scanner, as a CCD sensor and/or advantageously as a camera”, [0012], [0013] “Preferably, the detection elements in each case are configured as optical detection elements and particularly preferably in each case as a camera”, [0035] “The detection unit 14 is configured in the present case as an image detection unit. The detection unit 14 is intended to detect an interior 28, in particular a useable space, of the household appliance 12”, [0037-0039], etc.,), the method comprising:
obtaining one or more images using the camera assembly ([0035] the characteristic variable is image derived, 30 and 32 are cameras to [0039] “detect a geometry of at least one of the consumer products… detect a position and/or location of at least one of the consumer products”, etc.,);
analyzing the one or more images using one or more machine learning image recognition ([0006] “In this embodiment, advantageously the computer unit and at least parts of the database are implemented by a neural network”) processes specifically trained to distinguish between desired vs non-desired orientations of items to identify a tilted item (‘tilted’ is ‘status information’ (more specifically a ‘storage orientation’) that is a detected/determined ‘storage characteristic’ that deviates (see comparing [0018], [0047]) from a preferred/predefined/setpoint ‘storage characteristic variable’, [0008] “Moreover, "storage characteristic variable" is to be understood, in particular, as a preferably predefined characteristic variable which is correlated, in particular, to a storage, in particular a storage condition, a shelf life, a storage orientation, for example upright and/or horizontal, and/or advantageously a storage location of the consumer product which is, in particular, arranged and/or can be arranged within the item of household furniture and/or household appliance”, [0018] “A "setpoint storage characteristic variable" is to be understood, in particular, as a characteristic variable which is advantageously predefined and particularly preferably stored in the, in particular, predefined database and which is correlated, in particular, to a setpoint storage, in particular a setpoint storage condition, a setpoint shelf life, a setpoint storage orientation, for example upright and/or horizontal, and/or advantageously a setpoint storage location of the consumer product which is arranged and/or can be arranged”, [0047] “the computer unit 26 is intended to compare the storage characteristic variable with a setpoint storage characteristic variable 36”, etc.,); and
providing a user notification in response to identifying the tilted item (output via output unit 44, and/or transmitted via 50 to e.g. electronic device/smartphone 52 in response to the detected storage orientation deviating from that preferred/desired [0018], [0008] “Advantageously, the computer unit may provide information about a current storage condition, a current shelf life, a current storage orientation and/or a current storage location of the consumer product and/or determine the current storage condition, the current shelf life, the current storage orientation and/or the current storage location of the consumer product”, [0019], [0022] “to output the instruction message… As a result, in particular, an advantageously intuitive instruction message may be generated”, [0050] “Alternatively, it is conceivable to generate just one instruction message and/or a plurality of different instruction messages. Moreover, an instruction message could also be configured alternatively or additionally as a text message”).
While Wetzl discloses that the computer unit and/or one or more sub-components therein may be implemented by means of a neural network, Wetzl is silent regarding implementing at least one of an object detection model or an image segmentation model to determine the candidate/detected storage orientation/tilt.
Adato however evidences the obvious nature of employing machine learning comprising implementing at least one of an object detection model or an image segmentation model to perform various object determinations to include object orientation relative to a horizontal surface/shelving and preferred/setpoint orientations ([0659] “Bounding boxes may be defined relative to areas of a captured image determined to represent each canned good product. Minor and major axes may be assigned, and an orientation of the canned good item (e.g., whether the item is standing upright or resting on its side) may be determined”, [0521] “In some embodiments, image processing unit 130 may include a machine learning module that may be trained using supervised models. Supervised models are a type of machine learning that provides a machine learning module with training data, which pairs input data with desired output data. The training data may provide a knowledge basis for future judgment”, [0578] “In accordance with this disclosure, the at least one processor may be configured to analyze the at least one image to detect the plurality of products. The plurality of products may be detected according to any means consistent with this disclosure. For example, a product may be detected by recognition in the image data of a distinctive characteristic of the product (e.g., its size, shape, color, brand, etc.) or by contextual information relating to a product (e.g., its location on a shelf, etc.). A product may be detected using object detection algorithms, using machine learning algorithms trained to detect products using training examples, using artificial neural networks configured to detect products, and so forth. Detecting products may include first detecting a background of an image and then detecting items distinct from the background of the image. By way of example, system 100 may detect products as discussed relative to step 2112 of FIG. 21”, [0521-0522], [0517] “Image processing unit 130 may use any suitable image analysis technique including, for example, object recognition, image segmentation … image processing unit 130 may utilize machine learning algorithms and models trained using training examples to detect occlusion events in images and/or to identify occluding objects from images” in further view of the manner in which capturing devices 125, comprising image sensors 310, etc., capture images of items on shelving to include that within refrigerators, [0295], [0393] “For example, system 100 may receive an image of a refrigerated shelf containing orange juice cartons and milk cartons. The size and shape of the cartons may be used to differentiate between the orange juice and the milk”, etc.,) in addition to prompt/notify for a rearrangement/re-orienting the non-preferred orientation ([0567] “Consistent with the present disclosure, a rearrangement event may be determined. A rearrangement event may exist when one or more product arrangement conditions on at least one shelf are determined to be present, where altering at least one of the one or more of the product arrangement conditions may improve service (e.g., by product rearrangement). Consistent with the present disclosure, a product-related task may be generated. The product-related task may include directions for a human or machine to rearrange, re-orient, or otherwise manipulate a product”).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date, to modify the system and method of Wetzl such that computing unit 26 for performing that disclosed comparison, further comprises implementing at least one of an object detection model or an image segmentation model to determine a storage/product orientation as taught/suggested by Adato, the motivation being as similarly taught/suggested therein that implementing such models may facilitate a more accurate recognition (by means of e.g. supervised training samples, suited for instances of high-occlusion, etc.,) for those products/containers expected and/or otherwise characterized by a high degree of prevalence/relevance.
Wetzl in view of Adato further teaches/suggests the method wherein analyzing the one or more images using the one or more machine learning image recognition processes comprises implementing an object detection model to identify a presence of a tiltable item (see Adato as identified above, also [0398] “system 110 may detect a product using one or more algorithms similar to those discussed in connection with image processing unit 130. For example, the product may be detected using object detection algorithms, using machine learning algorithms trained to detect products using training examples, using artificial neural networks configured to detect products, and so forth”, [0578] “For example, a product may be detected by recognition in the image data of a distinctive characteristic of the product (e.g., its size, shape, color, brand, etc.) or by contextual information relating to a product (e.g., its location on a shelf, etc.). A product may be detected using object detection algorithms, using machine learning algorithms trained to detect products using training examples, using artificial neural networks configured to detect products, and so forth. Detecting products may include first detecting a background of an image and then detecting items distinct from the background of the image”; Wetzl further discloses a neural network and POSITA would recognize such networks as generally used for object detection broadly, e.g. YOLO (Redmon et al. 2015) which uses a convolutional neural network to predict object bounding boxes and class probabilities for an input image).
Wetzl in view of Adato fails to explicitly disclose any detected object named as ‘tiltable’ based on the item having an aspect ratio over a specific threshold. See Examiner’s response to remarks above, regarding whether interpretation for the limitation in question may be met by consequence of the item detected (the claim recites that the item detected has/possess any of aspect ratios satisfying a threshold), or if the language explicitly requires an assessment of aspect ratio.
Assuming the later, Bochkovskiy evidences the obvious nature of assessing an object aspect ratio as compared to a specified (ground truth based) threshold/IoU, for the purposes of object detection (page 3, section 2.2 right column final paragraph “In order to make this issue processed better, some researchers recently proposed IoU loss [90], which puts the coverage of predicted BBox area and ground truth BBox area into consideration. The IoU loss computing process will trigger the calculation of the four coordinate points of the BBox by executing IoU with the ground truth, and then connecting the generated results into a whole code. Because IoU is a scale invariant representation, it can solve the problem that when traditional methods calculate the l1 or l2 loss of {x, y, w, h}, the loss will increase with the scale. Recently, some researchers have continued to improve IoU loss. For example, GIoU loss [65] is to include the shape and orientation of object in addition to the coverage area. They proposed to find the smallest area BBox that can simultaneously cover the predicted BBox and ground truth BBox, and use this BBox as the denominator to replace the denominator originally used in IoU loss. As for DIoU loss [99], it additionally considers the distance of the center of an object, and CIoU loss [99], on the other hand simultaneously considers the overlapping area, the distance between center points, and the aspect ratio. CIoU can achieve better convergence speed and accuracy on the BBox regression problem”, page 6 section 3.2 For improving the object detection training, a CNN usually uses the following: … Bounding box regression loss: MSE, IoU, GIoU, CIoU, DIoU).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date, to further modify the system and method of Wetzl in view of Adato such that the machine learning model based object detection as performed therein further comprises as assessment of object aspect ratio in determining/detecting an associated object/class (e.g. a ‘tiltable’ object of various embodiments) as taught/suggested by Bochkovskiy, the motivation as similarly taught/suggested therein that such an aspect ratio related IoU loss may serve to achieve better convergence speed and accuracy on the BBox regression problem.
As to claim 5, Wetzl in view of Adato and Bochkovskiy teaches/suggests the method of claim 1.
Wetzl in view of Adato and Bochkovskiy further teaches/suggests the method wherein analyzing the one or more images using the one or more machine learning image recognition processes comprises implementing an image segmentation model to identify an area of the tiltable item and determine an angle of tilt of the tiltable item (Adato [0578] segmentation so as to distinguish object from background and/or neighboring/potentially occluding objects, in further view of that modification to Adato as presented above for the case of claim 1).
As to claim 9, Wetzl in view of Adato and Bochkovskiy teaches/suggests the method of claim 1.
Wetzl in view of Adato and Bochkovskiy further teaches/suggests the method wherein the refrigerator appliance further comprises a user interface panel, and wherein the user notification is provided through the user interface panel (Wetzl display/interface 48 of refrigerator/appliance 12, [0022] “In a further embodiment of the invention, it is proposed that the household system has an output unit, in particular the aforementioned output unit, which is intended to output the instruction message”, [0033] “The output unit 44 comprises at least one output element 48, 50. In the present case, the output unit 44 by way of example comprises two output elements 48, 50. A first output element 48 of the output elements 48, 50 is configured as an optical output element, in the present case in particular as a display. The first output element 48 is arranged on a front face of the household appliance 12. A second output element 50 of the output elements 48, 50 is configured as a communication element. The second output element 48 is connected and/or is able to be connected so as to communicate with an electronic appliance 52, in the present case in particular by way of example a smartphone”).
As to claim 10, Wetzl in view of Adato and Bochkovskiy teaches/suggests the method of claim 1.
Wetzl in view of Adato and Bochkovskiy further teaches/suggests the method wherein the user notification is provided through a remote device through an external network (instruction messages as identified above for the case of claim 1, as provided to remote device/smartphone 52, via output element 50 and network unit 54, e.g. [0050] “an instruction message could also be configured alternatively or additionally as a text message”).
As to claim 11, Wetzl in view of Adato and Bochkovskiy teaches/suggests the method of claim 1.
Wetzl further teaches/suggests the method wherein the user notification comprises at least one image of the one or more images for display to a user Wetzl output comprising that image as disclosed in [0022] “wherein the instruction message is an image of an object which is correlated to the consumer product, advantageously in an illustration of an interior of the item of household furniture and/or household appliance and/or in front of an exemplary background. As a result, in particular, an advantageously intuitive instruction message may be generated”).
Wetzl fails to disclose any superimposed boundary or marker.
Adato however discloses an exemplary user interface where product/object images are displayed with superimposed boundaries and/or markers (Fig. 11D, see markers for correct placement, misplaced, empty, etc.,; see also Fig. 15 bounding box 1501 to notify the user of an object of concern/soliciting user input guiding future detections/ classification output). Examiner notes that Adato is not the exclusive source of a teaching of such a superimposed boundary, and output bounding boxes are common among much of the object detection literature made of record.
It would have been obvious to a person of ordinary skill in the art, before the effective filing date, to modify the system and method of Wetzl such that the displayed instruction message comprising an image of the product/object being stored at a non-preferred storage orientation further comprises a superimposed boundary and/or marker as taught/suggested by Adato (and common in the art in view of attached NPL in the event that the superimposed boundary is simply an object bounding box), the motivation as similarly taught/suggested by Adato and readily recognized by POSITA that such a superimposed boundary and/or marker may provide the user with additional context/ information regarding which specific object(s) is/are being referenced in the instruction message.
As to claim 12, this claim is the system claim corresponding to the method of claim 1 and is rejected accordingly. Wetzl discloses that same/equivalent refrigerator 12 structure to include cabinet/housing defining chilled chamber 28, cameras 30 and 32 disposed therein, hinged door of Fig. 3, and controller/computer unit 26.
As to claim 16, this claim is the system claim corresponding to the method of claim 5 and is rejected accordingly.
As to claim 19, this claim is the system claim corresponding to the method of claim 10 and is rejected accordingly.
As to claim 20, this claim is the system claim corresponding to the method of claim 11 and is rejected accordingly.
2. Claims 2 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Wetzl et al. (US 2019/0390898 A1) in view of Adato et al. (US 2019/0215424 A1), Bochkovskiy et al., “Yolov4: Optimal speed and accuracy of object detection”, and SEO et al. (US 2014/0320647 A1).
As to claim 2, Wetzl in view of Adato and Bochkovskiy teaches/suggests the method of claim 1.
While Wetzl discloses that detection elements/cameras 30 and 32 image the interior 28, door shelf ([0030]), etc., Wetzl fails to explicitly disclose appliance/refrigerator 12 as comprising a door sensor, and obtaining the one or more images after the door sensor indicates that the door has been closed. Adato explicitly discloses capturing of images within refrigerated spaces (e.g. [0297]), but not explicitly in response to any door being closed.
Seo evidences the obvious nature of a refrigerator comprising a door sensor (Fig. 6, door switch 31), and obtaining one or more images after the door sensor indicates that the door has been closed (Fig. 7, capture step S150 after door closed decision S140, [0125] “When the user closes the open door and the door switch 31 senses closing of the door (S140), the controller 200 initiates capture of an image by the camera 120”, etc.,).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date, to modify the system and method of Wetzl to further comprise a door sensor and image acquisition post door closure as taught/suggested by Seo, the motivation as similarly taught/suggested therein and readily recognized by POSITA that such an acquisition ensures the system may account for any shifting/changes in the product/container position/orientation resultant from door closure.
As to claim 13, this claim is the system claim corresponding to the method of claim 2 and is rejected accordingly.
3. Claims 3, 6-8, 14 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Wetzl et al. (US 2019/0390898 A1) in view of Adato et al. (US 2019/0215424 A1), Bochkovskiy et al., “Yolov4: Optimal speed and accuracy of object detection”, and Zhong (US 2025/0078450 A1).
As to claim 3, Wetzl in view of Adato and Bochkovskiy teaches/suggests the method of claim 1.
Wetzl in view of Adato and Bochkovskiy further teaches/suggests the method wherein analyzing the one or more images using the one or more machine learning image recognition processes comprises:
implementing at least one of an object detection model or an image segmentation model to determine an angle of tilt of the tilted item (see Adato as applied above in the rejection of claim 1), the angle of tilt being measured between a center line of the tilted item and a horizontal line (Adato object/product’s major axis oriented parallel to both bounding box sides, relative to a horizontal line that is e.g. the shelf, such that upright is 90 degrees and a product laying/resting on its side would be characterized by a 0 degree angle between the major/central/longitudinal axis of the object/product and a horizontal line associated with the shelf/surface, [0659] “Bounding boxes may be defined relative to areas of a captured image determined to represent each canned good product. Minor and major axes may be assigned, and an orientation of the canned good item (e.g., whether the item is standing upright or resting on its side) may be determined”; Adato further suggests that determining product/item coordinates relative to a shelf/horizontal surface (and further as they relate to ‘undesired orientation’ [0194]) are within the level of ordinary skill in the art).
Under any assertion that the horizontal line equivalent of Adato is at best suggested, Zhong more explicitly evidences the obvious nature of determining a product orientation, from an image, as a measure between a center line of the tilted item and a horizontal line (Fig. 2A, horizontal line HA, wherein the center line is “substantially the same orientation” as those side bounding box lines illustrated, OR is the center line and α1 is the angle of tilt, [0004], [0042-0044], [0043] “For example, a water bottle typically has a bottom side and a top side, the orientation of the water bottle is from the bottom side of the water bottle where it typically sits on a surface to a top side of the water bottle where a cap is situated. In an application scenario of retail product, the orientation OR of the object to be detected is typically the same as an orientation of texts on the retail product. For example, the orientation OR of the water bottle in FIG. 2A and FIG. 2B is perpendicular to that of the text "water" on the water bottle. The first bounding box BBi and the object to be detected have a substantially the same orientation, e.g., the orientation OR”, etc.,).
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It would have been obvious to a person of ordinary skill in the art, before the effective filing date, to further modify the system and method of Wetzl in view of Adato and Bochkovskiy such that the tilt angle is calculated on the basis of such a major/longitudinal/center object axis relative to a horizontal line as taught/suggested by Zhong, the motivation as similarly taught/suggested therein and readily recognized by POSITA that such a center line may efficiently be calculated from bounding box sides and such a horizontal line as a reference serves as an “Obvious to Try” reference considering known/fixed features of the image/chamber (i.e. shelving) and/or known coordinates associated with a calibrated camera disposed therein, enabling an object/product orientation quantification characterized by a reasonable expectation of success.
As to claim 6, Wetzl in view of Adato and Bochkovskiy teaches/suggests the method of claim 5.
Wetzl in view of Adato and Bochkovskiy further teaches/suggests the method wherein identifying the tilted item comprises determining that the angle of tilt of the tiltable item Wetzl in view of Adato as applied, [0659], wherein an object “resting on its side” would be characterized by a center/longitudinal object axis/line that is substantially parallel to a horizontal line, shelving, etc,; additionally Wetzl inherently/necessarily discloses a threshold degree of similarity/non-similarity when comparing the detected storage orientation to the setpoint/reference orientation, even if not an angle threshold per se).
Wetzl in view of Adato and Bochkovskiy fails to explicitly disclose any predetermined threshold angle.
Zhong however suggests such a tilt detection characterized by any of various ranges/threshold values (see Zhong as applied above for the case of claim 3, Fig. 2A, in view of [0044] “α1 is an angle that is not zero or 90 degrees. For example, α1 is in a range of 5 degrees to 85 degrees, e.g., 5 degrees to 10 degrees, 10 degrees to 15 degrees, 15 degrees to 20 degrees, 20 degrees to 25 degrees, 25 degrees to 30 degrees, 30 degrees to 35 degrees, 35 degrees to 40 degrees, 40 degrees to 45 degrees, 45 degrees to 50 degrees, 50 degrees to 55 degrees, 55 degrees to 60 degrees, 60 degrees to 65 degrees, 65 degrees to 70 degrees, 70 degrees to 75 degrees, 75 degrees to 80 degrees, or 80 degrees to 85 degrees”). POSITA would further recognize that different threshold values may be appropriate for different objects/containers – particularly in view of the manner in which each may be characterized by differing dimensions and/or fluid levels/volumes. A bottle/container characterized by a long neck relative to body dimensions, or similarly one that is nearly empty and characterized by a neck of a small diameter relative to large shoulder/body dimensions (e.g. a plastic gallon of milk), might permit a lower tilt angle before warranting alert/notification.
It would have been obvious to a person of ordinary skill in the art, before the effective filing date, to further modify the system and method of Wetzl in view of Adato and Bochkovskiy such that the inherent threshold in the comparison of detected and setpoint orientations of Wetzl, and Wetzl as modified in view of Adato’s object major axis consideration relative to a shelf, further comprises a predetermined threshold angle as taught/ suggested by Zhong and as an “Obvious to Try” (MPEP § 2143 Rationale E) threshold basis (considering a finite/six Degrees-of-Freedom characterizing object orientation) in the aforementioned orientation comparison, the motivation being as recognized by POSITA that such a threshold may be object specific and thereby potentially reduce the issuance of any alerts/instructions that do not actually pose any risk of spills and/or further changes in product/item/object orientation/stability.
As to claim(s) 7-8, Wetzl in view of Adato, Bochkovskiy and Zhong teaches/suggests the method of claim 6.
Wetzl in view of Adato, Bochkovskiy and Zhong further teaches/suggests the method wherein the predetermined threshold angle is 75 degrees (claim 7) or alternatively 60 degrees (claim 8, properly made separate in view of MPEP 2173.05(c)) (see rejection of claim 6 above and that modification and corresponding motivation supplied – Examiner notes that Applicant’s Specification at [0060] (of the PGPUB) suggests optional threshold embodiments are design choice constraints not critical to and/or specifically themselves realizing any improvement).
As to claim 14, this claim is the system claim corresponding to the method of claim 3 and is rejected accordingly.
As to claim 17, this claim is the system claim corresponding to the method of claim 6 and is rejected accordingly.
As to claim 18, this claim is the system claim corresponding to the method of claim 7 and is rejected accordingly.
Additional References
Prior art made of record and not relied upon that is considered pertinent to applicant's disclosure:
Additionally cited references (see attached PTO-892) otherwise not relied upon above have been made of record in view of the manner in which they evidence the general state of the art. Winkle et al. (US 2018/0211208 A1) discloses detecting at [0047] “As another example, regarding orientation, the third array of sensors may be configured to capture images that show if the item is front facing (as may be desirable), offset with respect to front facing, or may be knocked over and lying on its side”. Ryu et al. (US 2022/0397338 A1) is applicable under 102(a)(1) and not subject to any 102(b)(2)(C) exception accordingly, and discloses optical sensing device 210 that [0038] “In general , noncontact scanning device 210 may include any suitable number, type, position, and configuration of sensors or devices that are used to identify a position or orientation of an object” in addition to ML as applied to in-refrigerator object detection/recognition. How such an orientation is determined does not appear disclosed explicitly in Ryu, suggesting that such an object orientation determination would be within the level of ordinary skill in the art if Ryu satisfies analysis under 35 U.S.C. 112(a). SANO et al. (US 2024/0351216 A1) discloses Fig. 11 steps S18 and S19, [0123] “Next, in step S18, it is determined whether or not the product 700 has fallen over (step 18 is an optional step)”, [0124] “The product transfer apparatus 1 may detect only the falling of the product 700, or the product transfer apparatus 1 may detect abnormality by determining whether or not the state of the product 700 is different from a predetermined normal state”, [0129] “If it is determined that the condition is abnormal (determination result in step S18 is No), in step S19, an alert is issued to the store employee or the operator, etc.”.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Inquiry
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/IAN L LEMIEUX/Primary Examiner, Art Unit 2669
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