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 .
Status of the Claims
This Office Action is in response to Applicants reply dated 6/15/2026, claims 1, 4-6, 9, 11, 16, and 19-20 have been amended, claims 1-20 are currently pending and being examined in this reply.
Response to Arguments
Regarding the 102/103 arguments:
Applicant’s arguments regarding the 102 rejection have been considered and are found persuasive in part, as such the previous 102 rejection has been withdrawn but is moot in view of new grounds of rejection found below.
Applicant has argued that Chaubard does not disclose “identifying a location of an item….inputting the location of the item into the…network”, the Examiner disagrees, as below in the 103 rejection, Chaubard discloses using the location information of the item from sensors on the shopping cart and using the information in combination with image data to train the model. See below citations.
Regarding the 101 arguments:
Applicant has argued that the claims recite patent-eligible subject matter by providing an improvement to machine learning. The Examiner disagrees, and asserts that the focus of the claims are certain methods of organizing human activity (fundamental economic practices and managing personal behavior or relationships or interactions between people) performed by generic computer components. That is, other than reciting “sensors, shopping cart, camera, and IRNN”, nothing in the claim element precludes the step from practically being a method of organized human activity.
Therefore the previous 101 rejection is maintained and is final.
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 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without “significantly more.” Claims 1-20 are directed to certain methods of organizing human activity which is considered an abstract idea. Further, the claim(s) as a whole, when examined on a limitation-by-limitation basis and in ordered combination do not include an inventive concept.
Step 1 – Statutory Categories
In regard to claims 1-20 as indicated in the preamble of the claims, the examiner finds the claims are directed to a process, machine, or article of manufacture.
Step 2A – Prong One - Abstract Idea Analysis
Representative claim 1 recites the following abstract concepts, in italics below, which are found to include an “abstract idea”:
A method comprising: receiving a set of data from a plurality of sensors coupled to a shopping cart, wherein the set of data describe position information of an item within a storage area of the shopping cart; identifying a location of the item within the storage area based on the set of data; obtaining information related to the item by capturing one or more images of the item through a camera coupled to the shopping cart; identifying the item by applying an image recognition neural network to the one or more captured images of the item and the location of the item, wherein applying the image recognition neural network to the one or more captured images and the location of the item comprises inputting the one or more captured images and the location of the item into the image recognition neural network, and wherein the image recognition neural network is trained by a process comprising: accessing a plurality of training examples, wherein each training example comprises input data and a label, wherein the input data comprises features for one or more training images depicting an example item and features for a training location of the example item, and wherein the label is an item identifier for the item; inputting the input data for each of the plurality of training examples to the image recognition neural network to generate a plurality of outputs; computing a loss score for each of the plurality of training examples by comparing the plurality of outputs to corresponding labels of the plurality of training examples using a loss function; and updating parameters of the image recognition neural network based on the computed loss scores; and processing payment information of the item based on the identifying of the item.
The claim features in italics above as drafted, under its broadest reasonable interpretation are certain methods of organizing human activity (fundamental economic practices and managing personal behavior or relationships or interactions between people) performed by generic computer components. That is, other than reciting “sensors, shopping cart, camera, and IRNN”, nothing in the claim element precludes the step from practically being a method of organized human activity. For example, but for the “sensors, shopping cart, camera, and IRNN”, the above italicized limitations in the context of this claim encompasses certain methods of organizing human activity. If the claim limitations, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people 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. Accordingly, the claim recites an abstract idea.
Step 2A – Prong Two - Abstract Idea Analysis
This judicial exception is not integrated into a practical application. In particular, the claim only recites 4 additional elements – “sensors, shopping cart, camera, and IRNN”. They are recited at a high-level of generality (i.e., as a generic processor performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component (MPEP 2106.05(f)), data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)), and linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B - Significantly More Analysis
The claims 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 “sensors, shopping cart, camera, and IRNN” amounts to no more than mere instructions to apply the exception using a generic computer component, insignificant extra-solution activity, and linking the use of the judicial exception to a particular technological environment or field of use. Mere instructions to apply the exception using a generic computer component, insignificant extra-solution activity, and linking the use of the judicial exception to a particular technological environment or field of use, cannot provide an inventive concept. Further, the background and specification does not provide any indication that the “sensors, shopping cart, camera, and IRNN” is anything other than a generic, off-the-shelf computer components. For these reasons, there is no inventive concept.
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.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
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 of carrying out his invention.
Claims 1-20 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention. The independent claims recite “computing a loss score….using a loss function”, and “updating parameters….based on the computed loss scores”. There is no mention of loss functions, computing scores of any kind the Examiner is able to find in the specification. The Examiner is unclear what in this context the loss score or a loss function is or how it is calculated, nor how it would be used to update a parameter.
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 1-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential elements, such omission amounting to a gap between the elements. See MPEP § 2172.01. The omitted elements are: The independent claims recite “computing a loss score….using a loss function”, and “updating parameters….based on the computed loss scores”. There is no mention of loss functions, computing scores of any kind the Examiner is able to find in the specification. The Examiner is unclear what in this context the loss score or a loss function is or how it is calculated, nor how it would be used to update a parameter.
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 1-3, 11-13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Application Publication No. 2018/0330196 A1 to Chaubard (“Chaubard”), in view of WO 2015/011688 A2 to Lillicrap et al. (“Lillicrap”).
In regards to claims 1, 11, and 20, Chaubard discloses the following limitations:
A method comprising: receiving a set of data from a plurality of sensors coupled to a shopping cart, wherein the set of data describe position information of an item within a storage area of the shopping cart; (see at least Chaubard Abstract “portable checkout unit automatically generates training data for an automatic checkout system as a customer collects items in a store. A customer uses an item scanner of portable checkout unit to generate a virtual shopping list of items collected in the shopping cart. When the customer adds a new item to the shopping cart or on some regular interval, the portable checkout unit captures images of the items contained by the shopping cart” ¶ 0020 “For example, the image labeling module 180 may use pressure sensor data, motion sensor data, depth sensor data, or RFID sensor data to determine where in the shopping cart the item was placed”)
identifying a location of the item within the storage area based on the set of data; obtaining information related to the item by capturing one or more images of the item through a camera coupled to the shopping cart; (see at least Chuaubard ¶¶ 0006 “The portable checkout may detect an item that is added to the shopping cart using a camera or one or more sensors that detect that a change in inventory has occurred inside the shopping cart”; 0018 “Each camera attached to the shopping cart or hand-held basket may capture a single image or a series of images. In some embodiments, the image labeling module 180 instructs the one or more cameras to capture images of the contents of the shopping cart or hand-held basket when sensors on the shopping cart or hand-held basket detect that the contents of the shopping cart have changed. For example, the image labeling module 180 may instruct the one or more cameras to capture images when some combination of pressure, motion, depth, or RFID sensors on the shopping cart detect a change in the contents of the shopping cart”)
identifying the item by applying an image recognition neural network to the one or more captured images of the item and the location of the item; and (see at least Figure 3 and ¶¶ 0021 “In some embodiments, the image labeling module 180 uses the previous positions of bounding boxes to generate new bounding boxes. For example, the image labeling module 180 may determine that an item in the shopping cart did not move when the new item was added to the cart, and thus may determine that the bounding box for the item should be near the location of a previously-determined bounding box for the item. In some embodiments, the image labeling module 180 generates bounding boxes that identify the positions of items within the images captured by the cameras attached to the shopping cart without identifying the item contained by each bounding box. The image labeling module 180--may then pair the generated bounding boxes with previously-generated bounding boxes to identify the items contained by newly-generated bounding boxes. The item identifier for the item contained by the previously-generated bounding boxes may be associated with the newly-generated bounding box that is paired with the previously-generated bounding box. In some embodiments, the bounding boxes are paired using a pairing algorithm such as the Intersection over Union algorithm or the Mutual Information algorithm; 0018 “Each camera attached to the shopping cart or hand-held basket may capture a single image or a series of images… capture images when some combination of pressure, motion, depth, or RFID sensors on the shopping cart detect a change in the contents”)
identifying the item by applying an image recognition neural network to the one or more captured images of the item and the location of the item, wherein applying the image recognition neural network to the one or more captured images and the location of the item comprises inputting the one or more captured images and the location of the item into the image recognition neural network; (see at least Chaubard Figure 3 and ¶¶ 0021 “the image labeling module 180 generates bounding boxes that identify the positions of items within the images captured by the cameras… paired using a pairing algorithm such as the Intersection over Union algorithm or the Mutual Information algorithm”; 0025 “An automated checkout system trained using the training data… can identify items placed in the shopping cart in real time using the cameras attached to the shopping cart”)
and wherein the image recognition neural network is trained by a process comprising: accessing a plurality of training examples, wherein each training example comprises input data and a label, wherein the input data comprises features for one or more training images depicting an example item and features for a training location of the example item, and wherein the label is an item identifier for the item; (see at least Chaubard ¶¶ 0007 “the generated training data includes pairings of bounding boxes with item identifiers”; 0020 sensor data used “to determine where in the shopping cart the item was placed”; 0022 “the image labeling module 180 transmits the labeled image data to the store system… for use in training an automatic checkout system”. Chaubard thus discloses training examples formed from image-derived features (bounding boxes) together with the item’s location in the cart, each paired with an item identifier serving as the label.)
processing payment information of the item based on the identifying of the item. (see at least Chaubard ¶ 0016 “In some embodiments, when the customer is ready to checkout from the store, the customer can select a checkout option presented by the portable checkout unit 110 and, in response, the portable checkout unit 110 initiates the transaction for the customer to check out of the store. In some embodiments, the customer checks out of the store without going to a POS system. For example, the customer may use a payment interface 170 provided by the portable checkout unit 110 to pay for the items in their virtual shopping list. The payment interface 170 may include a magnetic card reader, an EMV reader, or an NFC scanner to receive payment information from the customer. Payment information can include credit card information, debit card information, bank account information, or peer-to-peer payment service information. The portable checkout unit 110 transmits payment information received from the customer to the store system 130 to execute the checkout transaction”)
Chaubard does not appear to specifically disclose the following limitations:
inputting the input data for each of the plurality of training examples to the image recognition neural network to generate a plurality of outputs; computing a loss score for each of the plurality of training examples by comparing the plurality of outputs to corresponding labels of the plurality of training examples using a loss function; and updating parameters of the image recognition neural network based on the computed loss scores.
Chaubard discloses generating and transmitting labeled training data and that an automated checkout system is “trained using the training data” (¶¶ 0007, 0022, 0025), but does not specifically describe the mechanics of the supervised training loop — namely inputting the training examples to generate outputs, computing a per-example loss score against the labels using a loss function, and updating the network parameters based on the computed loss.
The Examiner provides Lillicrap to teach the following limitations:
inputting the input data for each of the plurality of training examples to the image recognition neural network to generate a plurality of outputs; (see at least Lillicrap ¶¶ 0004 “artificial neural networks can be trained by using a training set comprising a set of inputs and corresponding expected outputs”; 0038 “an input having a corresponding expected output is supplied to the network, and… an output received from the network”. Lillicrap describes the network in the context of image and character recognition — see ¶ 0002 “image recognition” and ¶ 0048 a network “trained to categorise handwritten digits.”)
computing a loss score for each of the plurality of training examples by comparing the plurality of outputs to corresponding labels of the plurality of training examples using a loss function; (see at least Lillicrap ¶ 0004 “an error signal is generated from the difference between the expected output and the actual output… and a summary of the error called the loss or cost is computed (typically, the sum of squared errors)”; ¶ 0035 defining the loss L from the error e = y* − y between the expected output y* and the network output y.)
updating parameters of the image recognition neural network based on the computed loss scores. (see at least Lillicrap ¶ 0036 “The computed change matrices are then applied to update the parameters via: Wₜ₊₁ = Wₜ − ηΔ”, where η is the learning rate; ¶ 0038 “the connection weights of a weight matrix in the network are modified… steps 22 to 26 are repeatedly performed for a plurality of inputs and corresponding expected outputs”.)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the system and method of Chaubard the supervised neural-network training mechanics taught by Lillicrap since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
In regards to claims 2 and 12, Chaubard discloses the following limitations:
training the image recognition neural network using the set of data obtained from the plurality of sensors. (see at least Chaubard ¶ 0007 “The portable checkout unit generates training data based on the identified portions of the images. In some embodiments, the generated training data includes pairings of bounding boxes with item identifiers. The portable checkout unit may store the generated training data locally or may transmit the generated training data to a store system for storage; and ¶ 0022: The image labeling module 180 transmits the labeled image data to the store system 140 for storage or use in training an automatic checkout system for use in the store. The image labeling module 180 may also transmit additional training data to store system 130”)
In regards to claims 3 and 13, Chaubard discloses the following limitations:
determining weight information related to the item based on the set of data from the plurality of sensors; and identifying the item based on the determined weight information. (see at least ¶ 0020 “For example, the image labeling module 180 may use pressure sensor data, motion sensor data, depth sensor data, or RFID sensor data to determine where in the shopping cart the item was placed”)
Claims 4-5 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Application Publication No. 2018/0330196 A1 to Chaubard (“Chaubard”), in view of WO 2015/011688 A2 to Lillicrap et al. (“Lillicrap”), in view of United States Patent Application Publication No. 2014/0052555 A1 to Macintosh (“Macintosh”).
In regards to claims 4 and 14, Chaubard does not appear to specifically disclose the following limitations:
wherein the plurality of sensors and components comprise at least one sensor configured to: determining shape information related to the item based on the set of data from the plurality of sensors; and identifying the item based on the determined shape information.
The Examiner provides Macintosh to teach the following limitations:
wherein the plurality of sensors and components comprise at least one sensor configured to: determining shape information related to the item based on the set of data from the plurality of sensors; and identifying the item based on the determined shape information. (see at least Macintosh Figure 6, ¶¶ 0091, 0133, and 0159 “Camera 16 generates imagery in which each patch is depicted with a particular size, shape and position within the image frame”)
Therefore it would have been obvious to one of ordinary skill in the art at the time of filing the invention to include in the system and method of Chaubard the teachings of Macintosh since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
In regards to claims 5 and 15, Chaubard teaches using motion sensor data for determining item placement in the cart and identification see at least ¶ 0019, however Chaubard does not appear to specifically disclose the following limitations:
further comprising triangulating motion and incline information of the shopping cart based on the set of data from the plurality of sensors, and identifying the item based on the triangulated motion and incline information.
The Examiner provides Macintosh to teach the following limitations:
further comprising triangulating motion and incline information of the shopping cart based on the set of data from the plurality of sensors, and identifying the item based on the triangulated motion and incline information. (see at least Macintosh ¶ 0122 “plenoptic information...tilt angle”, and ¶ 0085 motion blur)
Therefore it would have been obvious to one of ordinary skill in the art at the time of filing the invention to include in the system and method of Chaubard the teachings of Macintosh since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 6-9 and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Application Publication No. 2018/0330196 A1 to Chaubard (“Chaubard”), in view of WO 2015/011688 A2 to Lillicrap et al. (“Lillicrap”), in view of United States Patent No. 10,192,087 B2 to Davis (“Davis”),
In regards to claims 6 and 16, Chaubard does not appear to specifically disclose the following limitations:
determining location information related to the shopping cart based on the set of data from the plurality of sensors; and identifying the item based on the location information related to the shopping cart.
The Examiner provides Davis to teach the following limitations:
determining location information related to the shopping cart based on the set of data from the plurality of sensors; and identifying the item based on the location information related to the shopping cart. (Davis discloses a system and method of identifying items in a shopping cart by detecting an item being placed into a cart with a camera or weight sensor, and using location data to help determine the item that was placed in the cart. see at least col. 32 line 62 – col. 33 line 6)
Therefore it would have been obvious to one of ordinary skill in the art at the time of filing the invention to include in the system and method of Chaubard the teachings of Davis since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
In regards to claims 7 and 17, Chaubard does not appear to specifically disclose the following limitations:
identifying a set of candidate items based on the location information; and calculating a score for each of the set of candidate items based on the one or more images.
The Examiner provides Davis to teach the following limitations:
identifying a set of candidate items based on the location information; (Davis discloses using location data to help determine a set of items for which the detected item is part of based on the location where the cart was at time of item take. see at least Claim 1, and col. 32 line 40 – col. 33 line 6)
calculating a score for each of the set of candidate items based on the one or more images. (Davis discloses calculating a confidence score based on input to the decision module and refining the confidence score until an item is deduced. see at least Figure 6 and Col. 33 line 22 – line 27; Col. 34 lines 46 – 53)
Therefore it would have been obvious to one of ordinary skill in the art at the time of filing the invention to include in the system and method of Chaubard the teachings of Davis since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
In regards to claims 8 and 18, Chaubard does not appear to specifically disclose the following limitations:
identifying items within a threshold distance of the shopping cart based on the location information.
The Examiner provides Davis to teach the following limitations:
identifying items within a threshold distance of the shopping cart based on the location information. (Davis discloses using data that is within a determined distance to the shopper to determine which item was taken, such as using the store layout to determine where the user was when the item was taken and in the negative with lack of evidence of where the shopper was not. see at least Col. 32 line 38 – Col. 33 line 33)
Therefore it would have been obvious to one of ordinary skill in the art at the time of filing the invention to include in the system and method of Chaubard the teachings of Davis since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
In regards to claims 9 and 19, Chaubard does not appear to specifically disclose the following limitations:
wherein identifying items within the threshold distance of the shopping cart comprises: accessing item layout information of an area around the shopping cart.
The Examiner provides Davis to teach the following limitations:
wherein identifying items within the threshold distance of the shopping cart comprises: accessing item layout information of an area around the shopping cart. (Davis discloses using location data to help determine a set of items for which the detected item is part of based on the location where the cart was at time of item take. see at least col. 32 line 40 – col. 33 line 6)
Therefore it would have been obvious to one of ordinary skill in the art at the time of filing the invention to include in the system and method of Chaubard the teachings of Davis since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 10 is rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Application Publication No. 2018/0330196 A1 to Chaubard (“Chaubard”), in view of WO 2015/011688 A2 to Lillicrap et al. (“Lillicrap”), in view of United States Patent No. 10,192,087 B2 to Davis (“Davis”), in view of United States Patent Application Publication No. 2018/0025412 A1 to Chaubard (“Chaubard2”).
In regards to claim 10, Chaubard does not appear to specifically disclose the following limitations:
wherein the location information comprises global positioning system data.
The Examiner provides Chaubard2 to teach the following limitations:
wherein the location information comprises global positioning system data. (Chaubard2 teaches determining item takes based on images and location information, specifically discloses location data of the image/shopper is based on a GPS location. see at least Chaubard2 Abstract and ¶ 0025)
It would have been obvious to one of ordinary still in the art at the time of filing the invention to include in the system and method of Chaubard the teachings of Chaubard2 since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH M MUTSCHLER whose telephone number is (313)446-6603. The examiner can normally be reached 0600-1430.
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/JOSEPH M MUTSCHLER/ Examiner, Art Unit 3627
/A. Hunter Wilder/Primary Examiner, Art Unit 3627
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