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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on April 28, 2026 has been entered.
Status of the Claims
This action is in response to the claims filed on October 31, 2025.
Claims 1-9 and 11-20 are pending.
Claim 10 has been cancelled
Claims 1, 2, 9, 12-13 and 20 have been amended.
Response to Arguments
Applicant's arguments filed April 28, 2026 have been fully considered but they are not persuasive. Applicant argues on pages 8-11 claim rejections under 35 USC 101. Applicant argues that the amended claims are not directed to an abstract idea because they recite:
a multi-stage machine-learning pipeline that processes digital media and contextual information to generate visual and functional feature vectors;
neural-network-based object identification;
a real-time, location-aware guidance system based on a map of the physical location and a user device’s location; and
a technological improvement in object recognition and navigation.
Applicant further argues that the Office’s characterization of the claims as generic computer implementation overlooks the claimed technical mechanisms and that the claims therefore integrate any alleged judicial exception into a practical application.
Examiner disagrees. Applicant’s arguments are not persuasive.
The amended claims, as a whole, are directed to the abstract idea of receiving information, analyzing information, comparing that information to known data, and outputting a result, together with guiding a user based on that result. The additional recitations below do not meaningfully alter the character of the claims. These are still steps of data gathering, data processing, comparison, and result presentation:
visual feature vectors
functional feature vectors
trained neural networks
guidance route
monitoring a real-time location
transmitting an alert
The claims do not recite a specific improvement to computer functionality itself. Rather, they use conventional computer-implemented tools to carry out the abstract tasks of recognizing products and guiding a user to a location of interest.
Applicant argues on pages 9-10, that the claims integrate the judicial exception into a practical application because they recite a “multi-stage machine-learning pipeline” and “real-time, location-aware guidance system.” Examiner disagrees. The claim language reciting generation of visual and functional feature vectors merely describes data representations used to perform classification and matching. Such recitations do not impose a meaningful limit on how the computer operates. They are functionally defined results, not a specific technological implementation.
Next, Applicant argues that the claims improve object recognition and navigation technology. However, the specification and claims, as amended, do not set forth a technical improvement to computer functionality itself, such as a new data structure, a new neural-network architecture, a new camera calibration technique, a new routing engine, or a new device control mechanism. The amended claims recite standard computer-based tools, including: digital media processing, contextual information processing, neural networks, maps, user device monitoring, and alerts. These are generic computational tools used in their ordinary ways. The claims do not require any non-conventional operation of those tools. As such, the claims amount to no more than applying abstract ideas with generic computer components. The record, including the cited patents, demonstrates that these functions were well understood and conventional in the retail, product-recognition, and wayfinding arts.
The additional limitations merely implement the abstract idea using known components and routine operations: feature extraction, neural-network classification, map-based route generation, location monitoring, and alerting. The combination of these elements does not amount to significantly more than the abstract idea itself. The claims do not recite an unconventional arrangement of components, a novel machine, or a technical improvement in the operation of the computer system.
Applicant argues on pages 11-12 that Truitner “does not disclose or suggest processing contextual information associated with the digital media to generate a functional feature vector” and “does not distinguish between the visual and functional feature representations.”
Examiner disagrees. Applicant’s arguments are not persuasive.
Truitner expressly discloses feature extraction and the use of a neural-network-based recognition process to identify consumer products from images and video. For example, Truitner states that the recognition application performs:
feature extraction
template matching
generate[s] a machine learning model which is trained based on the assigned plurality of identification tags to output the known consumer product.
Truitner further explains that the system uses image sets, tags, and matching confidence thresholds to identify consumer products, including:
the recognition application captures images and uses general classification methods as well as multi-scale template matching to achieve more detailed identification of consumer products detected in the captured images and
the neural network determines a distinct feature of the unknown consumer product.
These disclosures teach identifying objects from digital media using extracted features and trained neural networks.
Applicant’s recitation of a functional feature vector based on contextual information does not patentably distinguish over Truitner. Truitner expressly contemplates use of:
object characteristics,
assigned identification tags,
contextual classification,
and feature-based narrowing of candidate products.
Truitner further discloses that the recognition application uses contextual classifications and tags such as product type, color, brand, material, and other attributes to refine identification. The use of such contextual attributes would have suggested to one of ordinary skill in the art that non-visual information associated with an object can be incorporated into the recognition process as additional feature data.
Accordingly, even if Truitner does not use the exact words “functional feature vector,” the claimed concept of generating and using feature representations derived from the media and its associated context would have been obvious in view of Truitner’s teachings.
Also, the claim does not require any particular algorithmic implementation for generating the asserted “visual” and “functional” vectors. Rather, the claim broadly recites generating feature representations from digital media and contextual information and using them in trained neural networks. Such broad functional language reads on Truitner’s disclosure of:
image analysis,
feature extraction,
template matching,
ranking tags,
and neural-network-based output of product identity.
The applicant has not shown that the claimed “functional feature vector” is meaningfully different from the contextual feature extraction and classification already disclosed or suggested by the combination of references.
Next, Applicant argues on page 12 that Schack does not disclose or suggest the amended guidance limitation because Schack allegedly only teaches alerts regarding replacement items and not “generating a guidance route based on a map of the physical location, monitoring a real-time location of a user device within the physical location, and transmitting an alert to the user device in response to determining that the user is within a defined proximity to at least one of the set of target items.”
Examiner disagrees. This argument is not persuasive.
Schack discloses a wayfinding application that:
receives a current location of a client device within a warehouse
accesses a layout of the warehouse describing locations of items,
identifies a route from the current location to one or more target item locations,
generates augmented reality elements comprising instructions for navigating the route, and
sends the augmented reality elements to the display area of the client device.
Schack therefore directly teaches generating a guidance route based on a map/layout of the physical location and monitoring the device’s current location to navigate the user to the target item.
Applicant attempts to characterize Schack as merely generating an alert for a replacement item. That characterization is incomplete. Schack discloses a full navigation workflow, including:
a layout database,
a route engine,
a location tracking system,
and a display of navigation instructions and augmented reality elements.
Thus, Schack’s guidance is not limited to a static alert. It explicitly includes active route generation and user navigation within a physical retail environment.
Applicant’s recitation of transmitting an alert when the user is within a “defined proximity” to a target item is an obvious variation of Schack’s route guidance and item detection functions. Schack already teaches determining a current location, identifying a route, detecting item locations, and modifying guidance based on the user’s position and item presence.
Using a proximity threshold to trigger a user alert is a predictable and routine implementation choice in the context of Schack’s wayfinding system. Such a limitation would have been obvious to one of ordinary skill in the art.
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 1-9 and 11-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
Claims 1-9 and 11-20 are directed to a method, system and computer readable medium. Thus, each of the claims falls within one the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A- Prong One
Independent claims 1, 12 and 20 recite steps that, under their broadest reasonable interpretations, cover certain methods of organizing human activity, e.g. advertising, marketing or sales activities. Specifically, claim 1 recites:
receiving a digital media from a computing device coupled to a point-of-sale (POS) terminal associated with a physical location;
identifying objects depicted within the digital media, comprising:
processing the digital media and contextual information associated with the digital media to generate a visual feature vector and a functional feature vector for at least one of the objects, the visual feature vector representing visual characteristics of the at least one object, and the functional feature vector representing functional characteristics of the at least one object, and
inputting the extracted visual and functional feature vectors into one or more trained neural networks (NNs) to output one or more product identifiers corresponding to the objects;
determining items currently available at the physical location by analyzing information collected by a set of cameras at the physical location;
identifying a set of target items, from the items currently available at the physical location, that are similar to at least one of the objects based on the visual and functional vector feature vectors extracted from the digital media and corresponding visual and functional vector feature vectors extracted from the collected information; and
generating a guidance that navigates to at least one of the set of target items within the physical location, comprising:
generating a guidance route based on a map of the physical location,
monitoring a real-time location of a user device within the physical location, and
transmitting an alert to the user device in response to determining that the user device is within a defined proximity to at least one of the set of target items.
But for the recitation of generic computer components like digital media, computing device, user device, point-of-sale (POS) terminal, neural networks (NNs) and cameras, the italicized functions, when considered as a whole, describe a situation where a person walks into a store or warehouse looking for certain items. If those certain items are not available, the person is guided to alternative or similar items that are available. Accordingly, claim 1 recites an abstract idea in the form of a certain method of organizing human activity.
Similarly, claims 12 and 20 recite:
one or more memories collectively storing computer-executable instructions; and
one or more processors configured to collectively execute the computer-executable instructions and cause the system to:
receive a digital media from a computing device coupled to a point-of-sale (POS) terminal associated with a physical location;
identify objects depicted within the digital media, comprising:
processing the digital media and contextual information associated with the digital media to generate a visual feature vector and a functional feature vector for at least one of the objects, the visual feature vector representing visual characteristics of the at least one object, and the functional feature vector representing functional characteristics of the at least one object, and
inputting the extracted visual and functional feature vectors into one or more trained neural networks (NNs) to output one or more product identifiers corresponding to the objects;
determine items currently available at the physical location by analyzing information collected by a set of cameras at the physical location;
identify a set of target items, from the items currently available at the physical location, that are similar to at least one of the objects based on the visual and functional vector feature vectors extracted from the digital media and corresponding visual and functional vector feature vectors extracted from the collected information; and
generate a guidance that navigates to at least one of the set of target items within the physical location, comprising:
generating a guidance route based on a map of the physical location,
monitoring a real-time location of a user device within the physical location, and
transmitting an alert to the user device in response to determining that the user device is within a defined proximity to at least one of the set of target items.
But for the recitation of generic computer components like digital media, computing device, user device, point-of-sale (POS) terminal, neural networks (NNs) and cameras, the italicized functions, when considered as a whole, describe a situation where a person walks into a store or warehouse looking for certain items. If those certain items are not available, the person is guided to alternative or similar items that are available. Accordingly, claims 12 and 20 recite an abstract idea in the form of a certain method of organizing human activity.
Dependent claims 2-9, 11 and 13-19 inherit the limitations that recite an abstract idea from their dependence on claims 1 and 12, respectively, and thus these claims also recite an abstract idea under the Step 2A- Prong 1 analysis. In addition, claims 2-9, 11 and 13-19 recite additional limitations that further describe the abstract idea identified in the independent claims. Examiner notes that dependent claims 2-9, 11 and 13-19 recite some additional generic computer components like user device, neural networks, inventory database, artificial intelligence-based algorithms and a scanning camera which will be analyzed later.
Claims 2 and 13 recite wherein the guidance is displayed on at least one of (i) the POS terminal associated with the physical location or (ii) the user device, and the guidance is displayed along with information related to at least one of the set of target items. (Commercial or legal interactions including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)
Claims 3 and 13 recite monitoring, via the set of cameras at the physical location, changes in status of at least one of the set of target items in real time, and updating the guidance based on the changes. (Commercial or legal interactions including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)
Claims 4 and 13 recite accessing an inventory database to check inventory of at least one of the objects at one or more other physical locations; and generating an alternative purchase path to the user. (Commercial or legal interactions including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)
Claims 5 and 13 recite wherein the set of cameras at the physical location are configured with artificial intelligence-based algorithms to determine at least one of (i) a category or (ii) a quantity of each of the items currently available at the physical location. (Commercial or legal interactions including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations and mathematical calculations, formulas or relationships)
Claims 6 and 13 recite wherein the set of target items comprises at least one of (i) one or more currently available items that are same as at least one of the objects, (ii) one or more currently available items that are visually similar to at least one of the objects, or (iii) one or more currently available items that are functionally similar to at least one of the objects. (Commercial or legal interactions including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)
Claims 7 and 13 recite wherein the digital media may comprise at least one of an image, a video, a live stream, a three-dimensional model, or a motion graphic. (Commercial or legal interactions including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)
Claims 8 and 13 recite wherein: the one or more neural networks are trained using historical received digital media as inputs, and labeled product identifiers as target outputs, and the one or more neural networks learn to correlate features from each respective digital media of the historical received digital media to a respective product identifier of the labeled product identifiers. (Commercial or legal interactions including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations and mathematical calculations, formulas or relationships)
Claim 9 recites receiving feedback from the user device regarding an accuracy of the objects identified from the digital media; and refining the one or more neural networks based on the received feedback. (Commercial or legal interactions including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations and mathematical calculations, formulas or relationships)
Claim 11 recites wherein receiving the digital media comprises scanning, by the POS terminal, the digital media displayed on the computing device, and wherein the POS terminal comprises a scanning camera. (Commercial or legal interactions including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)
Step 2A-Prong 1: YES. The claims are abstract
Step 2A- Prong Two
This judicial exception is not integrated into a practical application. In particular, independent claims 1, 12 and 20 do not include additional elements that integrate the abstract idea into a practical application. The additional elements that are cited one or more memories collectively storing computer-executable instructions, computing device, POS terminal, processors, digital media, neural network, user device and cameras amount to mere instructions to implement an abstract idea on a computer, see applicant’s specification paragraphs 12-14, 64-66 and 75-80 and see MPEP 2106.05(f)).
The judicial exception recited in dependent claims 2-9, 11 and 13-19 is also not integrated into a practical application under a similar analysis as above. The functions of claims 2-11 and 13-19 are performed with the same additional elements introduced in the independent claims, along with additional generic computer components like a device associated with a physical location, a devise of a user, neural networks, inventory database, artificial intelligence-based algorithms and a scanning camera however, even these additional elements amount to mere instructions to implement an abstract idea on a computer (see MPEP 2106.05(f)).
Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application.
Step 2B
Claims 1-9 and 11-20 does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements one or more memories collectively storing computer-executable instructions, processors, digital media, cameras, a device associated with a physical location, user device, neural networks, inventory database, artificial intelligence-based algorithms, computing device, POS terminal and a scanning camera amount to mere instructions to implement an abstract idea on a computer (see MPEP 2106.05(f)) as evidenced by Applicants specification paragraphs 12-14, 64-66 and 75-80. The computer components above are described at a very high level and generic in nature such that one of ordinary skill in the art would understand that generic computer components could be used to implement the invention.
Step 2B: NO. The claims do not provide significantly more.
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, 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-9 and 11-20 are rejected under 35 U.S.C. 103 as being unpatentable over Schack et al., US Patent No. 11,783,400 (see PTO-892, Ref. C) in view of Keith, US Patent No. 10,915,906 (ss PTO-892, Ref. A) and further in view of Truitner, US Patent No. 11,907,841 (see PTO-892, Ref. D).
As per claim 1, Schack teaches
determining items currently available at the physical location by analyzing information collected by a set of cameras at the physical location (see col. 27, lines 53-67 and col. 28, lines 1-14);
identifying a set of target items, from the items currently available at the physical location, that are similar to at least one of the objects based on the digital media and the collected information (see col. 29, lines 31-58); and
generating a guidance that navigates the user to at least one of the set of target items within the physical location, comprising (see col. 41, lines 8-34),
generating a guidance route based on a map of the physical location (see col. 41, lines 8-34),
monitoring a real-time location of a user device within the physical location (see col. 41, lines 8-34), and
transmitting an alert to the user device in response to determining that the user device is within a defined proximity to at least one of the set of target items (see col. 47, lines 49-67 and col. 48, lines 1-23).
Schack does not explicitly teach receiving a digital media from a computing device coupled to a point-of-sale (POS) terminal associated with a physical location, identifying objects depicted within the digital media, comprising: extracting visual feature vectors and functional feature vectors from the digital media, inputting the extracted visual and functional feature vectors into one or more trained neural networks (NNs) to output one or more product identifiers corresponding to the objects and identifying a set of target items, from the items currently available at the physical location, that are similar to at least one of the objects based on the visual and functional vector feature vectors extracted from the digital media and corresponding visual and functional vector feature vectors extracted from the collected information.
Keith teaches receiving a digital media from a computing device coupled to a point-of-sale (POS) terminal associated with a physical location (see col. 16, lines 10-16).
Therefore, it would be prima facie obvious to a person of ordinary skill in the art before the effective filing date of the invention to combine the teachings of Schack and Keith to receive digital media from a computing device coupled to a point-of-sale (POS) terminal associated with a physical location because it would assist a consumer to pick up the item at a physical location (see col. 22, lines 61-65).
Truitner teaches identifying objects depicted within the digital media, comprising:
processing the digital media and contextual information associated with the digital media to generate a visual feature vector and a functional feature vector for at least one of the objects, the visual feature vector representing visual characteristics of the at least one object, and the functional feature vector representing functional characteristics of the at least one object (see column 10, lines 8-67 and column 11, lines 1-51),
inputting the extracted visual and functional feature vectors into one or more trained neural networks (NNs) to output one or more product identifiers corresponding to the objects (see column 11, lines 12-30) and
identifying a set of target items, from the items currently available at the physical location, that are similar to at least one of the objects based on the visual and functional vector feature vectors extracted from the digital media and corresponding visual and functional vector feature vectors extracted from the collected information (see column 10, lines 8-67 and column 11, lines 1-51).
Therefore, it would be prima facie obvious to a person of ordinary skill in the art before the effective filing date of the invention to combine the teachings of Schack and Truitner to identify items based on digital media visual and functional vectors or features because it would assist a consumer to identify a products easier as taught by Truitner (see col. 9, lines 9-15).
Regarding claim 2, Schack, Keith and Truitner teach the method of claim 1 as seen above. Schack further teaches wherein the guidance is displayed on at least one of (i) the POS terminal associated with the physical location or (ii) the user device, and the guidance is displayed along with information related to at least one of the set of target items (see col. 3, lines 57-67- col. 4, lines 1-21 and col. 6, lines 50-52).
Regarding claim 3, Schack, Keith and Truitner teach the method of claim 1 as seen above. Schack further teaches monitoring, via the set of cameras at the physical location, changes in status of at least one of the set of target items in real time, and updating the guidance based on the changes (see col. 45, lines 32-67- col. 46, lines 1-41).
Regarding claim 4, Schack, Keith and Truitner teach the method of claim 1 as seen above. Schack further teaches accessing an inventory database to check inventory of at least one of the objects at one or more other physical locations; and generating an alternative purchase path (see col. 46, lines 16-41 and col. 49, lines 9-55).
Regarding claim 5, Schack, Keith and Truitner teach the method of claim 1 as seen above. Schack further teaches wherein the set of cameras at the physical location are configured with artificial intelligence-based algorithms to determine at least one of (i) a category or (ii) a quantity of each of the items currently available at the physical location (see col. 12, lines 3-67- col. 13, lines 1-7).
Regarding claim 6, Schack, Keith and Truitner teach the method of claim 1 as seen above. Schack further teaches wherein the set of target items comprises at least one of (i) one or more currently available items that are same as at least one of the objects, (ii) one or more currently available items that are visually similar to at least one of the objects, or (iii) one or more currently available items that are functionally similar to at least one of the objects (see col. 29, lines 31-67- col. 30, lines 1-26).
Regarding claim 7, Schack, Keith and Truitner teach the method of claim 1 as seen above. Keith further teaches wherein the digital media may comprise at least one of an image (see col. 16, lines 10-16).
Therefore, it would be prima facie obvious to a person of ordinary skill in the art before the effective filing date of the invention to combine the teachings of Schack and Keith to receive digital media comprising of an image because it would assist a consumer to pick up the item at a physical location (see col. 22, lines 61-65).
As per claim 8, Schack, Keith and Truitner teach the method of claim 1 as seen above. Truitner further teaches wherein:
the one or more neural networks are trained using historical received digital media as inputs, and labeled product identifiers as target outputs (see column 10, lines 8-67 and column 11, lines 1-51), and
the one or more neural networks learn to correlate features from each respective digital media of the historical received digital media to a respective product identifier of the labeled product identifiers (see column 10, lines 8-67 and column 11, lines 1-51).
Therefore, it would be prima facie obvious to a person of ordinary skill in the art before the effective filing date of the invention to combine the teachings of Schack and Truitner to train neural networks with digital media inputs because it would assist a consumer to identify a products easier as taught by Truitner (see col. 9, lines 9-15).
As per claim 9, Schack, Keith and Zheng teach the method of claim 8 as seen above. Truitner further teaches comprising
receiving feedback from the user device regarding an accuracy of the objects identified from the digital media; and
refining the one or more neural networks based on the received feedback (see col 11, lines 22-30 and col 16, lines 24-38).
Therefore, it would be prima facie obvious to a person of ordinary skill in the art before the effective filing date of the invention to combine the teachings of Schack, Keith Zheng with Truitner to train neural networks with feedback regarding a product because it would allow for improvements to the digital media image as taught by Truitner (see col 16, lines 33-38).
Regarding claim 11, Schack, Keith and Truitner teach the method of claim 1 as seen above. Keith further teaches wherein receiving the digital media comprises scanning, by the POS terminal, the digital media displayed on the computing device, and wherein the POS terminal comprises a scanning camera. (see col. 22, lines 66-67, col. 23, lines 1-17 and col. 37, lines 17-24 and 54-59).
Therefore, it would be prima facie obvious to a person of ordinary skill in the art before the effective filing date of the invention to combine the teachings of Schack and Keith to scan digital media displayed on a user device by a scanning device at a physical location because it would help a retailer with loss prevention initiatives as taught by Keith (see col. 22, lines 66-67 and col. 23, lines 1-17).
Claims 12 and 20 recite similar limitations to claim 1 and thus rejected using the same art and rationale in the rejection of claim 1 as set forth above.
Claim 13 recites similar limitations to claim 2 and thus rejected using the same art and rationale in the rejection of claim 2 as set forth above.
Claim 14 recites similar limitations to claim 3 and thus rejected using the same art and rationale in the rejection of claim 3 as set forth above.
Claim 15 recites similar limitations to claim 4 and thus rejected using the same art and rationale in the rejection of claim 4 as set forth above.
Claim 16 recites similar limitations to claim 5 and thus rejected using the same art and rationale in the rejection of claim 5 as set forth above.
Claim 17 recites similar limitations to claim 6 and thus rejected using the same art and rationale in the rejection of claim 6 as set forth above.
Claim 18 recites similar limitations to claim 7 and thus rejected using the same art and rationale in the rejection of claim 7 as set forth above.
Claim 19 recites similar limitations to claim 8 and thus rejected using the same art and rationale in the rejection of claim 8 as set forth above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAHID R MERCHANT whose telephone number is (571)270-1360. The examiner can normally be reached M-F 7:30-5.
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/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
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