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 .
Claims 1-20 are currently pending in U.S. Patent Application No. 19/010,958 and an Office action on the merits follows.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-20 are rejected on the grounds of nonstatutory double patenting as being unpatentable over one or more claims (see correspondence tables below) of:
U.S. Patent No. 12,211,253 B1 to parent application 17/240,649
in view of references of record to include e.g. Tsai et al. US 10,789,198 B1, Guo et al. US 2014/0379426 A1 and/or Liu et al. “Deepfashion: Powering robust clothes recognition and retrieval with rich annotations” (2016).
Although the claims at issue are not identical, they are not patentably distinct from each other because claims of reference anticipate and/or render obvious independent claim(s) of the instant application, in further view of the following reasons/considerations:
• Instant claims and claims of reference recite common subject matter, and recite the open ended transitional phrase “comprising” which does not preclude any additional elements recited by claims of reference – see the limitation mappings/table presented below;
• Language/terminology of instant claim(s) constituting minor/slight variations from the claims of reference, if/where present (e.g. image comprising ‘spatial coordinates’ vs. images comprising ‘bounding boxes’ inherently/necessarily indicative of spatial coordinates in e.g. the pixel/image domain (i.e. defining the four corners of such boxes), in further view of the manner in which detected bounding boxes for ‘each of one or more products’ (in the disclosed context – e.g. Fig. 11) serve as recognition and localization of upper and lower body apparel), require interpretations under Broadest Reasonable Interpretation and/or plain meaning definitions (MPEP 2173 and 2111) equivalent to/met by language of the reference claims in view of that corresponding/shared Specification. While the disclosure of reference may not be used as prior art (Double Patenting concerns the claims of reference), portions of the specification which provide support for reference claims may also be examined and considered when addressing the scope of claim(s) of reference and the issue of whether an instant claim defines an obvious variation or falls within the scope of an invention claimed in the claim(s) of reference. See MPEP 804 with reference to In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970).
• Whereby element(s) of instant claim(s) otherwise not present explicitly in corresponding reference claim(s) (see * in the table below – namely that ‘transforming’ such that the training samples are explicitly “without background noise”), at most correspond to limitations evidenced by literature of record (see e.g. the corresponding prior art-based rejections below) and requiring no more than obvious modification to the claims of reference, even if it can be argued that the recited ‘cropping’ serves to satisfy the requirement that the sample(s) be “without background noise”. See also Dhua et al. US 10,049,308 B1, Fig. 2B, 4B, Fig. 8 814; and Zhao et al. “Clothing Cosegmentation for Shopping Images With Cluttered Background” (2016) (page 1 “Due to complex backgrounds, the search engines usually return irrelevant results and the desired clothes are totally absent. Therefore, how to efficiently and effectively extract clothing objects and remove backgrounds becomes valuable and critical”), Tsai et al. US 10,789,198 B1 at e.g. col 11 lines 1-10 “In some embodiments, the one or more trained image segmentation based DCNNs may remove the background portions of any received images depicting wearable items in order to obtain just the wearable items, i.e., the foreground wearable items with the background removed” etc.. Examiner additionally notes backgrounds are frequently eliminated from training samples (even if then blended with alternate backgrounds to increase a set of available synthetic training samples – which the recited language does not exclude) so as to disentangle background features from those more directly pertinent to the apparel/object of interest and associated task (e.g. for both detection and classification). Re. upper and lower body apparel, see e.g. Lao et al. “Convolutional Neural Networks for Fashion Classification and Object Detection” (2015) (more specifically Fig. 5 categories head, upper, lower, foot, other), and see also the widely known/referenced Deepfashion dataset from 2016 (Liu et al. “Deepfashion: Powering robust clothes recognition and retrieval with rich annotations” – Fig. 4, page 4 Landmark Annotation for “upper-body items” and “lower-body items”). It would have been obvious to a person of ordinary skill in the art, before the effective filing date, to modify claims of reference such that each of the recited bounding boxes concern one or more products comprising each of both upper-body and lower-body apparel, and further such that by consequence of the cropping and resizing recited the transformed training samples are “without background noise”, as taught/suggested by references of record, the motivation as similarly taught/suggested therein and readily recognized by POSITA that such a training sample modification would ensure the attribute classifier does not associate the label/attribute with features less related to the apparel under consideration (e.g. features of a person’s face/body, for example), in a manner further characterized by a reasonable expectation of success. It would have further been obvious to a person of ordinary skill in the art, before the effective filing date, to modify claims of reference in a manner consistent with those modifications and supporting rationale provided in the prior-art based rejections below, for those same reasons identified therein.
Instant Claims
Claims of Reference US 12,211,253 B1
Claim(s) 1/8/15
A system configured to train an apparel detection model and an attribute recognition model, comprising:
Claim(s) 1/8/15
A system of automatic product attribute recognition, comprising:
a computer, comprising a processor and memory, and configured to:
a computer, comprising a processor and memory, and configured to:
receive training images comprising spatial coordinates associated with one or more apparel products;
receive training images comprising bounding boxes associated with one or more products in the training images;
train a first convolutional neural network model to recognize and localize one or more upper-body apparel and one or more lower-body apparel from the received training images;
train a first convolutional neural network (CNN) model to generate bounding boxes for and identify each of the one or more products with the training images until the accuracy of the first CNN model is above a first predetermined threshold …
*see ODP bullet 3 above re. distinct upper and lower body classes
transform the recognized and localized one or more upper-body apparel and one or
more lower-body apparel to generate training data for a second convolutional neural network,
wherein the training data comprises one or more images without background noise*; and
crop the training images to the image area within the bounding boxes;
resize the cropped images to a predetermined set of dimensions;
*see ODP bullet 3 above
train the second convolutional neural network model to recognize one or more apparel attributes using the generated training data.
train a second CNN model with the resized cropped images and one or more attributes for each of the products associated with the resized cropped images …
Instant application
Claims of Reference
Claim 1/8/15
Claim 1/8/15
Claim 2/9/16
Claim 1/8/15 in view of permissible interpretation for bounding box recited and in view of ODP bullet 3 above – see also prior art-based rejection as applied below
Claim(s) 3/10/17
Claim 2/9/16
Claim(s) 4/11/18
*see ODP bullet 3 above in view of prior art based rejections below – Guo et al. US 2014/0379426 A1
Claim(s) 5-7/12-14/19-20
*see ODP bullet 3 above in view of prior art based rejections below – Liu et al. DeepFashion dataset and scraping, data-sources, associated meta-data, etc., disclosed at Section 2.1 Image Collection
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 1-2, 5-9, 12-16 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Tsai et al. (US 10,769,198 B1) in view of Liu et al. “Deepfashion: Powering robust clothes recognition and retrieval with rich annotations” (2016 – see attached PTO-892 at page 2 NPL Citation No. X).
As to claim 1, Tsai discloses a system configured to train an apparel detection model (Fig. 7 706 “Training an image segmentation neural network based on the first images and the first set of labels”, Fig. 2A) and an attribute recognition model (Fig. 7 712 “Training an image classification neural network based on the second set of predetermined images and the second set of labels”, Fig. 3), comprising:
a computer, comprising a processor and memory (Fig. 9, col 15 line 1-10 “one or more processors (e.g., one or more processors of the server system 102)”, col 16 lines 40-60 device 900 comprising CPU/GPU/TPU 920 and memories 940 and 930), and configured to:
receive training images comprising spatial coordinates associated with one or more apparel products (Fig. 2A 202, col 13 lines 10-35, wherein the segmentation based networks/DCNN/M-RCNNs determine bounding box 503 containing wearable item 501; Examiner recognizes the disclosure of col 13 lines 10-35 concerns a region proposal network (RPN) result/bounding box during execution, however this disclosure at least suggests the ground truth samples for said network comprise ground truth location information – e.g. ground truth bounding boxes and/or landmark information, in the event that Applicant finds Fig. 2B 212-218b to be non-equivalent ‘spatial coordinate’ information; see col 10 lines 50-65 “In step 202 , a set of images depicting wearable items with corresponding labels may be received. In some embodiments, the corresponding labels may depict binary mask layers of the images. FIG. 2B illustrates an exemplary embodiment of a set of images 212a, 214a, 216a, 218a depicting wearable items 212c, 214c, 216c, 218c with corresponding labels which may include binary mask layers 212b, 214b, 216b, 218b of the images”, Fig. 2B 212-218b binary mask layers serving as ‘spatial coordinate’ (in pixel/image domain) information);
Should Applicant assert that the ‘spatial coordinates’ information as recited, in view of corresponding supporting disclosure at e.g. [0085] of the PGPUB, cannot reasonably be read as an equivalent of the binary/silhouette mask information of Tsai’s mask layers for the training samples received at 202 for training that first network equivalent at 204, Examiner takes Official Notice (MPEP 2144.03) to the manner in which such spatial coordinate information is typically used in the form of ground truth bounding boxes as used in supervised training for RPN/detection networks (to further evidence this claim see e.g. Kiapour et al. “Where to buy it…” – attached PTO-892 page 3 NPL Citation No. U).
Tsai further discloses train a first convolutional neural network model (Fig. 2 204) to recognize and localize (col 13 lines 10-35 output bounding box 503, col 13 lines 50-60 “to recover a label and/or information for the wearable item 501”, “such as a product identifier number”, col 14 lines 25-30 “may label each bounding box”, etc.,) one or more upper-body apparel (Fig. 5) and one or more lower-body apparel (Fig. 6, 605) from the received training images (Fig. 2 202-204); Examiner notes that the recited language appears permissibly interpreted such that a bounding box output from the first/segmentation network, associated with either may read provided the network/model is capable of detecting both instances of upper and lower body apparel (which is at least suggested in Tsai, since such a detection result is simply a function of the corresponding training inputs/associated labels, and Tsai’s disclosure supports detecting objects of a wide variety to include e.g. cars, handbags, people, and worn apparel as illustrated/disclosed), further depending on permissible interpretation thereof (e.g. does a dress constitute lower body apparel or does it not count as such because it is also worn on the upper body? Supporting literature supports the later – that it would instead be considered ‘full-body’ and accordingly Tsai may fail to explicitly disclose lower body apparel despite that illustrated dress).
Under any assertion that Tsai fails to fairly disclose a distinct lower-body apparel class, Liu’s DeepFashion dataset and network/model based thereon discloses the same, in addition to ‘spatial coordinate’ information associated with each corresponding training sample (Liu page 1 Section 1, “Previous studies tried to handle the above challenges by annotating clothes datasets either with semantic attributes (e.g. color, category, texture) [1, 3, 6], clothing locations (e.g. masks of clothes) [20, 12], or cross-domain image correspondences [10, 12]. However, different datasets are annotated with different information. A unified dataset with all the above annotations is desired. This work fills in this gap. As illustrated in Fig.1, we show that clothes recognition can benefit from learning these annotations jointly. In Fig.1 (a), given the additional landmark locations may improve recognition. As shown in Fig.1 (b), massive attributes lead to better partition of the clothing feature space, facilitating the recognition and retrieval of cross-domain clothes images”, page 1097 (2 of 9) “Our landmark annotation is at a finer level than existing bounding-box label [12]”, page 1099 (4 of 9) Fig. 4, “for upper-body clothes, lower-body clothes and full-body clothes”, Landmark Annotation section “For instance, the landmarks for upper-body items are defined as left/right collar end, left/right sleeve end, and left/right hem. Similarly, we define landmarks for lower-body items and full-body items”, page 1102 (7 of 9) Attribute Prediction, etc.,).
While not relied upon, see also Lao et al. “Convolutional Neural Networks for Fashion Classification and Object Detection”, e.g. Fig. 12, Fig. 5, etc., in view of distinct lower body apparel classes e.g. jeans, shorts, leggings, pants, skirt.
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 Tsai such that the training samples retrieved/received at 202, and corresponding first model outputs comprising a bounding box and corresponding object label, further comprise distinctions between upper and lower-body (and further even full-body) apparel, in view of corresponding ground truth spatial information (be it in the form of landmarks effectively defining corners of a bounding box, or a ground truth bounding box itself – see above), the motivation as similarly taught/suggested therein and readily apparent to POSITA that such a dataset and correspondingly trained first/segmentation network would thereby be able to distinguish between particular apparel classes of interest, and more effectively reducing a corresponding search space accordingly, for an ultimate task that is predicting/recommending products similar to a query/input image (e.g. distinguishing upper from full would serve to exclude dresses even if they had other attributes similar to a query shirt).
Tsai further teaches/suggests transform the recognized and localized one or more upper-body apparel and one or more lower-body apparel (see Tsai col 14 lines 30-40, wherein the ‘transforming’ is a background removal/masking operation, so as to ensure the image(s) provided to the second/classification model concern primarily the apparel/wearable item of interest “The one or more image segmentation based DCNNs may determine a mask image of the object (also referred to as a segmentation mask) within the bounding boxes”, in further view of that proposed modification above as it would impact corresponding outputs from the first/segmentation model of Tsai, col 11, etc.,) to generate training data for a second convolutional neural network (Fig. 7 708, Fig. 3 302), wherein the training data comprises one or more images without background noise (the ‘mask image’ that is the intermediate output from Tsai’s first/segmentation network, has background portions removed, see corresponding figures, col 13 lines 35-50 “In step 506, the one or more segmentation based DCNNs may determine that the portions of the query image 510 around the mask image 505 are background portions and remove the background portions to obtain just the wearable item 501”, etc., – while not relied upon see also Zhao et al. “Clothing Cosegmentation for Shopping Images with Cluttered Background” (see attached PTO-892 page 1 NPL Citation W)); and
train the second convolutional neural network model to recognize one or more apparel attributes using the generated training data (Fig. 3 304, col 12 line 10 “For example, the one or more classification based DCNNs may be used to determine one or more classifications of the wearable item depicted in the mask image … e.g. the red dress shirt… dress shirt… color and/or pattern … determined based on the determined classifications (e.g., dress shirt, red color, no pattern, etc.) for the wearable item depicted in the mask image”, col 6 “similar styles”, etc.,).
As to claim 2, Tsai in view of Liu teaches/suggests the method of claim 1.
Tsai in view of Liu further teaches/suggests the method wherein each image of the training images comprises one or more annotations of a spatial location of an object located in each image (see Liu as applied in the modification to Tsai as presented above in the rejection of claim 1; while not relied upon see also Kiapour “Where to by it…” NPL).
As to claim 5, Tsai in view of Liu teaches/suggests the method of claim 1.
Tsai in view of Liu further teaches/suggests the method wherein the computer is further configured to: generate one or more images for training by scraping one or more images from one or more external data sources (Liu page 1098 (page 3 of 9) Section 2.1 Image Collection “Shopping websites are a common source for constructing clothing datasets [10, 12]. In addition to this source, we also collect clothing images from image search engines, where the resulting images come from blogs, forums, and the other user-generated contents, which supplement and extend the image set collected from the shopping websites. Collecting Images from Shopping Websites We crawled two representative online shopping websites, Forever212 and Mogujie3. The former one contains images taken by the online store. Each clothing item has 4 ∼ 5 images of varied poses and viewpoints. The latter one contains images taken by both the stores and consumers. Each clothing image in shop is accompanied by several user-taken photos of exactly the same clothing item. Therefore, these data not only cover the image distribution of professional online retailer stores, but also the other different domains such as street snapshots and selfies”), wherein the one or more scraped images comprise one or more associated identifiers and product attributes (Liu section 2.1 “Collecting Images from Google Images4 To obtain meaningful query keywords for clothing images, we traversed the catalogue of several online retailer stores and collected names of clothing items, such as “animal print dress”. This process results in a list of 12,654 unique queries. We then feed this query set to Google Images, and download the returned images along with their associated meta-data. A total of 1,273,150 images are collected from Google Images”, page 1099 “As for the 1,000 attributes, since the number is huge and multiple attributes can fire on the same image, manual annotation is not manageable. We thus resort to the meta-data for automatically assigning attribute labels. Specifically, for each clothing image, we compare the attribute list with its associated meta-data, which is provided by Google or corresponding shopping website”, etc.,; Examiner understands no additional modification/rationale necessary since these limitations further limit the database/training images of Tsai as modified by Liu given the rationale presented above in the rejection of claim 1, however Liu does explicitly disclose additional rationale regarding e.g. obtaining associated identifiers and product attributes from meta-data information among others).
As to claim 6, Tsai in view of Liu teaches/suggests the method of claim 5.
Tsai in view of Liu further teaches/suggests the method wherein the one or more generated images each further comprise one or more tags, the one or more tags comprising one or more identifiers and one or more product attributes (see Liu disclosure identified above and pertinent portions reproduced below).
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As to claim 7, Tsai in view of Liu teaches/suggests the method of claim 5.
Tsai in view of Liu further teaches/suggests the method wherein the wherein the one or more external data sources comprise one or more of: one or more online postings, one or more advertisements, one or more product reviews, one or more fashion guides, one or more fashion e-magazines and one or more photographs (Liu Section 2.1).
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As to claim 8, this claim is the method claim corresponding to the system (generic computer structure required, processor + memory) of claim 1 and is rejected accordingly.
As to claim 15, this claim is the non-transitory CRM claim corresponding to the system/method claim(s) 1/8 respectively and is rejected accordingly. See also Tsai’s corresponding CRM disclosure at e.g. col 16 lines 60-66 and col 17 lines 1-5 – secondary memory 930 and disclosed CRM embodiments.
As to claims 9 and 16, these claims are the method and CRM claims respectively corresponding to system claim 2, and are rejected accordingly.
As to claims 12-14 and 19-20, these claims are the method and CRM claims respectively corresponding to system claims 5-7 respectively, and are rejected accordingly.
2. Claims 3, 10 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Tsai et al. (US 10,769,198 B1) in view of Liu et al. “Deepfashion: Powering robust clothes recognition and retrieval with rich annotations”, and Lao et al. “Convolutional Neural Networks for Fashion Classification and Object Detection”.
As to claim 3, Tsai in view of Liu teaches/suggests the method of claim 1.
Tsai in view of Liu further teaches/suggests the method wherein the generated training data comprises one or more of: cropped images, rescaled images and resized images (Tsai discloses cropped images, in view of the manner in which areas outside of bounding box 603, for e.g. image 604, are eliminated prior to that additional background removal/masking (for background portions inside the bounding box)). Examiner also understands all of the recited transformation embodiments to constitute data augmentation techniques routinely employed when generating training samples, and Lao evidences the same.
Lao discloses equivalent data augmentation for samples used in training garment attribute classification models, section 2.1 “We will be using the Apparel Classification with Style (ACS) Dataset [3], which contains 89,484 images that been cropped based on bounding boxes aimed at encapsulating the clothing on an individual’s upper body”, 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 Tsai in view of Liu, such that generated second training samples for that second/classification network are further subject to one or more of the recited data augmentation techniques as taught/suggested by Tsai and/or Lao, the motivation as similarly suggested therein and readily recognized by POSITA that such augmentation would avoid overfitting and improve model generalization.
As to claims 10 and 17, these claims are the method and CRM claims respectively corresponding to system claim 3, and are rejected accordingly.
3. Claims 4, 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Tsai et al. (US 10,769,198 B1) in view of Liu et al. “Deepfashion: Powering robust clothes recognition and retrieval with rich annotations”, and Guo et al. (US 2014/0379426 A1).
As to claim 4, Tsai in view of Liu teaches/suggests the method of claim 1.
Tsai fails to explicitly disclose the method further configured to generate one or more recommendations of in-store products from Fig. 4 408 determine one or more recommended wearable items, Fig. 4 410, Fig. 5 508, col 13 lines 40-55, etc.,).
Tsai fails to explicitly disclose the method wherein the provided product recommendations are derived based in part from trending social media content – however Tsai arguably suggests the same if it can be asserted that the query image/media may be derived by one or more users from such trending media (e.g. the user recognizes a trending product from social media, and decides to provide an image of that product as the query image – in such a case Tsai would read). Liu may arguably suggest the same given those disclosed collection practices – even if indirectly, if e.g. Forever21, Mogujie, blogs/forums, etc., are sources of data indicative of or themselves considered trending ‘social media’ under BRI.
Guo further and more explicitly evidences the obvious nature of product recommendations derived based at least in part from trending social media content (Abs “The system may also make statistical inferences as to what types of clothing may be favored and disfavored by the user's social network group, and present these recommendations to the user. Other factors, such as weather, event type, and user's recent history of wearing various wardrobe items can also be considered. The system can additionally assist in shopping and gift giving, provide fashion related games, spot fashion trends, and provide advanced data for fashion suppliers”, Fig. 9 ‘regional trend’ and ‘news on style/trend’ as raw data input for ranking and prediction data mining prior to final ‘adaptive feedback’, [0012] shopping or gift recommendations, [0021] “In this embodiment, the regional clothing style trend prediction methods can integrate data from multiple sources, such as user personal preferences, regional store sales data, forum discussions, social network friend ranking data, news feed data, geographic data, and the history of preferences and daily outfit choices from different users in the same region. The system can then take this data, and automatically compute statistical reports pertaining to the latest fashion trends, such as what types and styles of clothing, in what colors, are becoming more popular”, etc.,).
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 Tsai in view of Liu, such that the provided product recommendations consider not only/solely a similarity to the query image given associated classification attributes, but also statistical information regarding latest fashion trends as complementary information concerning product/ apparel relevance, the motivation as similarly taught/suggested therein that such a consideration of fashion trends would facilitate the identification of those products/ apparel that would for example serve as good/well received gifts, among other considerations readily apparent to POSITA.
As to claims 11 and 18, these claims are the method and CRM claims respectively corresponding to system claim 4, and are 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.
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IAN L LEMIEUX whose telephone number is (571)270-5796. The examiner can normally be reached Mon - Fri 9:00 - 6:00 EST.
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/IAN L LEMIEUX/Primary Examiner, Art Unit 2669
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