Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Claims 1, 3 and 10 has been amended.
Claims 11-20 has been withdrawn previously.
Claims 1-10 are still pending for consideration.
Response to Arguments
Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-5 are rejected under 35 U.S.C. 103 as being unpatentable over
Lindgren (US 20230385903 A1) and Jung (US 20150092191 A1), in view of Ramamurthy et al. (US 20120166471 A1), and further in view of Chong et al. (US 20100194776 A1).
Regarding claim 1, Lindgren teach a method for predicting body complexion characteristics for body care (see Abstract: “The system may receive a photograph of an individual, detect non-facial skin areas and hair boundaries, and analyze the non-facial skin areas and hair to make predictions about the tone, type and concern related to those skin areas and hair”, see para [0110]; “User input data may be used to determine consumer specific preferences, characteristics, and requirements related to health and/or beauty products”) comprising predicting one or more objects in a plurality of first inputs representing at least one subject area on a body of the client at least by executing, at an extraction model of the trained feature learning model, an object extraction model of the trained feature learning model (see Abstract; “and analyze the non-facial skin areas and hair to make predictions about the tone, type and concern related to those skin areas and hair”, see also para [0068]; “a convolutional neural network (CNN) with a trained encoder and decoder… An encoder may be trained to extract features corresponding to a pre-determined set of skin concern objects.. ... The encoder can be trained to extract features and detect one or more skin concern objects that may be present in the processed image in order to classify user skin concerns such that the system 100 may make personalized recommendations”), wherein the one or more objects comprise at least one of a freckle, hair, wrinkle, or a color spot, and at least some of the plurality of first inputs (see para [0055]; “fact-based input data (e.g., hair type, hair porosity, hair texture, skin type, dark spots, acne, hyperpigmentation, allergies, beauty routine, beauty product preference(s), etc.), a user-uploaded photograph, concern-based input data (e.g., fine lines and wrinkles, loss of skin elasticity, thinning hair, damaged hair, sun damage to skin, etc.)”) and are transmitted as a first data stream from an image capturing device of a mobile computing device to the extraction model (see para [0055]; “Examples of implicit data collection include, but are not limited to… mobile computing device (e.g., smart phone, tablet, laptop, smart wearable, etc.)”, see also Fig. 2, steps 210, 201, and 202, see para [0066]; “when a system 100 user uploads a photograph it may trigger skin analysis engine 200 to receive, retrieve, or otherwise obtain the uploaded image data 210 in order to perform skin analysis. The face detection module 201 analyzes the image data and detects the face of the user. Additionally, face detection module 201 can detect ethnic facial types. Upon detection of the user's face, the image is cropped and fed into a face oval extraction module 202 that extracts the face oval of user in the uploaded image”); receiving, at a classification model of the trained feature learning model, the one or more objects that are predicted by the extraction model and are transmitted to the classification model as a second data stream (see para [0066]; “Upon detection of the user's face, the image is cropped and fed into a face oval extraction module 202 that extracts the face oval of user in the uploaded image. The face oval can be used to determine the face shape of the user. The non-skin area removal module 203 may be used to remove non-skin areas from the face (e.g., eyebrows, eyes, lips, etc.). After the image data 210 has been processed through these modules, what remains is a processed image of the user's face without its facial features. The processed image may then be sent to either of or both of a classification network 206 to determine the skin-tone of the user and an image segmentation network 204 that detects and classifies the skin concerns of the user”); classifying, at a classification model of the trained feature learning model, the one or more objects into one or more respective classes (see para [0071]; “one or more classification networks 206 may be trained and configured to classify input images 210 such as for skin-tone analysis, skin concern analysis and various other skin related tasks. The outputted skin-tone 230 may be sent to data analysis”), wherein the body complexion index or value represents a body complexion characteristic of the at least one subject area of the client (see para [0087]; “Classification network may receive hair data from secondary feature module 1304 and use it as input into a classifier network which produces as output either of or both of a hair-tone and hair type 1320. A hair-tone may be color associated with a user's hair (e.g., on head, facial hair, body hair, etc.). A hair type may include the non-limiting, curly (e.g., loose curls, tight curls, etc.), wavy, coily, straight, thin, thick, long, short, fine, prone to oil, coarse, won't hold curls, etc. Hair type classification may be achieved by training a CNN on a dataset of labeled image data of various types of hair”), wherein presenting the textual and graphical information comprising: generating a set of textual and graphical information based at least in part upon the body complexion index or value (see para [0060]; “The outputted recommended products may be in vector form. A similarity score may be calculated between the outputted product vectors and the requirement vector. The product(s) with the highest similarity score may be displayed to the customer as recommended products”). However, Lindgren does not specifically teach wherein a class of the one or more respective classes corresponds to a color index or value in a L*A*B* color space or a Lch color space; predicting a body complexion index or value for the at least one subject area on the body of the client based at least in part upon the class; and presenting, in a user interface of the mobile computing device, textual and graphical information pertaining to the body complexion index or value.
In the same field of endeavor, Jung et al. teaches wherein a class of the one or more respective classes corresponds to a color index or value in a L*A*B* color space or a Lch color space (see para [0005]; “the handheld device processes data resulting from the spectral measurements to compute color values (e.g., L*, a*, b* values or L*, C*, h* values), and based on such data and/or such computed values one or more cosmetic products (e.g., a foundation) may be selected. Such selection preferably is determined based on a prediction algorithm which provides a prediction of a cosmetic product that a human observer would affirm is a good match or otherwise a suitable selection for the skin being measured”); predicting a body complexion index or value for the at least one subject area on the body of the client based at least in part upon the class (see para [0083]; “FIG. 6B is a flow chart for developing and updating a Classifier for shade recommendation. Field data is collected in the form of spectral measurements (612) and Features are extracted (614) in the form of the tristimulus calculations for Lightness, Chroma and hue (L*, C* and h*). The Features are used for class label prediction (616)”); and presenting, in a user interface of the mobile computing device, textual and graphical information pertaining to the body complexion index or value (see para [0007]; “It is another object of the present invention to provide systems and methods for communicating data resulting from such measurements, or predictive or other assessments based on such resulting data, via a display integral to the device to communicating via a wired or preferably wireless data connection to an external network”, see also para [0005]; “the device communicates wirelessly to an external network and also to a companion device such as a smartphone, tablet, notebook computer or other computing device”, and para [0113]; “a display, preferably an LCD, for conveniently providing graphic, textual and numeric information to users to provide an intuitive and powerful user interface”). Accordingly, it would have been obvious to one of ordinary skill in the art at the time of invention of the claimed invention to modify the general a method for intelligent context-based personalized beauty product recommendation of Lindgren in view of the use of method for measuring spectra and other optical characteristics of skin for predicting products to match the measured skin of Jung et al. in order to provide product in an intuitive and easy-to-use manner (see para [0005]).
However, the combination of Lindgren and Jung et al. as a whole does not teach filtering, via a first on-screen widget in the user interface or a hierarchical or nested filtering network, the set of textual and graphical information into a first reduced subset of textual and graphical information based at least in part upon a first characteristic pertaining to or for the client, the first reduced subset smaller than the set of textual and graphical information; filtering, via the first on-screen widget or a second on-screen widget in the user interface or the hierarchical or nested filtering network, the first reduced subset into the textual and graphical information based at least in part upon a second characteristic pertaining to or for the client, the second characteristic different from the first characteristic; and rendering the textual and graphical information on the user interface that has been at least twice reduced from the set of textual and graphical information, the textual and graphical information smaller than the first subset of textual and graphical information.
In the same field of endeavor, Ramamurthy et al. teaches filtering, via a first on-screen widget in the user interface or a hierarchical or nested filtering network, the set of textual and graphical information into a first reduced subset of textual and graphical information based at least in part upon a first characteristic pertaining to or for the client, the first reduced subset smaller than the set of textual and graphical information (see para [0033]; “faceted data is displayed for navigation and access using a file system oriented approach…. such file navigation and access are performed via a unique faceted multi-tree widget”, see also para [0031; “The contents of a data.* file will be the subset of data from the input data corpus that satisfy the facet-value constraints imposed by the faceted query represented by the path of the file”, Note; a faceted multi-tree widget is a form of hierarchical or nested filtering network); filtering, via the first on-screen widget or a second on-screen widget in the user interface or the hierarchical or nested filtering network, the first reduced subset into the textual and graphical information based at least in part upon a second characteristic pertaining to or for the client, the second characteristic different from the first characteristic (see para [0039]; “using the proposed faceted multi-tree widget might start by saying Color>Black>ProductName. This will list all products that have color as black. This could be a mobile phone, or a television set. He can then further choose to drill down by expanding nodes like Category>Mobile Phone, etc” Note; drilling down by expanding nodes like Category > Mobile Phone" corresponding to hierarchical, nested filtering network, and narrowing down based on multiple, different characteristics (e.g., first color, then category) ). Accordingly, it would have been obvious to one of ordinary skill in the art at the time of invention of the claimed invention to modify the general a method for intelligent context-based personalized beauty product recommendation of Lindgren in view of the use of method for measuring spectra and other optical characteristics of skin for predicting products to match the measured skin of Jung et al. and navigation of faceted data of Ramamurthy et al. in order to facilitate a non-technical business user to navigate and select data from data sources are becoming increasingly important (see para [0033]).
However, the combination of Lindgren, Jung et al. and Ramamurthy et al. as a whole does not teach and rendering the textual and graphical information on the user interface that has been at least twice reduced from the set of textual and graphical information, the textual and graphical information smaller than the first subset of textual and graphical information.
In the same field of endeavor, Chong et al. and rendering the textual and graphical information on the user interface that has been at least twice reduced from the set of textual and graphical information, the textual and graphical information smaller than the first subset of textual and graphical information (see para [0031]; “a color selection visual graphical user interface 200 which may be displayed on a display 22 of the device 10. The visual interface 200 includes first and second color display regions 202 and 204”, see also para [0148]-[0149]; “the colors of a color group shown in color group region 202 will be limited to those satisfying the desired color depth range…. The colors represented in the color group display region 202 of interface 200 will be limited only to colors within the selected color group that meet the desired harmony level….the colors of the selected color group currently displayed in region 202 will be limited to the colors that meet the specified color emotion levels”, see also para [0132]; “in order to limit the color group to colors that have a high likely hood of being harmonious, …. the "darker" colors are dropped from the color group display region 202 in harmony mode. …. selecting the harmony button from options 210 results in the interface 200 being modified so that only the top four rows of the selected color group is displayed”, and para [0153]; “then modifies the color group display region 202 to identify as a suggested color group subset 302 the color elements 211 that meet the criteria. The user can then select color elements 211 from the suggested color group subset 211 to add… such "rejected" color elements that are not part of the suggested color group subset 302 could be completely removed from the color group display region 202”). Accordingly, it would have been obvious to one of ordinary skill in the art at the time of invention of the claimed invention to modify the general a method for intelligent context-based personalized beauty product recommendation of Lindgren and the use of method for measuring spectra and other optical characteristics of skin for predicting products to match the measured skin of Jung et al. in view of navigation of faceted data of Ramamurthy et al. and further in view of Color selection and display methods and devices in which colors can be displayed according to color harmony of Chong et al. in order to assist consumers or other users in reaching confident and satisfying color selection choices (see para [0147]).
Regarding claim 2, the rejection of claim 1 is incorporated herein.
Lindgren in the combination further teach further comprising: identifying the plurality of first inputs, wherein the plurality of first inputs comprises one or more first actual images representing the at least one subject area on a body of a client (see para [0009]; “perform body part detection on the photograph to allow the photo to be marked or recognized as some body part other than a face that contains visible skin; detect skin and non-skin areas in the photograph after either detection or labeling of the skin-containing photograph”, see also para [0078]; “The various images may include (non-limiting) photographs of the consumer's face and/or body (e.g., individual limbs, whole body, face only photos, hair only photos, etc.)”).
Regarding claim 3, the rejection of claim 1 is incorporated herein.
Jung et al in the combination further teach further comprising at least one of: testing the trained feature learning model using at least a testing subset of the multiple subsets that is transmitted as a testing data stream to the trained feature learning model based at least in part upon a testing objective (see para [0116]; “test the Learning Model and determine if the results are desirable (696, 698), and optionally collect and test Field Data in the same manner as the training data (699, 697, and 695)”); or capturing the at least some of the plurality of first inputs during a scan session that invokes the image capturing device of a mobile computing device to generate the at least some of the plurality of first inputs (see Fig. 7E-7K disclose capturing many images, para [0139]; “Preferably an indicator to start a zone 1 scan is displayed proximal to one of buttons 9 (e.g., lower center button 9), and the start of the scan is initiated by pressing of the appropriate button 9”).
Regarding claim 4, the rejection of claim 3 is incorporated herein.
Lindgren in the combination further teach further comprising: predicting, by an artificial intelligence model, a list of body care products or services based at least in part upon the body complexion index or value (see Lindgren para [0060]; “recommendation engine 160 may utilize one or more neural networks 163 in order to predict recommended products for the system user”).
Regarding claim 5, the rejection of claim 3 is incorporated herein.
Lindgren in the combination further teach wherein the at least one subject area on the body of the client comprises a skin area on a skin of the client, a nail area on a nail of the client, or a hair area in the hairs of the client (see Lindgren para [0042]; “The system may receive a photograph of an individual, detect non-facial skin areas and hair boundaries, and analyze the non-facial skin areas and hair to make predictions about the tone, type and concern related to those skin areas and hair”); the body complexion index or value is predicted by the trained feature learning model and comprises a color tone or value for the at least one subject area (see para [0071]; “As a result of the above process, one or more classification networks 206 may be trained and configured to classify input images 210 such as for skin-tone analysis, skin concern analysis and various other skin related tasks”), the body complexion characteristic comprises a color, a size, a dimension, a shape, or a condition of the at least one subject area (see para [0105]; “A user with a skin condition or skin damage can upload an image along with the name of their skin condition/damage which can be used by the system to train underlying models to detect secondary features associated with an uploaded image”), and the one or more objects comprise at least one of a hair, a freckle, a wrinkle, a mole, a differently colored spot, a pre-malignant body tissue growth, a malignant body tissue growth, or a capillary (see para [0055]; “a user-uploaded photograph, concern-based input data (e.g., fine lines and wrinkles, loss of skin elasticity, thinning hair, damaged hair, sun damage to skin, etc.), preference-based input, and goal-based input data (e.g., radiant and youthful, thermal protection hair, volumize hair, etc.)”).
Claims 6-9 are rejected under 35 U.S.C. 103 as being unpatentable over
Lindgren and Jung et al. in view of as Ramamurthy et al. and Chong et al. applied in claim 1 above, and further in view of Peyrelevade et al. (US 20030065578 A1).
Regarding claim 6, the rejection of claim 1 is incorporated herein.
Lindgren in the combination further teach further comprising: identifying a plurality of second inputs, wherein the plurality of second inputs comprises one or more actual second images respectively representing subject areas on one or more bodies of one or more clients (see Lindgren para [0104]; “Skin analysis engine 1300 detects the body part(s) 1404 of the user captured in the uploaded image. This step may be facilitated with trained classifier network which has been trained on a dataset comprising labeled images of body parts. The training dataset may comprise images from a diverse spectrum of individuals of various ethnic backgrounds and gender”) and one or more synthetic images (see Lindgren para [0054]; Furthermore, image portal may create a copy of the user-uploaded or cropped image so that one or more image processing and analysis tasks may be performed on the copy of the image. Image processing and analysis may be performed by a skin analysis engine 130. User-uploaded photograph(s) and cropped images may be added to the user's individual profile 120 and stored in user database(s) 140”). However, the combination of Lindgren, Jung et al. Ramamurthy et al. and Chong et al. as a whole does not expressly teach partition the plurality of second inputs into multiple subsets, the multiple subsets comprising a training subset that comprises the one or more actual images and the one or more synthetic images.
In the same field of endeavor, Peyrelevade et al. teach partition the plurality of second inputs into multiple subsets, the multiple subsets comprising a training subset that comprises the one or more actual images and the one or more synthetic images (see para [0139]; “If the image is representative of an external body condition, the image could be either an actual image showing the condition or an image including symbolizations of the condition, for example. The image may be an actual or a simulated image. Simulated images may include wholly or partially generated computer images, images based on existing images, and images based on stored features of a subject”). Accordingly, it would have been obvious to one of ordinary skill in the art at the time of invention of the claimed invention to modify the general a method for intelligent context-based personalized beauty product recommendation of Lindgren and the use of method for measuring spectra and other optical characteristics of skin for predicting products to match the measured skin of Jung et al. in view of navigation of faceted data of Ramamurthy et al. and further in view of Color selection and display methods and devices in which colors can be displayed according to color harmony of Chong et al. and simulate application of selected beauty products and recommended products on a facial image of Peyrelevade et al. in order to change the color of the related recommended product before or after the visual simulation (see para [0139]).
Regarding claim 7, the rejection of claim 6 is incorporated herein.
Peyrelevade et al. in the combination further teach further comprising: identifying at least one actual image from the one or more actual images (see para [0139]; “If the image is representative of an external body condition, the image could be either an actual image showing the condition or an image including symbolizations of the condition, for example. The image may be an actual or a simulated image. Simulated images may include wholly or partially generated computer images, images based on existing images, and images based on stored features of a subject”); and transforming the at least one actual image into a synthetic image of the one or more synthetic images at least by applying a visibly imperceptible change to the at least one actual image (see para [0105]; “Simulation on facial images may include modifying (or creating) a photograph of a model, a graphical representation of a model, a user's photograph, a graphical representation of a user, a 3-D projection of a model, a 3-D projection of a user, and/or any other representation of a user or a model. Regardless of the format, simulation may be performed on any selected portion and/or all of the facial image”).
Regarding claim 8, the rejection of claim 6 is incorporated herein.
Lindgren in the combination further teach further comprising: training, at a separate computing system, a feature learning model into the trained feature learning model using at least the training subset that is transmitted as a training data stream to the feature learning model (see para [0065]; “The various modules and networks operating within skin analysis engine 200 may comprise one or more machine and deep learning algorithms. These algorithms may be trained using a subset of the information located in user database(s) 140, product database(s) 150, and external data that may be obtained. The training of these algorithms may be conducted via supervised learning, unsupervised learning, or some combination of the two”).
In the same field of endeavor, Peyrelevade et al. in the combination teach and comprises the one or more actual images and the one or more synthetic images (see para [0069]; “FIG. 9 is an overview of an exemplary AI engine 540 based on neural networks consistent with one aspect of the invention. AI engine 540 may be trained based on input 910. Input 910 may include any information, including product information 710, expert advice 712, user profile 714, and/or data based on sensory perceptions”, see also para [0139]; “the term "image" may include either a visually perceptible image or electronic image data that may be either used to construct a visually perceptible image or to derive information about the subject…. If the image is representative of an external body condition, the image could be either an actual image showing the condition or an image including symbolizations of the condition, for example. The image may be an actual or a simulated image. Simulated images may include wholly or partially generated computer images, images based on existing images, and images based on stored features of a subject”, Note: the AI engines trained using a visually perceptible images which include an actual and simulated image).
Regarding claim 9, the rejection of claim 8 is incorporated herein.
Lindgren in the combination further teach wherein training the feature leaning model comprises: training a plurality of model parameters of the feature learning model into a plurality of trained model parameters; training a plurality of hyperparameters into a plurality of trained hyperparameters for the feature learning model; and populating the plurality of trained parameters and the plurality of trained hyperparameters for the feature learning model (see para [0074]; “Parametric optimizer 1050 may be configured to allow for hyperparameter tuning and neuron weight adjustment. Hyperparameters that may be configured and adjusted between training sessions include, but are not limited to, the number of hidden layers and units, dropout (e.g., to avoid overfitting the model thus increasing the generalization power), network weight initialization, the activation function (e.g., SoftMax, sigmoid, rectifier activation function, etc.), the learning rate (i.e., how quickly the network updates its parameters), momentum, number of epochs, batch size and the like. Parametric optimizer 1050 may utilize various methods to discover and tune hyperparameters such as manual search, grid search, random search, and Bayesian optimization. Beauty product RNN 1000 optimization and training may be conducted numerous times until model performance and accuracy have met a predetermined criteria for model performance”, see also para [0060]; “During the training process neural network 163 may be optimized by tuning the hyperparameters for each layer of the network, as well as using backpropagation and gradient descent techniques to minimize the error between layers and improve network predictions”). Accordingly, it would have been obvious to one of ordinary skill in the art at the time of invention of the claimed invention to modify the general a method for intelligent context-based personalized beauty product recommendation of Lindgren and the use of method for measuring spectra and other optical characteristics of skin for predicting products to match the measured skin of Jung et al. in view of navigation of faceted data of Ramamurthy et al. and further in view of Color selection and display methods and devices in which colors can be displayed according to color harmony of Chong et al. and simulate application of selected beauty products and recommended products on a facial image of Peyrelevade et al. in order to minimize the error between layers and improve network predictions (see para [0074]).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over
Lindgren and Jung et al. in view of as Ramamurthy et al. and Chong et al. applied in claim 1 above, and further in view of Theodorakopoul et al. (US 20180137417 A1).
Regarding claim 10, the rejection of claim 1 is incorporated herein.
Lindgren in the combination further teach further comprising: in response to an input received from a client indicating a characteristic of the client (see para [0055]; “fact-based input data (e.g., hair type, hair porosity, hair texture, skin type, dark spots, acne, hyperpigmentation, allergies, beauty routine, beauty product preference(s), etc.), a user-uploaded photograph, concern-based input data (e.g., fine lines and wrinkles, loss of skin elasticity, thinning hair, damaged hair, sun damage to skin, etc.), preference-based input, and goal-based input data (e.g., radiant and youthful, thermal protection hair, volumize hair, etc.)”). However, the combination of Lindgren, Jung et al. Ramamurthy et al. and Chong et al. as a whole does not expressly teach suppressing a pertinent portion of code or libraries for a trained feature learning model from further processing.
In the same field of endeavor, Theodorakopoul et al. teaches suppressing a pertinent portion of code or libraries for a trained feature learning model from further processing (see Lindgren para [0074]; “The exemplary module activates/deactivates a sub-set of filtering kernels, groups of kernels, or groups of full connected neurons, during the inference phase, on-the-fly for every input image depending on the input image content and the learned activation rules”). Accordingly, it would have been obvious to one of ordinary skill in the art at the time of invention of the claimed invention to modify the general a method for intelligent context-based personalized beauty product recommendation of Lindgren and the use of method for measuring spectra and other optical characteristics of skin for predicting products to match the measured skin of Jung et al. in view of navigation of faceted data of Ramamurthy et al. and further in view of Color selection and display methods and devices in which colors can be displayed according to color harmony of Chong et al. and simulate application of selected beauty products and recommended products on a facial image of Theodorakopoul et al. in order to use as few computing resources as possible (see para [0074]).
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.
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/WINTA GEBRESLASSIE/ Examiner, Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677
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