DETAILED ACTION
This action is in response to a filing filed on January 17th, 2025. Claims 1-20 have been examined in this application.
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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Step 1: Claims 1-10 is/are drawn to computer readable media (i.e., a manufacture), and claims 11-20 is/are drawn to system (i.e., a manufacture). (Step 1: YES).
Step 2A - Prong One: In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether it/they recite(s) a judicial exception.
Claim 1: One or more non-transitory computer readable media encoded with instructions that, when executed by one or more processors of a computing system, cause the computing system to:
determine a size of an item based on extracted size data corresponding to the item;
select a comparison item;
and generate an interactive representation of the item and the comparison item using the size of the item and size data of the comparison item to render the interactive representation.
(Examiner notes: The underlined claim terms above are interpreted as additional elements beyond the abstract idea and are further analyzed under Step 2A - Prong Two)
Under their broadest reasonable interpretation, the independent claims is/are directed to the abstract idea of evaluating and comparing item-size information and presenting the comparison. The limitations of determining a size of an item based on extracted size data, selecting a comparison item, and generating a representation of the item and comparison item using size data merely describe collecting item-size information, comparing the size information of two items, and displaying the comparison. These are mental processes because a person could perform the same evaluation by observing or obtaining dimensions of an item, choosing a known comparison object, comparing their relative sizes, and presenting or sketching the comparison. Additionally, when viewed in the product-selection context described by the application, the claim is directed to assisting a user in evaluating a product for purchase, which is a commercial interaction and therefore a certain method of organizing human activity. The Examiner notes that although the claim limitations are summarized, the analysis regarding subject matter eligibility considers the entirety of the claim and all of the claim elements individually, as a whole, and in ordered combination.
The dependent claims 2-10 and 12-20 do not add subject matter that meaningfully limits the abstract idea recited in independent claims 1 and 11. Rather, the dependent claims merely add further data collection, data extraction, size/scale analysis, image analysis, mathematical/geometric processing, database selection, and presentation or manipulation of visual comparison information. These additional limitations remain directed to collecting information, analyzing and comparing information, and presenting the results of the comparison, which fall within the mental-process grouping and, in the product-selection context, certain methods of organizing human activity. Claims 2-4, 12-14, and 20 recite additional ways of obtaining, extracting, or using item-size information, including locating standardized components to provide scale, extracting size data from text or images, determining comparison-item size from comparison-item size data, using natural language processors to search textual descriptions, using a pattern-recognition model to locate a scale object, and selecting comparison items from a database of known-size items accessible through a network. These limitations merely specify sources or techniques for collecting and organizing size-related information and do not improve the functioning of a computer or image-processing system itself. The use of textual descriptions, images, databases, natural-language processing, or pattern recognition is recited at a high level of generality and serves only to obtain or organize information used in the abstract size-comparison process. Claims 5-6 and 15-16 recite that the item size may include dimensions and range-of-motion information for a movable portion and that the interactive representation may allow a user to move or actuate a portion of the item or comparison item. These limitations merely refine the type of information being compared or the manner in which the comparison is visually manipulated. Determining dimensions, considering movement of an object portion, and allowing a user to move or actuate a displayed representation are part of modeling, evaluating, and presenting object information, and do not provide a specific technological improvement to computer operation, rendering technology, or user-interface functionality. Claims 7-9 and 17-19 recite generating a three-dimensional rendering from a plurality of images, extracting edges, determining dimensions of extracted edges, generating a fully dimensioned model, and combining images using pose estimation. These limitations are directed to mathematical and geometric analysis of image data, including identifying edges, measuring dimensions, estimating pose, and producing a model or rendering for display. The claims do not recite any particular improved algorithm, sensor arrangement, rendering architecture, or technical solution to a computer-vision problem. Instead, these limitations merely apply generic image-processing and mathematical/geometric operations to the abstract idea of comparing item sizes and visually presenting the comparison. Claim 10 and 20 recite selecting a comparison item from a database of known-size items based on the determined size of the item, with claim 20 further reciting that the database is accessible through a network/data interface. These limitations merely organize known-size reference information in a database and retrieve comparison information for use in the size-comparison process. Using a generic database, data interface, and network to access comparison items does not integrate the abstract idea into a practical application or provide an improvement to database or network technology. Taken together, the additional limitations of claims 2-10 and 12-20 merely refine the abstract idea of independent claims 1 and 11 by adding further data gathering, preprocessing, mathematical/geometric analysis, database retrieval, and presentation of visual comparison information. The recited computer, processor, memory, database, network, image-processing, natural-language-processing, and pattern-recognition components are used as generic tools to implement the abstract process rather than to improve the underlying technology. Accordingly, the dependent claims are directed to the same abstract idea as the independent claims and do not add significantly more than the judicial exception under 35 U.S.C. § 101.
.
Independent claim(s) 11 recite/describe nearly identical steps (and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and this/these claim(s) is/are therefore determined to recite an abstract idea under the same analysis.
As such, the Examiner concludes that claims 1 recites an abstract idea (Step 2A – Prong One: YES).
Step 2A - Prong Two: In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the exception into a practical application of that exception. An “addition element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception.
The requirement to execute the claimed steps/functions using a computer, processor, memory, database, network, image-processing, natural-language-processing, and pattern-recognition components, etc. (Claims 1 and 11) is/are equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer.
Similarly, the limitations of using computer, processor, memory, database, network, image-processing, natural-language-processing, and pattern-recognition components, etc. (Claims 1 and 11, and dependent claims 2-10, and 12-20) are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
Further, the additional limitations beyond the abstract idea identified above, serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. Specifically, it/they serve(s) to limit the application of the abstract idea to computerized environments (e.g., obtain, generate, vectorize, produce, display, etc. steps performed by computer, processor, memory, database, network, image-processing, natural-language-processing, and pattern-recognition components, etc.). This reasoning was demonstrated in Intellectual Ventures I LLC v. Capital One Bank (Fed. Cir. 2015), where the court determined "an abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, such as the Internet [or] a computer"). This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(h)).
The recited additional element(s), the steps of including determining a size of an item based on extracted size data, selecting a comparison item, generating an interactive representation of the item and comparison item, rendering the representation using size data, extracting size data from textual descriptions or images, locating standardized components or scale objects, selecting comparison items from a database, generating three-dimensional renderings, extracting edges, determining dimensions from edge measurements, combining images using pose estimation, and displaying or manipulating the resulting representation merely constitute data gathering, intermediate data analysis, mathematical/geometric processing, and output presentation. For example, obtaining or extracting item-size data from text, images, product descriptions, metadata, databases, or known-size reference objects is pre-solution data gathering because it merely collects information to be used in the size-comparison analysis. Similarly, locating standardized components, identifying scale objects, extracting image edges, determining dimensions, using pose estimation, generating a three-dimensional rendering, and selecting a comparison item from a known-size database are intermediate processing steps that organize, analyze, or mathematically/geometrically transform information for purposes of the abstract comparison. Further, generating, rendering, displaying, moving, or actuating the interactive representation is post-solution activity because it merely presents or refines the results of the size comparison to the user. The use of a processor, memory, computing system, user device, database, data interface, network, natural language processor, pattern-recognition model, and image-processing functionality is recited at a high level of generality and performs well-understood, routine, and conventional computer functions of receiving, storing, retrieving, processing, analyzing, and displaying data. The claims do not recite a particular improvement to computer functionality, image-processing technology, database operation, networking, rendering systems, or user-interface technology. Rather, the recited computer components are used merely as tools to implement the abstract idea of comparing item-size information and presenting the comparison. Accordingly, these additional elements do not integrate the abstract idea into a practical application and instead represent insignificant extra-solution activity. Accordingly, the additional elements of independent claims the abstract idea into a practical application and constitute insignificant extra-solution activity and fail to integrate (Independent Claims 1 and 11), additionally and/or alternatively simply append insignificant extra-solution activity to the judicial exception, (e.g., mere pre-solution activity, such as data gathering, in conjunction with an abstract idea). This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application. (See MPEP 2106.05(g)).
Dependent claims 2-10 and 12-20 fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims is/are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e., they are part of the abstract idea recited in each respective claim).
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B: In step 2B, the claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for an "inventive concept." An "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception itself. Alice Corp., 134 S. Ct. at 2355, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966).
As discussed above in “Step 2A – Prong 2”, the identified additional elements in independent Claims 1 and 11, and dependent claims 2-10 and 12-20 are equivalent to adding the words “apply it” on a generic computer, and/or generally link the use of the judicial exception to a particular technological environment or field of use. Therefore, the claims as a whole do not amount to significantly more than the judicial exception itself.
The recited additional element(s) of outputting a plurality of tokens, presenting the communication record in a standardized form and displaying on a graphical user interface (Independent Claims 1 and 11), additionally and/or alternatively simply append insignificant extra-solution activity to the judicial exception, (e.g., mere pre-solution activity, such as data gathering, in conjunction with an abstract idea), i.e. these steps merely perform the steps of steps of extracting item-size data from text, images, product descriptions, metadata, databases, or known-size reference objects is pre-solution data gathering because it merely collects information to be used in the size-comparison analysis. Similarly, locating standardized components, identifying scale objects, extracting image edges, determining dimensions, using pose estimation, generating a three-dimensional rendering, and selecting a comparison item from a known-size database are intermediate processing steps that organize, analyze, or mathematically/geometrically transform information for purposes of the abstract comparison. These steps are performed before, during, or after the abstract idea to presents or refines the results of the size comparison to the user, which is similar to “Receiving or transmitting data over a network, e.g., using the Internet to gather data”, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), “Storing and retrieving information in memory”, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; “Presenting offers to potential customers and gathering statistics generated based on the testing about how potential customers responded to the offers; the statistics are then used to calculate an optimized price”, OIP Technologies, 788 F.3d at 1363, 115 USPQ2d at 1092-93, Determining an estimated outcome and setting a price, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93, is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here) (See MPEP 2106.05(d) (II)).
This conclusion is based on a factual determination. Applicant’s own disclosure at paragraph [0020] acknowledges that “ser may interact with a user interface described herein by viewing the interface, and by providing input through one or more input devices such as, but not limited to, one or more touchscreens, keyboards, mice, AR/VR devices, or stylus devices. Examples of items which may be visually represented in accordance with examples described herein generally may include any item that a user may be interested in viewing, inspecting, purchasing, and/or learning about. Examples of items include products, clothing, cars, sporting equipment, furniture, medical devices, home goods, or vehicles.” This additional element therefore do not ensure the claim amounts to significantly more than the abstract idea.
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer or/and append the abstract idea with insignificant extra solution activity associated with the implementation of the judicial exception, (e.g., mere data gathering, post-solution activity) and/or simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception.
The dependent claims 2-10, and 12-20 fail to include any additional elements. In other words, each of the limitations/elements recited in respective independent claims is/are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e., they are part of the abstract idea recited in each respective claim).
Specifically, claims 2-10 and 12-20 likewise fail to provide an inventive concept sufficient to transform the abstract idea into patent-eligible subject matter. Claims 2-4, 12-14, and 20 add limitations directed to obtaining, extracting, organizing, or using size-related information, including locating standardized components to provide scale, extracting size data from textual descriptions or images, determining a size of a comparison item from comparison-item size data, using natural language processors to search textual descriptions, using a pattern-recognition model to locate a scale object, and selecting comparison items from a database of known-size items accessible through a network. These additional elements merely refine the manner in which information is collected, identified, and organized for the underlying size-comparison analysis. The use of textual descriptions, images, natural language processors, pattern-recognition models, databases, data interfaces, and networks is recited at a high level of generality and does not amount to an improvement in computer functionality, database operation, image-processing technology, or natural-language-processing technology. Rather, these elements amount to using generic computer tools to gather and organize data for the abstract idea. Claims 5-6 and 15-16 add limitations directed to including dimensions and range-of-motion information for a movable portion of an item and allowing a user to move or actuate a portion of the item or comparison item in an interactive representation. These limitations merely add additional object characteristics to be evaluated or provide conventional user manipulation of displayed information. The claims do not recite any particular unconventional user-interface mechanism, rendering architecture, physical actuator, sensor arrangement, or technical improvement for modeling motion. Instead, these limitations amount to representing, manipulating, or presenting object information using generic computer functionality. Claims 7-9 and 17-19 add limitations directed to generating a three-dimensional rendering from a plurality of images, extracting edges from images, determining dimensions of extracted edges, generating a fully dimensioned model, and combining images using pose estimation. These limitations are directed to image analysis, mathematical/geometric processing, and model generation used to support the abstract size-comparison process. The claims do not recite a specific improved computer-vision algorithm, improved pose-estimation technique, improved 3D reconstruction process, or unconventional technical implementation. Rather, the limitations are recited functionally and at a high level of generality, and therefore amount to generic image-processing and mathematical/geometric analysis performed on a generic computing system. Claim 10 and claim 20 further recite selecting a comparison item from a database of known-size items based on the determined size of the item, with claim 20 additionally reciting that the database is accessible through a data interface via a network. These limitations merely use conventional database retrieval and network access to obtain comparison information for use in the abstract size-comparison process. The use of a database, data interface, and network does not provide an inventive concept because these elements perform their ordinary functions of storing, retrieving, and transmitting information. None of these limitations meaningfully limit the abstract idea or integrate it into a practical application. Accordingly, the additional limitations of the dependent claims do not amount to significantly more than the abstract idea and therefore fail to provide an inventive concept under Step 2B. Because these elements do not solve a specific technical problem or offer a technical improvement over existing systems, they are viewed as merely "applying" the abstract idea on a generic computer, thus failing to provide a practical application that would render the claims patent-eligible, and therefore do not add an inventive concept sufficient to transform the abstract idea into patent-eligible subject matter.
When viewed as an ordered combination, the additional elements of claims 2-10 and 12-20 merely instruct to implement the abstract idea using generic computer components to collect, store, represent, and display information. The claims do not recite any unconventional arrangement of elements, nor do they effect an improvement to computer functionality or another technical field and therefore fail to integrate the abstract concept into a practical application and it is recited at a high level of generality and does not integrate the judicial exception into a practical application.
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Therefore, claims 1-20 are not eligible subject matter under 35 USC 101.
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 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.
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 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:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. 20160364779 (“Kim”) in view of U.S. Pub. 20240404124 (“Wiesel”).
As per claims 1 and 11, Kim discloses, non-transitory computer readable media encoded with instructions that, when executed by one or more processors of a computing system, cause the computing system to (Examiner interprets that Kim’s computing device, auto-comparator module, object selector, image scale/rotator, positioner, display module, processor, memory, and storage collectively correspond to the claimed non-transitory computer-readable media, computing system, processor, memory, and executable instructions) (“FIG. 4 is a block diagram of internal and external components of a computing device 400, which is representative of the computing device of FIG. 1, in accordance with an embodiment of the present invention. It should be appreciated that FIG. 4 provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments may be implemented. In general, the components illustrated in FIG. 4 are representative of any electronic device capable of executing machine-readable program instructions. Examples of computer systems, environments, and/or configurations that may be represented by the components illustrated in FIG. 4 include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, laptop computer systems, tablet computer systems, cellular telephones (e.g., smart phones), multiprocessor systems, microprocessor-based systems, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments that include any of the above systems or devices.”) (0033-0035, 0015-0018, 0041-0048): determine a size of an item based on extracted size data corresponding to the item (Examiner interprets that claimed “extracted size data” reads on Kim’s provided product dimensions and metadata mined from manuals or other sources. Kim uses such product dimensions/metadata to determine the relative size/scale of the product image) (“Image scale and rotator 126 allows the scaling of a comparison image in a meaningful way to a target image (i.e., a product for purchase). Image scale and rotator 126 may use provided product dimensions and/or may mine metadata from product manuals or other sources to determine a meaningful relative comparison, and may access reference objects database 132. For example, a user viewing a product, such as a television, may drag the image of the television to an image of the user's living room, and image scale and rotator 126 automatically resizes the image of the television to the scale of the image of the user's living room, so that the user may visualize how the real-world size of the television fits within the dimensions of the user's living room.” and “FIG. 3C depicts an image of environment 320 (e.g., a user's living room) and an image of object 322 (e.g., a couch), which the user is contemplating purchasing. The image of environment 320 has the correctly scaled dimensions (i.e., height, width, and depth) of the user's living room, and the real-world dimensions associated with object 322 are also known. Auto comparator module 122 can scale and position the image of object 322 to fit the image of the user's living room (i.e., environment 320), so that the user is able to visualize how object 322 will fit (and if it will fit) in the user's living room, before purchasing object 322.”) (0020-0024 and 0032):
select a comparison item (Examiner interpretation that a reference objects database storing known-size objects such as a dollar bill, human of a particular height and size, tablet device, or smartphone. Kim further teaches an object selector that chooses one or more objects or environments for product comparison based on contextual relevance, familiarity, comparable size/color/volume, and usage scenario i.e. selected standardized object, personal object, previously purchased/viewed item, or environment corresponds to the claimed “comparison item.”) (“Reference objects database 132 stores a standardized collection of objects with a relative cultural and cognitive recognition to a user. For example, reference objects may include an image of a dollar bill, an image of a human of a particular height and size, and an image of a tablet device or smart phone of a known size. Reference objects database 132 may be modified to include objects which have a cultural significance to the user. For example, in the United States, an image of a dollar may be stored, whereas in Europe, an image of a euro may be stored. In this exemplary embodiment, reference objects database 132 is stored locally on computing device 120. In other embodiments, reference objects database 132 may be stored remotely, such as on a server, and accessed and/or downloaded by computing device 120 … Object selector 124 chooses one or more objects or environments for a product comparison. In this exemplary embodiment, object selector 124 selects objects or environments which have a contextual relevance to a user. Object selector 124 determines which objects or environments may have a contextual relevance to a user by factoring the following criteria: the familiarity of the object or environment to the user (e.g., a room in the user's house or the cellphone of a user), the size, color, and/or volume of the object or environment is comparable to the product, and the usage scenarios between the product and object or environment (e.g., comparison between the size of a pan contemplated for purchase and a user's stovetop)”) (0016-0019, 0025);
and generate an interactive representation of the item and the comparison item using the size of the item and size data of the comparison item to render the interactive representation (Examiner notes that the underlined limitation is disclosed by another prior art. Examiner interprets that Kim’s GUI-based ability to move, scale, drag, reposition, and display the comparison image relative to the product image corresponds to generating an interactive representation of the item and comparison item.) (“Display module 130 communicates with auto comparator module 122, to invoke various object comparisons when a user views a product on one or more of websites 108A-N. Display module 130 can include a graphical user interface (GUI) (not depicted in FIG. 1), which allows a user to move, scale, and/or reposition a retrieved image near a product or offering, to make a contextual visual comparison. In some embodiments, display module 130 can be integrated with auto comparator module 122 …”) (0017-0021, 0026-0027).
Kim specifically doesn’t disclose, using the size of the item and size data of the comparison item to render the interactive representation, however Wiesel discloses, using the size of the item and size data of the comparison item to render the interactive representation (Examiner interprets that the processing product images by incorporating the product image(s) into an anchor image to generate a composite anchor image, where the image processing includes using product dimension information to scale the product images as necessary to match the scale of the anchor image. Wiesel further teaches that the product dimension information may be obtained from product dimension data maintained with the product or derived from numeric or alphanumeric product size data i.e. Wiesel’s product dimension information corresponds to the claimed “size of the item” and “size data,” and Wiesel’s composite anchor image corresponds to rendering a visual/interactive representation using such size data.) (“product dimension information is obtained from product dimension data maintained in association with said one or more products or is derived from numeric or alphanumeric product size data maintained in association with said one or more products … one or more product images are retrieved from a single source of all of said one or more products. In some embodiments, said composite anchor image depicts only said one or more products and a background that is blank or includes a background image. In some embodiments, said composite anchor image depicts said one or more products being worn by an anthropomorphic entity. In some embodiments, said anthropomorphic entity comprises an image of a human that may be a user image received from a user system or device. In some embodiments, said anchor image includes a background image and said human image or said background image are scaled to match each other … process for combining user selectable product images and facilitating visualization-assisted coordinated product acquisition. One or more products having one or more associated product images may be identified. The one or more product images may be image processed by incorporating the image(s) into an anchor image to generate a composite anchor image that depicts the one or more products according to an intended manner of use thereof. The image processing may include using product dimension information to scale the one or more product images as necessary to match a scale of the anchor image. The composite anchor image may be caused to be displayed in association with one or more user interface elements, the user interface elements being operable to initiate a coordinated product transaction involving the one or more products. Responsive to the one or more user interface elements being activated, the coordinated product transaction may be initiated”) (0494-0497).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for determine a size of an item based on extracted size data corresponding to the item; select a comparison item; and generate an interactive representation of the item and the comparison item, as taught by Kim, using the size of the item and size data of the comparison item to render the interactive representation, as taught by Wiesel for the purpose to apply product-dimension-based scaling to visual comparison system to improve the accuracy, realism, and usefulness of the rendered product comparison, thereby helping a user better assess product size before purchase.
As per claims 2, Kim discloses, wherein the instructions further cause the computing system to determine the size of the item by locating standardized components of the item to provide a scale to an image of the item (Examiner interprets that determining the size of the item by using standardized/known-size reference objects to provide scale to an image. Kim teaches that reference objects database 132 stores known-size reference objects, including a dollar bill, human of particular height and size, tablet device, or smartphone i.e. “standardized components” are interpreted broadly as standardized or known-size objects/features used to provide scale to an image. Kim’s known-size reference objects provide scale information for determining or presenting item size) (“Image scale and rotator 126 allows the scaling of a comparison image in a meaningful way to a target image (i.e., a product for purchase). Image scale and rotator 126 may use provided product dimensions and/or may mine metadata from product manuals or other sources to determine a meaningful relative comparison, and may access reference objects database 132. For example, a user viewing a product, such as a television, may drag the image of the television to an image of the user's living room, and image scale and rotator 126 automatically resizes the image of the television to the scale of the image of the user's living room, so that the user may visualize how the real-world size of the television fits within the dimensions of the user's living room.”) (0020, 0030).
As per claims 3, Kim discloses, wherein the extracted size data is extracted from one or more of textual description of the item and an image of the item (Examiner interprets that the extracted size data may be extracted from textual description or other textual/item information, because Kim teaches using provided product dimensions and mining metadata from product manuals or other sources to determine a meaningful relative comparison i.e. Product dimensions, product metadata, product manuals, and web-catalog textual product information are all forms of extracted size data from textual product information) (“Image scale and rotator 126 allows the scaling of a comparison image in a meaningful way to a target image (i.e., a product for purchase). Image scale and rotator 126 may use provided product dimensions and/or may mine metadata from product manuals or other sources to determine a meaningful relative comparison, and may access reference objects database 132. For example, a user viewing a product, such as a television, may drag the image of the television to an image of the user's living room, and image scale and rotator 126 automatically resizes the image of the television to the scale of the image of the user's living room, so that the user may visualize how the real-world size of the television fits within the dimensions of the user's living room.”) (0020).
As per claims 4, Kim discloses, wherein the instructions further cause the computing system to determine a size of the comparison item using the size data of the comparison item (Examiner interprets that determining a size of the comparison item using size data of the comparison item. Kim teaches a reference objects database that stores known-size objects and teaches selecting and automatically scaling standardized objects such as money, a smartphone, and a key for comparison to a wallet. Kim also teaches scaling an object into a living-room environment having correctly scaled dimensions i.e. Kim’s known dimensions of standardized reference objects or environments correspond to the claimed “size data of the comparison item.”) (“FIG. 3B depicts another embodiment of auto comparator module 122. In this example, wallet 304 is an image a user is viewing for a contemplated purchase. As in FIG. 3A, the user, for example, wants to purchase a new wallet from a shopping website which is at least large enough to fit the user's smart phone, keys, and money. In this embodiment, auto comparator module 122 accesses reference objects database 132 and selects, and automatically scales, standardized objects of money 306, smart phone 308, and key 310, for the user to make a visual comparison … FIG. 3C depicts an image of environment 320 (e.g., a user's living room) and an image of object 322 (e.g., a couch), which the user is contemplating purchasing. The image of environment 320 has the correctly scaled dimensions (i.e., height, width, and depth) of the user's living room, and the real-world dimensions associated with object 322 are also known. Auto comparator module 122 can scale and position the image of object 322 to fit the image of the user's living room (i.e., environment 320), so that the user is able to visualize how object 322 will fit (and if it will fit) in the user's living room, before purchasing object 322”) (0030-0032, 0016).
As per claims 5 and 15, Kim specifically doesn’t disclose, wherein the size of the item includes one or more dimensions of the item and a range of motion of a movable portion of the item, however Wiesel discloses, wherein the size of the item includes one or more dimensions of the item and a range of motion of a movable portion of the item (Examiner interprets that the product visualization that accounts for product portions, movement/deformation, gravity effects, and region-specific product behavior. Wiesel teaches that the universal dressing module takes into account product image size, user pose, lighting, shading, and real-world gravitation i.e. the claimed “range of motion of a movable portion” reads on Wiesel’s simulation of movement/deformation/displacement of portions of a product, such as sleeves or garment regions, where the system estimates how portions move, drop, cling, or space apart due to gravity and fit. The “dimensions” portion is further supported by Wiesel’s product size/dimension and size-chart disclosures.) (“The Product Handler Module 102 prepares the clothes image (or product image) taken from different vendor catalogs for the dressing process using image processing, computer vision and machine learning. This module needs only a single product image that may be invariant, but is not limited to quality and camera angles in order to create a 3D estimation of the product. Additionally, multiple product images or videos may give a better estimation and be suited for different scenarios. The product handler module is further discussed herein, for example, with reference to FIG. 3.”) (0075-0076, 0097-0102, 0149-0154).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for determine a size of an item based on extracted size data corresponding to the item; select a comparison item; and generate an interactive representation of the item and the comparison item, as taught by Kim, wherein the size of the item includes one or more dimensions of the item and a range of motion of a movable portion of the item, as taught by Wiesel for the purpose to incorporate product-portion/gravity-based simulation into visual product comparison system to render a more realistic product representation and improve the user’s ability to understand not only static size but also how movable/deformable portions of a product would behave in use thus providing meaningful contextual product visualization before purchase.
As per claims 6 and 16, Kim discloses, wherein the interactive representation of the item and the comparison item includes allowing a user to move or actuate at least a portion of the item, a portion of the comparison item, or any combinations thereof (Examiner interprets that the interactive representation allows a user to move at least a portion of the item or comparison item. Kim teaches that the GUI allows a user to move, scale, and/or reposition a retrieved image near a product or offering and that the user may drag a selected image near or overlapping a product i.e. Kim’s moving, scaling, dragging, and repositioning of the product/comparison image corresponds to allowing a user to move at least a portion of the item or comparison item in the interactive representation) (“Display module 130 communicates with auto comparator module 122, to invoke various object comparisons when a user views a product on one or more of websites 108A-N. Display module 130 can include a graphical user interface (GUI) (not depicted in FIG. 1), which allows a user to move, scale, and/or reposition a retrieved image near a product or offering, to make a contextual visual comparison. In some embodiments, display module 130 can be integrated with auto comparator module 122”) (0017, 0021-0027).
As per claims 7 and 17, Kim specifically doesn’t disclose, generate the interactive representation by generating a three-dimensional rendering of the item using a plurality of images of the item, however Wiesel discloses, wherein the instructions further cause the computing system to generate the interactive representation by generating a three-dimensional rendering of the item using a plurality of images of the item (Examiner interprets that the Product Handler Module prepares product images from vendor catalogs using image processing, computer vision, and machine learning to create a 3D estimation of the product. Wiesel further teaches that multiple product images or videos may provide better estimation and may be suited for different scenarios. See Wiesel 0075. And that the combination image may comprise a two-dimensional image, three-dimensional image, three-dimensional model, video, or animation. See Wiesel 0424. Wiesel’s 3D estimation and multiple-image/video disclosure is supported by the publication text i.e. 3D estimation from product images/videos and its disclosure of a three-dimensional image/model/video/animation correspond to generating a 3D rendering using a plurality of images of the item) (“The Product Handler Module 102 prepares the clothes image (or product image) taken from different vendor catalogs for the dressing process using image processing, computer vision and machine learning. This module needs only a single product image that may be invariant, but is not limited to quality and camera angles in order to create a 3D estimation of the product. Additionally, multiple product images or videos may give a better estimation and be suited for different scenarios. The product handler module is further discussed herein, for example, with reference to FIG. 3.”) (0075, 0424, 0492-0496, 0112).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for determine a size of an item based on extracted size data corresponding to the item; select a comparison item; and generate an interactive representation of the item and the comparison item, as taught by Kim, generate the interactive representation by generating a three-dimensional rendering of the item using a plurality of images of the item, as taught by Wiesel for the purpose to use 3D estimation from multiple product images/videos to provide a more realistic and accurate interactive product representation and thus improve visual accuracy and user understanding of product size and fit..
As per claims 8 and 18, Kim specifically doesn’t disclose, extract edges of the item in the plurality of images and determine dimensions of the extracted edges of the item by combining edge measurements obtained from the plurality of images to generate a fully dimensioned model of the item, however Wiesel discloses, wherein the instructions further cause the computing system to: extract edges of the item in the plurality of images (Examiner interprets that product segmentation and classification, converting product images into product masks/templates, cutting internal and external boundaries of the product, completing missing contours, using edge detection, using multiple images of the same product from different angles, and correcting angular perspective) (“As part of the preparation of the Product Mask, portions of the product image may be removed, added, completed, or modified. For example, if the original product image (shown herein on the right side) depicts a shirt that is not worn by a human model, then the top area of the image shows a shirt region that should be hidden or obstructed if such shirt is later superimposed or overlaid upon an image of the user that performs virtual dressing. Therefore, such top area is cut or discarded or carved-out from the image, in order to allow the product mask to be ready for virtual fitting upon the user's image … system may further cure or modify or fix, a particular type of distortion that may occur due to angular perspective of imaging as well as the three-dimensional structure of the human body, as opposed to the generally-flat two-dimensional appearance of a shirt which is merely laid-down on a table for photography. The system may detect a non-curved, or a generally-linear, contour line of a product image, and may classify such product image as requiring modification. Classification may also be performed by checking points-of-interest in the product, which may be generated by the product classification and Big Data similarity module. Once the system detects a linear or non-curved contour line, an emulation or simulation may be performed, modifying the angular perspective of imaging to create curved contour lines; for example, modifying the angular perspective of the product image, from a full-frontal perspective, to an angular perspective as if the product is imaged from chest-level or neck-level.”) (0133-0143, 0144-0148, 0424-0425);
and determine dimensions of the extracted edges of the item by combining edge measurements obtained from the plurality of images to generate a fully dimensioned model of the item (Examiner interprets that Wiesel’s product mask, edge detection, contour detection, boundary cutting, missing-contour completion, and multiple-image processing correspond to extracting edges from a plurality of images. Wiesel’s product dimension information and scaling of product images correspond to determining dimensions/scale of the extracted product representation. In combination with Kim’s product size-comparison system, this teaches or suggests generating a dimensioned product model/representation for use in the interactive comparison.) (“present invention may process a product image by completing hidden or obstructed regions of the product. In this example, a Dress (product) is worn by a human model, with two obstructions: the model's hair obstructs a portion of the top side of the dress, and the model's fingers obstruct a portion of the middle area of the dress. As demonstrated, the system may determine the general contour of the dress (e.g., removing the head and neck of the human model); and may then add the estimated contour lines that are missing (e.g., the top shoulder line, and the right-side dress contour line); and may then fill-in those areas with the suitable color(s) or texture(s) based on the nearby colors or textures; thereby generating (shown on the left side) a product mask that is useful for further virtual fitting, with curing of the previously-obstructed product regions.”) (0139-0141, 0144-0148, 0492-0494).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for determine a size of an item based on extracted size data corresponding to the item; select a comparison item; and generate an interactive representation of the item and the comparison item, as taught by Kim, extract edges of the item in the plurality of images and determine dimensions of the extracted edges of the item by combining edge measurements obtained from the plurality of images to generate a fully dimensioned model of the item, as taught by Wiesel for the purpose to use edge/contour/mask extraction and product-dimension scaling techniques in visual comparison system to improve the accuracy of the product outline, scale, and rendered appearance thus allowing more accurate size visualization and better product fit assessment.
As per claims 9 and 19, Kim specifically doesn’t disclose, wherein the instructions further cause the computing system to combine the plurality of images of the item using pose estimation, however Wiesel discloses, wherein the instructions further cause the computing system to combine the plurality of images of the item using pose estimation (Examiner interprets that using multiple images/videos, correcting user distortion caused by different camera angles, recreating coordinates in a 3D world, correcting perspective distortion, correcting angular perspective of a product image, checking points of interest, and modifying angular perspective to create more realistic curved contour lines i.e. Wiesel’s correction of camera-angle distortion, recreation of 3D coordinates, use of points of interest, multiple images/videos, and angular-perspective modification correspond to using pose/perspective estimation to combine or process a plurality of images) (“Image enhancement and perspective correction module—this unit or process enhances the user image before segmentation. The enhancements fix a large set of problems resulting from improper usage of the camera including, but not limited to a dark photo, a blurry photo, a noisy photo, and a grayish photo (very low saturation). The same applies, but is not limited to multiple photos and/or a video. This unit or process also corrects the user distortion caused by different camera angles using the user's height. This allows the unit or process to recreate the user coordinates in a 3D world (understanding, but not limited to the user 3D surface, one's distance from the camera, and the correct perspective distortion to add to the products that will be dressed on the user in the dressing process)”) (0082, 0092-0094, 0097-0102, 0074-0075, 0141-0424).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for determine a size of an item based on extracted size data corresponding to the item; select a comparison item; and generate an interactive representation of the item and the comparison item, as taught by Kim, wherein the instructions further cause the computing system to combine the plurality of images of the item using pose estimation, as taught by Wiesel for the purpose to incorporate pose/perspective correction into product comparison system to improve the alignment, realism, and dimensional accuracy of product images used in the rendered comparison thus to improve visual comparison output.
As per claims 10, Kim discloses, wherein the comparison item is selected from a database of known size items based on the determined size of the item (Examiner interprets that reference objects database corresponds to the claimed database of known-size items, and Kim’s object selector corresponds to selecting a comparison item based on contextual relevance and comparable size/volume i.e. selecting the comparison item from a database of known-size items based on the determined size of the item. Kim teaches that reference objects database 132 stores known-size reference objects and that object selector 124 chooses one or more objects or environments for product comparison based on contextual relevance, familiarity, size, color, volume, and usage scenario) (“Object selector 124 chooses one or more objects or environments for a product comparison. In this exemplary embodiment, object selector 124 selects objects or environments which have a contextual relevance to a user. Object selector 124 determines which objects or environments may have a contextual relevance to a user by factoring the following criteria: the familiarity of the object or environment to the user (e.g., a room in the user's house or the cellphone of a user), the size, color, and/or volume of the object or environment is comparable to the product, and the usage scenarios between the product and object or environment (e.g., comparison between the size of a pan contemplated for purchase and a user's stovetop). Additionally, object selector 124 may determine one or more contextually relevant images using information from websites to which a user has uploaded content, viewed content, and/or purchased products. In this exemplary embodiment, object selector 124 automatically selects a relevant image to make a contextual comparison to a product.”) (0019-0020 0016, 0030).
As per claims 12, Kim specifically doesn’t disclose, wherein the instructions for extracting size data further includes one or more natural language processors configured to search textual description of an object, however Wiesel discloses, wherein the instructions for extracting size data further includes one or more natural language processors configured to search textual description of an object (Examiner interprets that a Products Web Crawler Module that automatically prepares web catalog products and uses machine learning to obtain textual information about each product and validate textual product information in a web catalog. See Wiesel 0077. Wiesel further teaches crawling product pages, locating textual information, identifying product descriptions/summary fields, using contextual/textual analysis, and using NLP analysis of textual descriptions, user reviews, comments, and product descriptions. See Wiesel 0103-0110, 0154, 0451. Wiesel’s NLP/textual description support is further reflected in its disclosure of Natural Language Processing and textual descriptions/reviews for product-context decisions i.e. machine-learning web crawler and NLP/textual analysis of product descriptions, reviews, comments, and catalog information correspond to the claimed natural language processors configured to search textual descriptions of an object) (“The Products Web Crawler Module 104 allows the automatic or autonomous preparation of any web catalog product for dressing. The web crawler uses machine learning in order to get textual information about each product. The web crawler validates products' textual information in the web catalog. This module may be invariant, but is not limited to the products' vendors. The products web crawler module is further discussed herein, for example, with reference to FIG. 5.” and “Perspective and shape correction module—this unit or process prepares the product for the dressing by, but not limited to, giving the product a 3D volume, perspective distortion, and realistic color (if the product was not worn by a model). If the product was worn by a model, this unit or process prepares the product for dressing by, but not limited to, extrapolating inner parts of the product that were hidden by the model's body) (0077 and 0103-0110, 0092-0094, 0097-0104).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for determine a size of an item based on extracted size data corresponding to the item; select a comparison item; and generate an interactive representation of the item and the comparison item, as taught by Kim, wherein the instructions for extracting size data further includes one or more natural language processors configured to search textual description of an object, as taught by Wiesel for the purpose to use NLP/textual product-description extraction in product comparison system to automate and improve extraction of product size and descriptive information used in visual size comparison.
As per claims 13, Kim discloses, wherein the size data includes raw or formatted data including information usable to determine the size of the object item (Examiner interprets that the size data includes raw or formatted data including information usable to determine the size of the object item. Kim teaches using provided product dimensions and/or mined metadata from product manuals or other sources to determine a meaningful relative comparison i.e. Product dimensions, metadata, product manuals, product dimension data, and numeric/alphanumeric product size data correspond to raw or formatted data usable to determine item size) (“Image scale and rotator 126 allows the scaling of a comparison image in a meaningful way to a target image (i.e., a product for purchase). Image scale and rotator 126 may use provided product dimensions and/or may mine metadata from product manuals or other sources to determine a meaningful relative comparison, and may access reference objects database 132. For example, a user viewing a product, such as a television, may drag the image of the television to an image of the user's living room, and image scale and rotator 126 automatically resizes the image of the television to the scale of the image of the user's living room, so that the user may visualize how the real-world size of the television fits within the dimensions of the user's living room.”) (0020).
As per claims 14, Kim discloses, a reference objects database storing standardized objects having known sizes, including a dollar bill, a human of a particular height and size, a tablet device, and a smartphone and using product dimensions/metadata and the reference objects database to determine a meaningful relative comparison, but specifically doesn’t disclose, use a pattern recognition model to locate a scale object in an image of the item and determine the size of the item based at least in part on a dimension of the scale object, however Wiesel discloses, use a pattern recognition model to locate a scale object in an image of the item (Examiner interprets that the product-image processing using image processing, computer vision, machine learning, artificial-intelligence techniques, product extraction, product segmentation/classification, product masks, edge detection, contour detection, points of interest, and product dimension scaling) (“Product extraction module—this unit or process extracts the product from the background and from the model (if it exists). This unit or process uses artificial intelligence techniques and textual data in order to distinguish between the model's body, the background, and different clothes. This unit or process may be invariant, but not limited to different model poses, backgrounds, product shape, angles, and different product vendors … Perspective and shape correction module—this unit or process prepares the product for the dressing by, but not limited to, giving the product a 3D volume, perspective distortion, and realistic color (if the product was not worn by a model). If the product was worn by a model, this unit or process prepares the product for dressing by, but not limited to, extrapolating inner parts of the product that were hidden by the model's body”) (0090-0094, 0075, 0492-0494);
and determine the size of the item based at least in part on a dimension of the scale object (Examiner interprets that the use of computer vision/machine learning to prepare product images and 3D estimation, as well as product segmentation, edge detection, and contour processing i.e. the pattern-recognition/image-processing mechanism for locating and processing objects, contours, boundaries, and points of interest in images. It would have been obvious to apply Wiesel’s computer-vision/pattern-recognition techniques to Kim’s known-size reference objects to locate a scale object in an image and determine item size based on the known dimension of that scale object) (“processing user selectable product images and facilitating visualization-assisted coordinated product transactions, the method comprising: identifying one or more products having one or more associated product images; image processing said one or more said product images by incorporating said image(s) into an anchor image to generate a composite anchor image that depicts said one or more products according to an intended manner of use thereof; said image processing including using product dimension information to scale said one or more product images as necessary to match a scale of said anchor image; causing said composite anchor image to be displayed; causing one or more user interface elements to be displayed in association with said composite anchor image, said one or more user interface elements being operable to initiate a coordinated product transaction involving said one or more products; and responsive to said one or more user interface elements being activated, initiating said coordinated product transaction.”) (0492-0494).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for determine a size of an item based on extracted size data corresponding to the item; select a comparison item; and generate an interactive representation of the item and the comparison item, as taught by Kim, use a pattern recognition model to locate a scale object in an image of the item and determine the size of the item based at least in part on a dimension of the scale object, as taught by Wiesel for the purpose to apply product-dimension-based scaling to visual comparison system to improve the accuracy, realism, and usefulness of the rendered product comparison, thereby helping a user better assess product size before purchase.
As per claims 20, Kim discloses, wherein the comparison item is selected from a database of known size items based on the determined size of the item, and wherein the database is accessible through the data interface via the network (Examiner interprets that the comparison item is selected from a database of known-size items based on the determined size of the item, and that the database is accessible through a data interface via a network. Kim teaches websites 108A-N, network 110, computing device 120, reference objects database 132, and object selector 124. Kim further teaches that the reference objects database may be stored remotely, such as on a server, and accessed and/or downloaded by the computing device) (“Object selector 124 chooses one or more objects or environments for a product comparison. In this exemplary embodiment, object selector 124 selects objects or environments which have a contextual relevance to a user. Object selector 124 determines which objects or environments may have a contextual relevance to a user by factoring the following criteria: the familiarity of the object or environment to the user (e.g., a room in the user's house or the cellphone of a user), the size, color, and/or volume of the object or environment is comparable to the product, and the usage scenarios between the product and object or environment (e.g., comparison between the size of a pan contemplated for purchase and a user's stovetop). Additionally, object selector 124 may determine one or more contextually relevant images using information from websites to which a user has uploaded content, viewed content, and/or purchased products. In this exemplary embodiment, object selector 124 automatically selects a relevant image to make a contextual comparison to a product.”) (0019-0020, 0012-0016).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US. Pub. 20160210602 (“Siddique”).
Siddique outlines a method to generating a combination image that depicts subject matter engaging with a first item of a first search result and at least one of two or more second items of two or more second search results, wherein the combination image incorporates a background of an image of a user-defined subject matter; and generating one or more user interface elements to be displayed in conjunction with the combination image; and enabling the one or more user interface elements to perform one or more operations, wherein the combination image includes one or more of: a two-dimensional image, a three-dimensional image, a three-dimensional model, a video, or an animation.
26. Any inquiry concerning this communication or earlier communications from the examiner should be directed to GAUTAM UBALE whose telephone number is (571)272-9861. The examiner can normally be reached Mon-Fri. 7:00 AM- 6:30 PM PST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Marissa Thein can be reached at (571) 272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/GAUTAM UBALE/
Primary Examiner, Art Unit 3689
Prosecution Insights