DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1, 11 and 17 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 11 and 17 of U.S. Patent No. 12,266,065 . Although the claims at issue are not identical, they are not patentably distinct from each other because it is clear that all the elements of the application claims 1, 11 and 17 are to be found in patent claims 1, 11 and 17 (as the application claims 1, 11 and 17 fully encompasses patent claims 1, 11 and 17). The difference between application claims 1, 11 and 17 and the patent claims 1, 11 and 17 lies in the fact that the patent claim includes many more elements and is thus much more specific. Thus, the invention of claims 1, 11 and 17 of the patent is in effect a “species” of the “generic” invention of the application claims 1, 11 and 17. It has been held that the generic invention is “anticipated” by the “species”. See In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993). Since application claims 1, 11 and 17 are anticipated by claims 1, 11 and 17 of the patent, they are not patentably distinct from claims 1, 11 and 17 of the patent.
19/062,858
1. A computing system, the system comprising: one or more processors; and one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the computing system to perform operations, the operations comprising: obtaining multimodal data comprising a text input and image data, wherein the text input comprises a query associated with an environment, and wherein the image data depicts at least a portion of the environment;
processing the text input and the image data with a vision language model to generate a model-generated query;
processing the model-generated query with a search engine to determine a plurality of search results;
processing the text input and at least a subset of the plurality of search results with a generative model to generate a model-generated response, wherein the model-generated response comprises a predicted response to the query, and wherein the model-generated response is associated with an object within the environment;
obtaining additional image data;
processing the model-generated response and the additional image data with an image augmentation model to generate an augmented image, wherein the augmented image is descriptive of the environment annotated based on the model-generated response, and wherein the image augmentation model annotates the additional image data based on detecting the object in the image data; and providing the augmented image for display.
11. A computer-implemented method, the method comprising:
obtaining, by a computing system comprising one or more processors, multimodal data comprising a text input and image data, wherein the text input comprises a query associated with an environment, and wherein the image data depicts at least a portion of the environment;
processing, by the computing system, the text input and the image data with a vision language model to generate a model-generated query;
processing, by the computing system, the model-generated query with a search engine to determine a plurality of search results;
processing, by the computing system, the text input and at least a subset of the plurality of search results with a generative model to generate a model-generated response,
wherein the model-generated response comprises a predicted response to the query, and wherein the model-generated response is associated with an object within the environment;
obtaining, by the computing system, additional image data;
processing, by the computing system, the model-generated response and the additional image data with an image augmentation model to generate an augmented image, wherein the augmented image is descriptive of the environment annotated based on the model-generated response, and wherein the image augmentation model annotates the additional image data based on detecting the object in the image data; and providing, by the computing system, the augmented image for display.
12,266,065 B1
1. A computing system for augmented-reality annotations, the system comprising: one or more processors; and one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the computing system to perform operations, the operations comprising: obtaining a user input, wherein the user input comprises a query associated with a user environment;
obtaining image data descriptive of the user environment, wherein the image data depicts at least a portion of the user environment; processing the query and the image data with a vision language model to generate a model-generated query;
processing the model-generated query with a search engine to determine a plurality of search results;
processing the user input and at least a subset of the plurality of search results with a generative model to generate a model-generated response, wherein the generative model comprises a machine-learned autoregressive language model, wherein the model-generated response comprises a predicted response to the query, and wherein the model-generated response is associated with an object; processing the model-generated response and the image data with an image augmentation model to generate an augmented image, wherein the augmented image is descriptive of the user environment annotated based on the model-generated response, and wherein the image augmentation model annotates the image data based on detecting the object in the image data; and providing the augmented image for display.
11. A computer-implemented method for providing a response in an augmented-reality experience, the method comprising:
obtaining, by a computing system comprising one or more processors, multimodal data, wherein the multimodal data comprises a query associated with features in a user environment, and wherein the multimodal data comprises an input image, wherein the input image depicts at least a portion of the user environment;
processing, by the computing system, the query and the input image with a vision language model to generate a model-generated query;
processing, by the computing system, the input image and the model-generated query with a search engine to determine a plurality of search results; processing, by the computing system, the multimodal data and at least a subset of the plurality of search results with a generative model to generate a model-generated response, wherein the generative model comprises a machine-learned autoregressive language model,
wherein the model-generated response comprises a predicted response to the query,
wherein the model-generated response comprises instructions for performing a sequence of actions, and wherein the model-generated response is associated with a plurality of objects;
obtaining, by the computing system, image data descriptive of the user environment, wherein the image data depicts at least a portion of the user environment; processing, by the computing system, the model-generated response and the image data with an image augmentation model to generate a plurality of augmented images, wherein the plurality of augmented images are descriptive of the user environment annotated based on the model-generated response, and wherein the image augmentation model annotates the image data based on detecting the plurality of objects in the image data; and providing, by the computing system, the plurality of augmented images for display in the augmented-reality experience in stages based on the sequence of actions associated with the instructions, wherein the augmented-reality experience is updated based on determining an environment change.
17. One or more non-transitory computer-readable media that collectively store instructions that, when executed by one or more computing devices, cause the one or more computing devices to perform operations, the operations comprising: obtaining multimodal data comprising a text input and image data, wherein the text input comprises a query associated with an environment, and wherein the image data depicts at least a portion of the environment;
processing the text input and the image data with a vision language model to generate a model-generated query;
processing the model-generated query with a search engine to determine a plurality of search results;
processing the text input and at least a subset of the plurality of search results with a generative model to generate a model-generated response, wherein the model-generated response comprises a predicted response to the query, and
wherein the model-generated response is associated with an object within the environment; obtaining additional image data;
processing the model-generated response and the additional image data with an image augmentation model to generate an augmented image, wherein the augmented image is descriptive of the environment
annotated based on the model-generated response, and wherein the image augmentation model annotates the additional image data based on detecting the object in the image data; and providing the augmented image for display.
17. One or more non-transitory computer-readable media that collectively store instructions that, when executed by one or more computing devices, cause the one or more computing devices to perform operations, the operations comprising: obtaining multimodal data, wherein the multimodal data comprises a query associated with features in a user environment, and wherein the multimodal data comprises an input image, wherein the input image depicts at least a portion of the user environment;
processing the query and the input image with a vision language model to generate a model-generated query, wherein the vision language model comprises a pre-trained large language model tuned to perform masked-language modeling and multimodal fusing with cross attention;
processing the input image and the model-generated query with a search engine to determine a plurality of search results; processing the multimodal data and at least a subset of the plurality of search results with a generative model to generate a model-generated response, wherein the generative model comprises a machine-learned language model, wherein the model-generated response comprises a predicted response to the query,
wherein the model-generated response is associated with a plurality of objects, and wherein the model-generated response comprises a multi-part response, wherein different parts of the multi-part response are associated with different objects of the plurality of objects;
processing, by the computing system, the model-generated response with an image generation model to generate predicted pixel data; obtaining image data descriptive of the user environment, wherein the image data depicts at least a portion of the user environment; processing the image data with a detection model to generate a first bounding box, wherein the first bounding box is descriptive of a position of a first object of the plurality of objects within the image data; processing the model-generated response, the first bounding box, the predicted pixel data, and the image data with an annotation model to generate a first augmented image, wherein the first augmented image depicts the user environment
annotated based on a first part of the model-generated response, wherein the augmented image data comprises at least a portion of the image data augmented based on the predicted pixel data, and wherein the first augmented image further comprises a first annotation that indicates the position of the first object of the plurality of objects within the image data; and providing the first augmented image for display.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wright 11,417,091 B2.
Regarding claim 1, Wright discloses a computing system, the system comprising: one or more processors (202, processor); and one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the computing system to perform operations (col. 63, lines 31-33, a non-transitory computer readable storage medium can have stored thereon instructions that, when executed by a processor, cause a HMD to), the operations comprising:
obtaining multimodal data comprising a text input and image data, wherein the text input comprises a query associated with an environment (col. 35, line 62, one or both of the DNN image input and location database may be further benefited when a user provides some verbal instructions; user is capturing an image or video; sound sequence; voice-to-text programs or algorithms may be used to analyze the sound sequence captured in association with the image or video and identify nouns; col. 33, lines 35-37, users may automatically verbally identify a name of the object and/or action associated with the object; for example, the user may say “look here, this is a . . .!”; col. 33, lines 40-44, they may also draw annotations to indicate which object(s) is really meant with “this”; system or user device may use both inputs), and wherein the image data depicts at least a portion of the environment (col. 33, lines 23-25, user is moving around in the environment with the camera of the user device activated); processing the text input and the image data with a vision language model to generate a model-generated query (col. 7, lines 30-41, processor may extract visual features; col. 7, lines 53-56, extracted visual features can include a machine-readable code; can be capable of being algorithmically decoded; col. 8, lines 28-29, may encode information about the object; Examiner interprets the encode and decode as a vision language model, in that Applicant’s specification discloses vision language model as having an encoder and decoder); processing the model-generated query with a search engine to determine a plurality of search results (col. 7, lines 63-65, processor can attempt to match the extracted visual features to records of objects stored in the object database; accessing the object database and locating a record in the database that matches the extracted features; col. 34, lines 35-37, (for each annotation visible in the view of any user) a search request in an associated neural network to obtain a new segmentation for the current camera image); processing the text input and at least a subset of the plurality of search results with a generative model to generate a model-generated response, wherein the model-generated response comprises a predicted response to the query, and wherein the model-generated response is associated with an object within the environment (col. 32, lines 25-31, deep learning may be used predictively to assist with generating labels and/or annotations based on the object in the view and the verbal commands/statements to simply user interaction in generating the annotations; if the user is typing annotation; deep learning may provide words of object); obtaining additional image data (col. 8, line 17, obtain more specific information); processing the model-generated response and the additional image data with an image augmentation model to generate an augmented image, wherein the augmented image is descriptive of the environment annotated based on the model-generated response, and wherein the image augmentation model annotates the additional image data based on detecting the object in the image data (col. 33, lines 58-62, identify verbs and nouns; may use the nouns to generate annotations displayed as a label text); and providing the augmented image for display (col. 15, lines 5-6, display may also provide annotations which can be “anchored” to objects within the environment; col. 15, line 14, and AR experience).
Regarding claim 2, Wright discloses wherein generating the augmented image comprises rendering a visual indicator within the additional image data, wherein the visual indicator is descriptive of at least a portion of the model-generated response (col. 14, lines 45-49, local user may be required to perform manual tasks while receiving information about the local environment; the tasks can relate to service and maintenance of an object/machinery, where the local user receive step-by-step instructions from a database, Examiner interpret each step as additional image data descriptive of a modeled task generated response).
Regarding claim 3, Wright discloses wherein the visual indicator comprises highlighting the object (col. 7, line 67 – col. 8, line 4, in response to visual features matching one of the records stored in the object database, the processor may highlight the object).
Regarding claim 4, Wright discloses wherein the visual indicator comprises text superimposed over the additional image data and tinting at least a portion of the additional image data (col. 26, lines 4-5, where each annotation is superimposed: in spatial relationship to the second object; col. 33, line 65 – col. 34, line 5, segment out image areas that fit to the label text and highlight the one area that contains the reference point; object will be highlighted; device may identify edges on the object that are closest to the drawn outline and replace the drawn outline with those edges; “new” drawn outline, which Examiner interprets as tinting).
Regarding claim 5, Wright discloses wherein an input image of the image data depicts at least a portion of a document (col. 1, line 21, consulting written documentation).
Regarding claim 6, Wright discloses the vision language model comprises a document understanding model (col. 53, lines 59-66, text conversion there will be verbs in the sentences that are generated around or said around the drawing time, and there will be nouns; use the nouns, like open the screw, to label the object automatically; system would recognize screw and would put screw as a label next to the drawing with a leading line to the screw).
Regarding claim 7, Wright discloses wherein the generative model comprises a document understanding model (col. 53, lines 59-66, text conversion there will be verbs in the sentences that are generated around or said around the drawing time, and there will be nouns; use the nouns, like open the screw, to label the object automatically; system would recognize screw and would put screw as a label next to the drawing with a leading line to the screw).
Regarding claim 8, Wright discloses wherein the model-generated response is associated with a plurality of feature sets within the input image (col. 30, lines 64-67, selectable features or parts can be internal to the object; camera’s field of view).
Regarding claim 9, Wright discloses wherein the augmented image comprises the additional image data annotated to indicate particular portions of the document relevant to the model-generated response (col. 30, lines 62-67, a CAD model of an object can be used to highlight selectable features of the object in the AR field of view; selectable features or parts can be internal to the object; Examiner interprets the internal portions as additional image data, and Wright further discloses can be highlighted, therefore relevant to the step by step instructions).
Regarding claim 10, Wright discloses wherein the generative model comprises a multitask unified model (col. 7, lines 19-22, a plurality of objects may be related in some way such that they may be considered a set of objects that could match a single record in the object database; Col. 7, lines 22-26, may receive assistance with assembling a piece of furniture; the furniture may include a plurality of different objects that are to be attached to one another; col. 14, lines 45-49, local user may be required to perform manual tasks while receiving information about the local environment; the tasks can relate to service and maintenance of an object/machinery, where the local user receive step-by-step instructions from a database).
Regarding claim 11, it is rejected based upon similar rational as above claim 1. Wright further discloses a computer-implemented method (col. 1, line 62).
Regarding claim 12, Wright discloses wherein the model-generated response comprises instructions for performing a sequence of actions (col. 14, lines 45-49, local user may be required to perform manual tasks while receiving information about the local environment; the tasks can relate to service and maintenance of an object/machinery, where the local user receive step-by-step instructions from a database).
Regarding claim 13, Wright discloses further comprising: generating, by the computing system, a plurality of augmented images with the image augmentation model based on the model-generated response (col. 30, lines 4-15, may update a new pose for each frame of the annotation and/or object; new pose allows for rendering the outline of the object from the geometric representation of the objects; recognizing objects using a trained neural network).
Regarding claim 14, Wright discloses further comprising: providing, by the computing system, the plurality of augmented images for display in an augmented-reality experience in stages based on the sequence of actions associated with the instructions, wherein the augmented-reality experience is updated based on determining an environment change (col. 32, lines 40-42, deep learning may be used to more easily create labels and also to remove labels as actions are completed or the objects disappear; col. 56, lines 30-44, determine whether or not a user is performing an action; open that panel door, and the user may be wearing augmented reality glasses, they open the door; recognize from the video that the user has completed the action).
Regarding claim 15, Wright discloses further comprising: processing, by the computing system, the model-generated response with an image generation model to generate predicted pixel data (col. 57, lines 18-20, analyze the pixels in a specific way and learns a way to distinguish images where cars are in and images where no cars are).
Regarding claim 16, Wright discloses wherein generating the augmented image with the image augmentation model comprises: Augmenting at least a portion of the additional image data based on the predicted pixel data (col. 46, lines 6-7, a key point is described by its surround pixels; the key point may be a descriptor which is basically looking at the surrounding of the point in image and is encoding the surrounding descriptor to be a unique thing that can be identified in another key frame; col. 46, lines 30-34, an equation system may use the 2D position of the feature points in the one image and feature points in the other image to figure out where these two cameras are).
Regarding claim 17, it is rejected based upon similar rational as above claim 1. Wright further discloses one or more non-transitory computer-readable media that collectively store instructions that, when executed by one or more computing devices, cause the one or more computing devices to perform operations (col. 63, lines 31-33).
Regarding claim 18, Wright discloses wherein the model-generated response is associated with a plurality of objects, and wherein the model- generated response comprises a multi-part response, wherein different parts of the multi-part response are associated with different objects of the plurality of objects (Col. 7, lines 22-26, may receive assistance with assembling a piece of furniture; the furniture may include a plurality of different objects that are to be attached to one another).
Regarding claim 19, Wright discloses wherein the operations further comprise: processing the additional image data with a detection model to generate a plurality of bounding boxes associated with the different objects of the plurality of objects (col. 28, lines 26-28, one common annotation may be a rough outline drawn around the object, which Examiner interprets as a bounding box, to highlight or indicate that the object is selected or identified; col. 28, lines 62-63, 3D object recognition and automatic outline generation; col. 29, lines 36-37, may utilize a database of all objects in the environment that users might annotate).
Regarding claim 20, Wright discloses wherein the operations further comprise: processing the model-generated response, the additional image data, and the plurality bounding boxes with the image augmentation model to generate a plurality of augmented images (col. 30, lines 4-15, may update a new pose for each frame of the annotation and/or object; new pose allows for rendering the outline of the object from the geometric representation of the object).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ross et al., U.S. Patent Publication Number 2018/0357826 A1
Ross discloses image data depicts at least a portion of the environment (paragraph 0008, capturing images of the physical environment or user gestures);
Eledath et al., U.S. Patent Publication Number 2016/0378861 A1
Eledath discloses obtaining multimodal data comprising a text input and image data, wherein the text input comprises a query associated with an environment, and wherein the image data depicts at least a portion of the environment (paragraph 0037, conduct a “multi-modal dialog” with a user, in which different portions of the dialog comprise different forms of input, e.g., visual imagery, natural language speech, gestures, gaze data, computer-synthesized elements, etc.; a user may, while viewing a real world scene through a camera speak a natural language request such as “show me pictures of something like that” or “who owns that truck”); processing the text input and the image data with a vision language model to generate a model-generated query (paragraph 0037, in response, the system can extract semantic information from the portion of the visual imagery that correspond to “this” and “that”, build a computer-executable query that expresses the intent of the user’ speech-based request, execute the query);
processing the model-generated response and the additional image data with an image augmentation model to generate an augmented image, wherein the augmented image is descriptive of the environment annotated based on the model-generated response, and wherein the image augmentation model annotates the additional image data based on detecting the object in the image data (paragraph 0036, annotations are generated using augmented realty techniques; paragraph 0037, present information retrieved from the query as, for example, an augmented reality overlay or system-generate natural language speech; FIG.5 and FIG. 6); and providing the augmented image for display (paragraph 0094, displays virtual element on the view of the real world scene using one or more display device of the computing system).
Hsaio et al., U.S. Patent Number 9,875,258 B1
Hsaio discloses col. 62-65, identify an item represented in an image to generate a search string and one or more refinements that can be used as inputs to a search query.
Shazeer et al., U.S. Patent Publication Number 2022/0374608 A1
Shazeer discloses obtaining multimodal data comprising a text input and image data (paragraph 0009, obtaining a contextual text string; paragraph 0024, textual analysis can include and/or leverage the use of structural tools which provide access to additional information), wherein the text input comprises a query associated with an environment (paragraph 0025, a query service that queries results); processing the text input and the image data with a vision language model to generate a model-generated query (paragraph 0023, interpretability of the machine-learned language model by teaching the model to generate textual analysis; paragraph 0047, language models can optionally have an encoder-decoder architecture); processing the model-generated query with a search engine to determine a plurality of search results (paragraph 0025, machine-learned language model may have access to: a query service that queries results from a search engine);
processing the text input and at least a subset of the plurality of search results with a generative model to generate a model-generated response, wherein the model-generated response comprises a predicted response to the query (paragraph 0030, the model can be trained by using the model to predict a next token); (paragraph 0024, access to additional information which may be up-to-date, factual, domain-specific, client- or user-specific); (paragraph 0024, language model can call and use such structural tools to have access to additional information which may be up-to-date, factual, domain-specific, client- or user-specific, etc. This improves the knowledge available to the language model when formulating the textual output and further improves the flexibility of the system by enabling the introduction of various information sources for various use cases; paragraph 0055, human annotator optionally modifies the base model’s response).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Motilewa Good-Johnson whose telephone number is (571)272-7658. The examiner can normally be reached Monday - Friday 6am-2:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jason Chan can be reached at 571-272-3022. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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MOTILEWA . GOOD JOHNSON
Primary Examiner
Art Unit 2616
/MOTILEWA GOOD-JOHNSON/Primary Examiner, Art Unit 2619
Prosecution Insights