ABSTRACT
Abstract
Various computing technologies for content analysis.
Description
CROSS-REFERENCE TO RELATED PATENT APPLICATIONS
This patent application is a continuation of U.S. Non-Provisional patent application Ser. No. 17/627,719 filed Jan. 17, 2022, which is a national phase entry of PCT Application PCT/US2020/043482 filed Jul. 24, 2020, which claims a benefit of priority to U.S. Provisional Patent Application 62/981,763 filed on Feb. 26, 2020, U.S. Provisional Patent Application 62/890,234 filed on Aug. 22, 2019, and U.S. Provisional Patent Application 62/878,931 filed on Jul. 26, 2019, each of which is incorporated by reference herein for all purposes.
BACKGROUND
In this disclosure, where a document, an act, and/or an item of knowledge is referred to and/or discussed, then such reference and/or discussion is not an admission that the document, the act, and/or the item of knowledge and/or any combination thereof was at a priority date, publicly available, known to a public, part of common general knowledge, and/or otherwise constitutes any prior art under any applicable statutory provisions; and/or is known to be relevant to any attempt to solve any problem with which this disclosure is concerned with. Further, nothing is disclaimed.
Patent annotation is technologically complicated. For example, some patent figures have fonts of various types, sizes, shapes, orientations, pitches, colors, inclinations, prints, and handwriting or cursive styles. As such, some OCR engines are thrown off by these fonts, which can lead to avoidance of patent figure segmentation, improper patent figure segmentation, inaccurate character recognition, non-recognition of characters, and other recognition issues. Moreover, if some characters are not recognized or inaccurately recognized, then end users are generally stuck with whatever results are available. Also, many annotations can be displayed over parts depicted in patent figures, which leads to reduced patent figure usability and can contribute to end user confusion or frustration. Additionally, some annotated patent figures can be plagiarized or copied, without any annotator compensation or recognition. Further, some patent figures can be difficult to understand due to patent figure color requirements. In addition, some patent figures can be difficult to understand without further context. Moreover, navigating between patent figures and patent specification can be difficult, especially when the patent figures have many part numbers or when the patent specification is technically dense. Also, understanding patent figures or patent specification can be laborious and time-consuming, especially when the patent figures have many part numbers or when the patent specification is technically dense.
BRIEF DESCRIPTION OF DRAWINGS
FIG. 1 is a flowchart of an embodiment of a visual association process according to this disclosure.
FIG. 2 is a flowchart of an embodiment of a visual association process according to this disclosure.
FIG. 3 is a flowchart of an embodiment of a visual association process according to this disclosure.
FIGS. 4 A- 4 E are various diagrams depicting various embodiments of visual associations according to this disclosure.
FIGS. 5 A- 5 C are various diagrams depicting various embodiments of visual associations according to this disclosure.
FIGS. 6 A- 6 B show a figure before and after an embodiment of a visual association process according to this disclosure.
FIG. 7 is a diagram of an embodiment of a topology within which a visual association is performed according to this disclosure.
FIGS. 8 A- 8 B are diagrams of embodiments of figures before and after visual association (and other processing) according to this disclosure.
FIG. 9 A is an embodiment of a presentation of an annotated figure according to this disclosure.
FIG. 9 B is an embodiment of a presentation of a translated annotated figure with a translation legend according to this disclosure.
FIG. 9 C is an embodiment of a presentation of a searchable database of patent figures according to this disclosure.
FIG. 9 D is an embodiment of a color-based search technique for convenient user review according to this disclosure.
FIG. 9 E is an embodiment of a presentation of a figure where all part numbers were recognized and matched to corresponding part names according to this disclosure.
FIG. 9 F is an embodiment of an annotated figure having a missing description bounding box that is colored to be visually distinct relative to other bounding boxes according to this disclosure.
FIG. 9 G is an embodiment of a presentation of a legend of an annotated figure where the reference numbers or part numbers in the legend are paired with respective annotation words or part names according to this disclosure.
FIG. 9 H is an embodiment of a presentation of an annotated figure with a missing description indication as well as a legend according to this disclosure.
FIG. 9 I is an embodiment of a presentation of an annotated figure where the user has an ability to move, resize, reshape, edit, delete, or take control of some, many, most, or all content (e.g., bounding boxes, labels, part names, translations) added to, associated with, or presented over the figure, whether presented internal or external thereto or page or user interface window containing a figure according to this disclosure.
FIG. 9 J is an embodiment of a presentation of various annotation options for variously annotating the figure according to this disclosure.
FIG. 9 K is an embodiment of a presentation of an On Hover (Out) annotation option for annotating the figure according to this disclosure.
FIG. 9 L shows an embodiment of a side-by-side view of a patent figure and a patent text according to this disclosure.
FIG. 9 M shows an embodiment of a text search of a text pane according to this disclosure.
FIG. 9 N shows an embodiment of a pane synchronization according to this disclosure.
FIG. 9 O shows a text pane being synchronized to a figure pane according to this disclosure.
FIG. 9 P presents a user movable or a user resizable panes or tiles in a user interface according to this disclosure.
FIG. 10 A illustrates an embodiment of a user interface according to this disclosure.
FIG. 10 B illustrates an embodiment of a user interface for enabling navigation from figure-to-text according to this disclosure.
FIG. 10 C illustrates an embodiment of a user interface according to this disclosure.
FIG. 10 D illustrates an embodiment of a user interface according to this disclosure.
FIG. 10 E illustrates an embodiment of a user interface according to this disclosure, where the user interface is programmed to enable an edit annotation feature, which allows the user to edit or to add part a name-part number mapping, when the part number is incorrectly recognized or not recognized according to this disclosure.
FIG. 10 F illustrates an embodiment of a user interface according to this disclosure, where there is highlighting of navigation from text-to-figure.
FIG. 10 G illustrates an embodiment of a user interface according to this disclosure, where there is a showing of a result responsive to activating (e.g., clicking, touching) the right arrow in the text shown in FIG. 10 F .
FIG. 10 H illustrates an embodiment of a user interface according to this disclosure, where the part name ârodâ in the text or the part number â23â in the text have been highlighted (green) in the text based on the activation (e.g., clicking, hovering, touching) of the part number â23â in the figure, as described herein.
FIG. 10 I illustrates an embodiment of a user interface according to this disclosure, where the search (within text) bar is active based on the text query (e.g., collar) having being entered thereinto, where the text query can include any alphanumeric content.
FIG. 10 J illustrates an embodiment of a user interface according to this disclosure, where there is highlighting within the legend and interactive navigation from within the legend through the figure or through the text.
FIG. 10 K illustrates an embodiment of a user interface according to this disclosure, where a search (within figure) by a part name is enabled alongside the legend and interactive navigation from within the legend through the figure or the text.
FIG. 10 L illustrates an embodiment of a user interface according to this disclosure, where swapping of content between the viewing panes occurs based on activating the âswap areasâ button in the user interface, as described herein.
FIGS. 10 M- 10 N illustrate an embodiment of a user interface having the legend being laterally positioned according to this disclosure.
FIG. 11 A illustrates an embodiment of a user interface with a tabbed configuration formed via a retrieve tab and a search database tab according to this disclosure.
FIG. 11 B shows the user interface for the search database tab where the user can search the database using âandâ or âorâ searches to narrow down a part name search according to this disclosure.
FIG. 11 C illustrates a message that may be presented in response to the user retrieving a patent document by a patent application publication number instead of by an issued patent grant number according to this disclosure.
FIG. 11 D illustrates a progress bar of a user interface according to this disclosure.
FIG. 11 E illustrates a message that may be presented upon completion of processing of an issued patent grant file to inform the user that a corresponding patent application publication file is still undergoing processing according to this disclosure.
FIG. 11 F illustrates a user interface presenting an issued patent grant file according to this disclosure.
FIG. 11 G illustrates a result of a user hovering over the âswitchâ button while the issued patent grant file is presented in a user interface according to this disclosure.
FIG. 11 H illustrates a result of the user pressing the âswitchâ button while the issued patent grant file is presented in a user interface according to this disclosure.
FIG. 11 I illustrates a result of a user hovering over the âswitchâ button while the patent application publication file is presented within the user interface according to this disclosure.
FIG. 11 J illustrates a user interface of the search database tab showing a set of user input fields for searching the patent database according to this disclosure.
DETAILED DESCRIPTION
The set of accompanying illustrative drawings shows various embodiments of this disclosure. Such drawings are not to be construed as necessarily limiting this disclosure. Like numbers and/or similar numbering scheme can refer to like and/or similar elements throughout.
This disclosure is now described more fully with reference to the set of accompanying drawings, in which some embodiments of this disclosure are shown. This disclosure may, however, be embodied in many different forms and
CROSS-REFERENCE TO RELATED PATENT APPLICATIONS
This patent application is a continuation of U.S. Non-Provisional patent application Ser. No. 17/627,719 filed Jan. 17, 2022, which is a national phase entry of PCT Application PCT/US2020/043482 filed Jul. 24, 2020, which claims a benefit of priority to U.S. Provisional Patent Application 62/981,763 filed on Feb. 26, 2020, U.S. Provisional Patent Application 62/890,234 filed on Aug. 22, 2019, and U.S. Provisional Patent Application 62/878,931 filed on Jul. 26, 2019, each of which is incorporated by reference herein for all purposes.
BACKGROUND
In this disclosure, where a document, an act, and/or an item of knowledge is referred to and/or discussed, then such reference and/or discussion is not an admission that the document, the act, and/or the item of knowledge and/or any combination thereof was at a priority date, publicly available, known to a public, part of common general knowledge, and/or otherwise constitutes any prior art under any applicable statutory provisions; and/or is known to be relevant to any attempt to solve any problem with which this disclosure is concerned with. Further, nothing is disclaimed.
Patent annotation is technologically complicated. For example, some patent figures have fonts of various types, sizes, shapes, orientations, pitches, colors, inclinations, prints, and handwriting or cursive styles. As such, some OCR engines are thrown off by these fonts, which can lead to avoidance of patent figure segmentation, improper patent figure segmentation, inaccurate character recognition, non-recognition of characters, and other recognition issues. Moreover, if some characters are not recognized or inaccurately recognized, then end users are generally stuck with whatever results are available. Also, many annotations can be displayed over parts depicted in patent figures, which leads to reduced patent figure usability and can contribute to end user confusion or frustration. Additionally, some annotated patent figures can be plagiarized or copied, without any annotator compensation or recognition. Further, some patent figures can be difficult to understand due to patent figure color requirements. In addition, some patent figures can be difficult to understand without further context. Moreover, navigating between patent figures and patent specification can be difficult, especially when the patent figures have many part numbers or when the patent specification is technically dense. Also, understanding patent figures or patent specification can be laborious and time-consuming, especially when the patent figures have many part numbers or when the patent specification is technically dense.
BRIEF DESCRIPTION OF DRAWINGS
FIG. 1 is a flowchart of an embodiment of a visual association process according to this disclosure.
FIG. 2 is a flowchart of an embodiment of a visual association process according to this disclosure.
FIG. 3 is a flowchart of an embodiment of a visual association process according to this disclosure.
FIGS. 4 A- 4 E are various diagrams depicting various embodiments of visual associations according to this disclosure.
FIGS. 5 A- 5 C are various diagrams depicting various embodiments of visual associations according to this disclosure.
FIGS. 6 A- 6 B show a figure before and after an embodiment of a visual association process according to this disclosure.
FIG. 7 is a diagram of an embodiment of a topology within which a visual association is performed according to this disclosure.
FIGS. 8 A- 8 B are diagrams of embodiments of figures before and after visual association (and other processing) according to this disclosure.
FIG. 9 A is an embodiment of a presentation of an annotated figure according to this disclosure.
FIG. 9 B is an embodiment of a presentation of a translated annotated figure with a translation legend according to this disclosure.
FIG. 9 C is an embodiment of a presentation of a searchable database of patent figures according to this disclosure.
FIG. 9 D is an embodiment of a color-based search technique for convenient user review according to this disclosure.
FIG. 9 E is an embodiment of a presentation of a figure where all part numbers were recognized and matched to corresponding part names according to this disclosure.
FIG. 9 F is an embodiment of an annotated figure having a missing description bounding box that is colored to be visually distinct relative to other bounding boxes according to this disclosure.
FIG. 9 G is an embodiment of a presentation of a legend of an annotated figure where the reference numbers or part numbers in the legend are paired with respective annotation words or part names according to this disclosure.
FIG. 9 H is an embodiment of a presentation of an annotated figure with a missing description indication as well as a legend according to this disclosure.
FIG. 9 I is an embodiment of a presentation of an annotated figure where the user has an ability to move, resize, reshape, edit, delete, or take control of some, many, most, or all content (e.g., bounding boxes, labels, part names, translations) added to, associated with, or presented over the figure, whether presented internal or external thereto or page or user interface window containing a figure according to this disclosure.
FIG. 9 J is an embodiment of a presentation of various annotation options for variously annotating the figure according to this disclosure.
FIG. 9 K is an embodiment of a presentation of an On Hover (Out) annotation option for annotating the figure according to this disclosure.
FIG. 9 L shows an embodiment of a side-by-side view of a patent figure and a patent text according to this disclosure.
FIG. 9 M shows an embodiment of a text search of a text pane according to this disclosure.
FIG. 9 N shows an embodiment of a pane synchronization according to this disclosure.
FIG. 9 O shows a text pane being synchronized to a figure pane according to this disclosure.
FIG. 9 P presents a user movable or a user resizable panes or tiles in a user interface according to this disclosure.
FIG. 10 A illustrates an embodiment of a user interface according to this disclosure.
FIG. 10 B illustrates an embodiment of a user interface for enabling navigation from figure-to-text according to this disclosure.
FIG. 10 C illustrates an embodiment of a user interface according to this disclosure.
FIG. 10 D illustrates an embodiment of a user interface according to this disclosure.
FIG. 10 E illustrates an embodiment of a user interface according to this disclosure, where the user interface is programmed to enable an edit annotation feature, which allows the user to edit or to add part a name-part number mapping, when the part number is incorrectly recognized or not recognized according to this disclosure.
FIG. 10 F illustrates an embodiment of a user interface according to this disclosure, where there is highlighting of navigation from text-to-figure.
FIG. 10 G illustrates an embodiment of a user interface according to this disclosure, where there is a showing of a result responsive to activating (e.g., clicking, touching) the right arrow in the text shown in FIG. 10 F .
FIG. 10 H illustrates an embodiment of a user interface according to this disclosure, where the part name ârodâ in the text or the part number â23â in the text have been highlighted (green) in the text based on the activation (e.g., clicking, hovering, touching) of the part number â23â in the figure, as described herein.
FIG. 10 I illustrates an embodiment of a user interface according to this disclosure, where the search (within text) bar is active based on the text query (e.g., collar) having being entered thereinto, where the text query can include any alphanumeric content.
FIG. 10 J illustrates an embodiment of a user interface according to this disclosure, where there is highlighting within the legend and interactive navigation from within the legend through the figure or through the text.
FIG. 10 K illustrates an embodiment of a user interface according to this disclosure, where a search (within figure) by a part name is enabled alongside the legend and interactive navigation from within the legend through the figure or the text.
FIG. 10 L illustrates an embodiment of a user interface according to this disclosure, where swapping of content between the viewing panes occurs based on activating the âswap areasâ button in the user interface, as described herein.
FIGS. 10 M- 10 N illustrate an embodiment of a user interface having the legend being laterally positioned according to this disclosure.
FIG. 11 A illustrates an embodiment of a user interface with a tabbed configuration formed via a retrieve tab and a search database tab according to this disclosure.
FIG. 11 B shows the user interface for the search database tab where the user can search the database using âandâ or âorâ searches to narrow down a part name search according to this disclosure.
FIG. 11 C illustrates a message that may be presented in response to the user retrieving a patent document by a patent application publication number instead of by an issued patent grant number according to this disclosure.
FIG. 11 D illustrates a progress bar of a user interface according to this disclosure.
FIG. 11 E illustrates a message that may be presented upon completion of processing of an issued patent grant file to inform the user that a corresponding patent application publication file is still undergoing processing according to this disclosure.
FIG. 11 F illustrates a user interface presenting an issued patent grant file according to this disclosure.
FIG. 11 G illustrates a result of a user hovering over the âswitchâ button while the issued patent grant file is presented in a user interface according to this disclosure.
FIG. 11 H illustrates a result of the user pressing the âswitchâ button while the issued patent grant file is presented in a user interface according to this disclosure.
FIG. 11 I illustrates a result of a user hovering over the âswitchâ button while the patent application publication file is presented within the user interface according to this disclosure.
FIG. 11 J illustrates a user interface of the search database tab showing a set of user input fields for searching the patent database according to this disclosure.
DETAILED DESCRIPTION
The set of accompanying illustrative drawings shows various embodiments of this disclosure. Such drawings are not to be construed as necessarily limiting this disclosure. Like numbers and/or similar numbering scheme can refer to like and/or similar elements throughout.
This disclosure is now described more fully with reference to the set of accompanying drawings, in which some embodiments of this disclosure are shown. This disclosure may, however, be embodied in many different forms and should not be construed as necessarily being limited to the embodiments disclosed herein. Rather, these embodiments are provided so that this disclosure is thorough and complete, and fully conveys various concepts of this disclosure to skilled artisans.
Aspects of this disclosure are described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. Each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. The various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
The flowchart and block diagrams as included herewith illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.
Words such as âthen,â ânext,â etc. are not intended to limit the order of the steps; these words are simply used to guide the reader through the description of the methods. Although process flow diagrams may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination may correspond to a return of the function to the calling function or the main function.
Features or functionality described with respect to certain embodiments may be combined and sub-combined in and/or with various other embodiments. Also, different aspects and/or elements of embodiments, as disclosed herein, may be combined and sub-combined in a similar manner as well. Further, some embodiments, whether individually and/or collectively, may be components of a larger system, wherein other procedures may take precedence over and/or otherwise modify their application. Additionally, a number of steps may be required before, after, and/or concurrently with embodiments, as disclosed herein. Note that any and/or all methods and/or processes, at least as disclosed herein, can be at least partially performed via at least one entity or actor in any manner.
The terminology used herein can imply direct or indirect, full or partial, temporary or permanent, action or inaction. For example, when an element is referred to as being âon,â âconnectedâ or âcoupledâ to another element, then the element can be directly on, connected or coupled to the other element and/or intervening elements can be present, including indirect and/or direct variants. In contrast, when an element is referred to as being âdirectly connectedâ or âdirectly coupledâ to another element, there are no intervening elements present.
Although the terms first, second, etc. can be used herein to describe various elements, components, regions, layers and/or sections, these elements, components, regions, layers and/or sections should not necessarily be limited by such terms. These terms are used to distinguish one element, component, region, layer or section from another element, component, region, layer or section. Thus, a first element, component, region, layer, or section discussed below could be termed a second element, component, region, layer, or section without departing from the teachings of the present disclosure.
The terminology used herein is for describing particular embodiments and is not intended to be necessarily limiting of this disclosure. As used herein, the singular forms âa,â âanâ and âtheâ are intended to include the plural forms (e.g., two, three, four, five, six, seven, eight, nine, ten, tens, hundreds, thousands, millions, or more) as well, including intermediate whole or decimal forms, unless the context clearly indicates otherwise. Also, as used herein, the term âaâ and/or âanâ shall mean âone or more,â even though the phrase âone or moreâ is also used herein. The terms âcomprises,â âincludesâ and/or âcomprising,â âincludingâ when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence and/or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Furthermore, when this disclosure states herein that something is âbased onâ something else, then such statement refers to a basis which may be based on one or more other things as well. In other words, unless expressly indicated otherwise, as used herein âbased onâ inclusively means âbased at least in part onâ or âbased at least partially on.â
As used herein, the term âorâ is intended to mean an inclusive âorâ rather than an exclusive âor.â That is, unless specified otherwise, or clear from context, âX employs A or Bâ is intended to mean any of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, then âX employs A or Bâ is satisfied under any of the foregoing instances. For example, X includes A or B can mean X can include A, X can include B, and X can include A and B, unless specified otherwise or clear from context.
As used herein, the term âresponseâ or âresponsiveâ are intended to include a machine-sourced action or inaction, such as input (e.g., local, remote), or a user-sourced action or inaction, such as input (e.g., via user input device).
As used herein, the term âaboutâ and/or âsubstantiallyâ refers to a +/â10% variation from the nominal value/term. Such variation is always included in any given.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. The terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and should not be interpreted in an idealized and/or overly formal sense unless expressly so defined herein.
Hereby, all issued patents, published patent applications, and non-patent publications that are mentioned or referred to in this disclosure are herein incorporated by reference in their entirety for all purposes, to a same extent as if each individual issued patent, published patent application, or non-patent publication were specifically and individually indicated to be incorporated by reference. To be even more clear, all incorporations by reference specifically include those incorporated publications as if those specific publications are copied and pasted herein, as if originally included in this disclosure for all purposes of this disclosure. Therefore, any reference to something being disclosed herein includes all subject matter incorporated by reference, as explained above. However, if any disclosures are incorporated herein by reference and such disclosures conflict in part or in whole with this disclosure, then to an extent of the conflict or broader disclosure or broader definition of terms, this disclosure controls. If such disclosures conflict in part or in whole with one another, then to an extent of conflict, the later-dated disclosure controls.
FIG. 1 is a flowchart of an embodiment of a visual association process according to this disclosure. In particular, a process 100 includes blocks 110 - 120 . The process 100 (or any steps thereof) can be performed, whether serially or in parallel with each other, via a single core processor or a multi-core processor, irrespective of whether these cores are local or remote to each other.
The multicore processor can include a plurality of independent cores. For example, the multicore processor is a computing component with two or more independent processing units, which are the units that read and execute program instructions, such as a front-end application, such as via multiprocessing or multithreading. The program instructions are processing instructions, such as add, move data, or branch, but the cores can run multiple instructions concurrently, thereby increasing an overall operational speed for the front-end application, which is amenable to parallel computing. The cores can process in parallel when concurrently accessing a file or any other data structure, as disclosed herein, while being compliant with atomicity, consistency, isolation, and durability (ACID) principles, which ensure that such data structure operations/transactions, such as read, write, erase, or others, are processed reliably. For example, a data structure can be accessed, such as read or written, via at least two cores concurrently without locking the data structure between such cores. For example, a figure and a text can be concurrently processed, as disclosed herein. Note that there can be at least two cores, such as two cores, three cores, four cores, five cores, six cores, seven cores, eight cores, nine cores, ten cores, twelve cores, tens of cores, hundreds of cores, thousands of cores, millions of cores, or more. The cores may or may not share caches, and the cores may or may not implement message passing or shared-memory inter-core communication methods. Common network topologies to interconnect cores include bus, ring, two-dimensional mesh, and crossbar. Homogeneous multi-core systems include only identical cores, heterogeneous multi-core systems can have cores that are not identical. The cores in multi-core systems may implement architectures, such as very long instruction word (VLIW), superscalar, vector, or multithreading. Whether additionally or alternatively, the process 100 can also be performed by a graphics card, a graphics processing unit (GPU), a programming logic controller (PLC), a tensor core unit, a tensor processing unit (TPU), an application specific integrated circuit (ASIC), or another processing circuit, whether on a stationary or mobile platform, whether in a single or distributed over a plurality of data centers or server farms. The process 100 can be a process on its own or can be a sub-process within another process, which can be a distributed process.
Note that although the process 100 is described in context of classical computing, the process 100 (or any other processes described herein) can be implemented via or supplemented via a quantum computing system (e.g., work in conjunction with classical computing). For example, the quantum computing system can a superconducting architecture (e.g., lattice, bowtie), nonlinear superconducting resonator architecture, annealing architecture, or others of any manufacturer (e.g., Google, IBM, D-Wave, Intel, Rigetti). For example, some systems and methods can be programmed for determining when and how to leverage quantum computing devices or systems when solving computational tasks (e.g., visual association, part name annotation, character recognition, image segmentation, character segmentation, part number recognition, handwriting recognition, part name identification, label shaping, label positioning, database searching, text summarization, specification summarization, independent or dependent or multiple dependent claim summarization, legend formation, part name-part number mapping for hyperlinking, artificial neural network training, deep learning). For example, some of the systems can receive computational tasks, (e.g., optimization tasks, character recognition, handwriting recognition, part number recognition, image segmentation, character segmentation, part name placement, shape positioning, text summarization, specification summarization, independent or dependent or multiple dependent claim summarization, legend formation, part name-part number mapping for hyperlinking, artificial neural network training, deep learning) to be performed. For example, some of the systems may be an optimization engine, which can be selectively started, running, stopped, or paused, that is configured to receive input data and to generate, as output, an optimal solution to an optimization task (e.g., part number recognition, image segmentation, character segmentation, part name placement, text summarization, specification summarization, independent or dependent or multiple dependent claim summarization, artificial neural network training, deep learning) based on the received input data. For example, the optimal solution can be probability-based as being most likely outcome. The received input data can include static or real-time data, which can include text, images, coordinates, sounds, patent or non-patent (e.g., technical articles, encyclopedia articles, technical manuals, product manuals, engineering blueprints, construction blueprints, building blueprints) informational documents (e.g., PDF files, HTML files, XML files, JSON data structures), or others. Some of the systems can outsource or offload computations associated with the received computational tasks to one or more local or remote external devices (e.g., cloud computing instances, hardware or virtual servers). Some of the systems may preprocess the computational tasks (e.g., preprocess patent figures, preprocess patent specification, preprocess independent or dependent or multiple dependent claims, artificial neural network training, deep learning) before outsourcing the computational tasks, which can include separating or grouping a received computational task into or with one or more sub-tasks (e.g., process patent figure segment). The external devices can include quantum computing devices (e.g., quantum annealers, quantum simulators, quantum gate computers) and classical computing devices (e.g., standard classical processors or supercomputers). Some of the systems can decide when and where to outsource various computations associated with the received computational tasks. Such task routing may be a complex problem that may be dependent on many factors. Some of the systems can be trained to learn optimal routings of received computational tasks (e.g., part number recognition, part name placement, image segmentation, character segmentation, text summarization, specification summarization, independent or dependent or multiple dependent claim summarization, legend formation, part name-part number mapping for hyperlinking, artificial neural network training, deep learning) using a set of training data (e.g., image segments, font types, character templates, text corpora, technical documents, patent literature, technical or product manuals, engineering or construction or building blueprints). The training data can include data from several sources, which may be used to generate multiple training examples.
Some or each training example can include (i) input data relating to a previous computational task, such as data specifying the task, size/complexity of the task, restrictions for solving the task, error tolerance, (ii) information relating to which computational device was used to solve the task, or (iii) metrics indicating a quality of the solution obtained using the device, (e.g., level of confidence in solution, computational time taken to generate solution, computational costs incurred). Other data may be included in the training data, including an indication of the computational resources available when processing the previous computational tasks, a number of qubits used in the quantum devices, ability of algorithms running on the quantum devices or classical devices to process the problem, costs of using the classical or quantum devices, or reliability of classical or quantum devices. Some of the systems can learn to adapt the routings of computational tasks based on traffic flow in the system, (e.g., if many computational tasks are received, then a machine learning module or a computational logic may learn to prioritize certain tasks or subtasks, or learn when more efficient to wait for a particular computational device to become available again or when more efficient to route a task to a second-choice computing device). Training can include applying conventional or proprietary or other machine learning techniques for various tasks (e.g., part number recognition, part name placement, image segmentation, character segmentation, text summarization, specification summarization, independent or dependent or multiple dependent claim summarization, legend formation, part name-part number mapping for hyperlinking, or others based on computing a loss function or backpropagating gradients, artificial neural network training, deep learning). Once the machine learning module or computational logic has been trained, then the machine learning module or computational logic may be used at runtime for inference (e.g., to receive new computational tasks to be performed and to find an improved routing of the computational tasks in order to obtain solutions to the received tasks). For example, some of such tasks can include part number recognition, part name placement, image segmentation, character segmentation, text summarization, specification summarization, part name and part number tokenization, independent or dependent or multiple dependent claim summarization, legend formation, part name-part number mapping for hyperlinking, or others.
Note that other forms of computing can be used, whether additionally or alternatively. For example, photons within neural network (tensor) processing units (e.g., TPUs) can be used for various neural network, machine learning, deep learning, or other computing tasks, as disclosed herein. For example, a photonic tensor core can be used as an integrated photonics-based tensor core unit by strategically utilizing (i) photonic parallelism via wavelength division multiplexing, (ii) high 2 peta-operations-per-second throughputs enabled by tens of picosecond-short delays from optoelectronics and compact photonic integrated circuitry, and (iii) near-zero static power-consuming novel photonic multi-state memories based on phase-change materials featuring vanishing losses in the amorphous state. Combining these physical synergies of material, function, and system, the performance of this 4-bit photonic tensor core unit can be 1 order of magnitude higher for electrical data. This photonic tensor processor enables processing of optical data, which can enable a 2-3 orders higher performance (operations per joule), as compared to an electrical tensor core unit, while featuring similar chip areas. The photonic specialized processors have the potential to augment electronic systems, whether classical or quantum, as disclosed herein, for any computing tasks disclosed herein. For example, a neuromorphic computing system (e.g., IBM TrueNorth, Intel Kapoho Bay system, Intel Pohoki Springs system, Intel Pohoiki beach, Intel Loihi architecture, a cognitive computer) can be used, which can be embodied in a datacenter rack-mounted system. For example, the neuromorphic system can be used for constraint satisfaction problems (e.g., character recognition, fuzzy-logic, part name placement, label shaping, neural network model training, learning from user edits or positioning of labels), which require evaluating a large number of potential solutions to identify the one or few that satisfy specific constraints. For example, the neuromorphic computing system can be used for searching graphs for optimal paths, such as finding the shortest route between locations (e.g., useful for label positioning) or searching for pixel or voxel patterns.
Block 110 includes matching (e.g., content, format, size, full, partial, fuzzy) a reference (e.g., alphanumeric, monospaced or proportional font content, cursive content, typewritten content, number, barcode, part number, layer name abbreviation, orientation symbol) recognized (e.g., computer vision, OpenCV, OCR, optical word recognition, Intelligent Character Recognition (ICR), Intelligent Word Recognition (IWR), barcode reading, text area detection, edge detection, segmentation, image segmentation, character segmentation, object detection, feature detection, sketch identification) in a figure (e.g., patent figure, blueprint figure, architectural figure, user device or system manual figure, medical imaging figure, engineering figure, CAD figure, anatomical figure, music sheet, image, JPG image, TIFF image, photo album, photo, social networking service image or photo) to an identifier (e.g., alphanumeric, word or words, barcode, QR code, part name, layer name, orientation name, object name, pattern identifier, metadata tag) found (e.g., text processing, natural language processing (NLP), natural language understanding, rule-based or statistical or syntax or semantic or discourse or speech processing, entity recognition, regular expressions, auto-generated text summarization) in a text (e.g., structured text, non-structured text, column, paragraph, sentence, data structure, cell, table) corresponding to the reference (e.g., one-to-one, one-to-many, many-to-one, many-to-many). For example, the figure can be on its own or included in a figure sheet or image or page. For example, the figure sheet or image or page can depict a single figure or a plurality of figures. The reference identifies the element referred to by the reference (e.g., by tokenized proximity in text, positional proximity to left or right or up or down by a preset number of tokens or positions or empty spaces or non-letter or non-number characters in text). Note that the patent figure can be of utility, design, or plant type of published patent application or issued patent, whether abandoned, maintained, pending, granted, or expired, whether of US patent format or foreign patent format (e.g., JPO, CIPO, SIPO, KIPO). For example, the US patent format can include provisional, utility, design, plant, reissue, defensive publication, statutory invention registration, additional improvement, X-patent, or others. Further, note that non-patent figure can be used as well (e.g., utility model).
For example, finding the identifier in the text can involve grammar induction, lemmatization, morphological segmentation, part-of-speech tagging, parsing (e.g., dependency, constituency), parse tree generation (e.g., probabilistic context-free grammar, stochastic grammar), sentence breaking, stemming, word segmentation, terminology extraction, lexical semantics, distributional semantics, machine translation, named entity recognition, natural language generation, natural language understanding, recognizing textual entailment, relationship extraction, sentiment analysis, topic segmentation or recognition, word sense disambiguation, automatic summarization, coreference resolution, discourse analysis, speech recognition, speech segmentation, or other techniques that run on text (e.g., files, columns, paragraphs, sentences). For example, such processing can be based on part numbers in patent text.
The reference in the figure can include an alphanumeric character (e.g., letter, number, grammar character, punctuation mark) visually tagging or labeling or referring (e.g., solid or broken or rectilinear or non-rectilinear lines or other shapes, positional proximity) to an element (e.g., object shown, subpart of object, layer of object, orientation of object, dimension of object, property of object) of the figure. One or more alphanumeric characters (e.g., 1, 26, 321f, 221-H, 533-3, car, d2!a, #g2) may be used, or even non-alphanumeric character references (e.g., barcode, QR code, symbol, graphic) may be used. For example, the reference can be or include symbols as well. The identifier can include a name or a brief description of the element or part, which is often textually described but can be non-textually described or linked to as well (e.g., barcode, QR code). Alternatively, the identifier can include a plurality of terms, a sentence, or a paragraph.
The identifier can be capped at a preset number of words or letters or characters linguistically preceding or associated with a content (e.g., alphanumeric, number) associated with or corresponding to the reference in the figure. For example, the identifier can be one, two, three, four, five or more words preceding the content associated with or corresponding to the reference in the figure. For example, the identifier can be capped based on a dictionary of words, which can be user-customized. For example, the identifier can be a single word preceding the content associated with or corresponding to the reference in the figure, unless the identifier is within the dictionary. In such case, the identifier can be more than one word (e.g., two, three, four). For example, the dictionary can include terms inclusive of member, means, element, device, system, about, verbs, adverbs, or others. For example, if the identifier is capped at one, then âa heating element 12â or âthe heating element 12â may be processed as âelementâ or â element 12,â which may be user uninformative, but if capped at two, then âheating elementâ or â heating element 12â can be captured. For example, the dictionary can include words in one language or more than one language (e.g., English, Russian, Hebrew, Arabic, Mandarin, Cantonese, Hindi, Dutch, Japanese, Korean, German, French). In such case, those words can be translated before, during, or after locating the identifier.
In order to locate the reference in the figure, various techniques can be performed on the figure. For example, the figure can be converted into an image (e.g., JPEG, TIFF), whether into higher or lower resolution, while maintaining or improving content quality of the image. For example, the image can be pre-processed (e.g., horizontal or vertical de-skew, noise reduction, despeckle, binarization, line removal, layout analysis/zoning, line and word detection, script recognition, character isolation/segmentation, script recognition, normalize aspect ratio or scale, or others). In some cases, a two-pass approach to character recognition can be used to recognize the reference in the image, where the second pass (e.g., adaptive recognition) uses the letter/number shapes recognized with high confidence on the first pass to recognize better the remaining letters on the second pass, which can be advantageous for unusual fonts or handwriting or cursive fonts or low-quality scans where the font is distorted (e.g. blurred or faded). In some cases, the reference, as recognized, can be stored in a data structure (e.g., JSON ALTO format, XML schema), In some cases, the reference is more accurately increased when recognition is constrained by a lexicon or a dictionary (e.g., list of content that are allowed to occur or be found), which can be user-customized. For example, the lexicon or the dictionary can be based on the identifier(s) found in the text, which can be based on various text processing at least as described herein. For example, the lexicon or the dictionary can be some, many, most, or all words in English language or a set of numbers (e.g., 0-10000 which can include or not include whole numbers or decimal numbers or numbers separated by a grammar character or a period or a dash or underscore) or a technical lexicon for a specific field or a list of nouns or adjective-noun pairs. The identifier can be output to a plain text stream or file of characters or preserve an original layout of the figure and produce, for example, an annotated PDF or image that includes both an original image of the page and a searchable textual representation. Note that the page can include a sheet or vice versa. For example, a sheet or page can include a single figure or a plurality of figures.
Typically, the name of the element is disclosed in a description of the figure (e.g., HTML file, PDF file, MS Word file, XML file, JSON file). For example, in a patent figure a number 10, which can be the reference, can visually refer to an element of the figure, such as a chair. A word âchairâ can be the identifier that is disclosed in a related patent text (e.g., same file, different file, local or remote from each other) describing the patent figure.
Block 120 includes visually associating (e.g., positioning proximity, laying over figure or object in figure, generating popup, hover over or pointer over, slide out) in the figure the identifier with the reference. Block 120 can be performed close in time (e.g., within 1 hour, within 30 minutes, within 15 minutes, within 10 minutes, within 5 minutes) or far apart in time to block 110 (e.g., several days apart). One way of visually associating (e.g., annotating) the identifier with the reference is by placing the identifier positionally adjacent to the reference over the figure or within the figure (e.g., modifying figure). Alternatively, non-adjacent visual association is possible as well where the identifier refers to the reference irrespective of where the identifier is placed on the figure (e.g., corner, several inches or pixel or voxel equivalents apart, margins). Thus, the word âchairâ does not have to be positionally adjacent to the number 10 (reference). As long as there is a visual association between the word âchairâ and the number 10 or a reader can perceive/believe that there is a visual association, even if the word âchairâ is at a far distance from the number 10 (e.g., at corner of page or figure, at bottom center or top center of page or figure, along left or right side of page or figure, margins), a user can easily identify what the number 10 is referring to. An example of an adjacent visual association is if the number 10 in the figure visually refers to a chair, then the word âchairâ is placed adjacent (e.g., within about 10, 9, 8, 7, 6, 5, 4, 3, 2, 1 inches or pixel or voxel equivalents) to the number 10 (e.g., avoid overwriting 10). Thus, a viewer of the figure (e.g., student, scientist, hobbyist, engineer, patent professional) can easily identify what the number 10 is referring to, without having to peruse the patent text to find the identifier. Visual associating the identifier with the reference corresponding thereto, even when two are not adjacent on same page or same document, is described herein. Note that although visual association is described as one example of action, other actions as described herein can also be performed, whether or not the references have been matched to the identifiers. For example, create, read, update and delete (CRUD) functionality can be enabled with annotation placements, annotation searching, specification or claim auto-summarization, or other actions described herein.
The figure or the page can be automatically, responsively, or manually rotated (e.g., less or more than inclusively within about 15 degrees, 30 degrees, 45 degrees, 60 degrees, 90 degrees, 120 degrees, 180 degrees, 270 degrees, 360 degrees, 720 degrees on Cartesian plane) before, during, or after part number recognition for recognition accuracy (e.g., pre-processing, segmentation, image segmentation, character segmentation) or for user readability. This rotation (e.g., clockwise or counterclockwise) may or may not rotate part numbers or part names. For example, when image rotation occurs and part numbers are correspondingly rotated, then at least some part names may remain not rotated to enable user readability. For example, this can occur when part names are presented based on image coordinates of part numbers that are recognized (e.g., enclosed within bounding boxes) and rotation of part numbers (or entire image) occurs while part names remain stationary or non-rotating. At least some rotation (e.g., clockwise or counterclockwise) can take place based on recognition of orientation or pitch of various part numbers in the figure (or the page) or standardized headings (e.g., sheet numbers, specific text) on the page. For example, if a part number X is recognized to be not upright (e.g., oriented or pitched to 9 o'clock or 3 o'clock), which can be relative to a figure content that is typically upright (e.g., relative to alphanumeric or barcode information on page margins, patent publication number, patent number, number of sheets) when that figure is presented in a default presentation (e.g., portrait), then the part number X or the figure can be rotated to be upright, which may make not upright the figure content that is typically upright.
Rotating the part number X to be upright but keeping the figure not upright, or rotating the figure to be upright but keeping the part number X not upright can include overwriting the figure or presenting a new upright part number (still X) over the not upright part number X or the not upright figure. For example, a technique for correcting orientation of patent figures for efficiently reviewing and analyzing a patent document (e.g., patent application, published patent document or patent) can include reading a patent image, identifying a figure page(s) in the patent image, determining which of the figure page(s) were originally prepared in a landscape orientation (e.g., based on figure identifier or figure sheet identifier and number orientation, part number orientation), and modifying or rotating those figure pages to be in a landscape orientation, thereby rotating
CLAIMS
Claims ( 1 )
1 . A method, comprising:
causing a processor to perform at least one of:
(a) generating a web page where the web page is programmed to simultaneously present a first viewing pane and a second viewing pane such that (i) the first viewing pane presents a patent figure and the second viewing pane presents a patent text, (ii) the patent figure presents an object, a line, and a first part number where the line extends between the object and the first part number, (iii) the patent text presents a part name and a second part number where the second part number follows the part name and matches the first part number in value, (iv) the first part number is hyperlinked to be activatable such that the patent text moves to the part name, and (v) the part name is hyperlinked to be activatable such that a visual marker associated with the first part number is presented over the patent figure where the visual marker is visually distinct relative to the object, the line, and the first part number;
(b) generating a user interface of a web browser where the user interface is programmed to (i) present an address bar and a viewport where the viewport presents a first content, (ii) receive a request after the address bar and the viewport are presented, and (iii) simultaneously present a first viewing pane and a second viewing pane within the viewport responsive to the request where the first viewing pane presents the first content and a second viewing pane presents a second content;
(c) generating a user interface on a mobile computing device where the user interface is programmed to (i) receive a selection of an area of an image, (ii) request a search of a set of content items for what is depicted in the area responsive to the selection, and (iii) present a result of the search responsive to the selection;
(d) generating a user interface of a word processing application where the user interface is programmed to present a viewport and an editable text in a plurality of rows within the viewport such that the editable text is scrollable within the viewport on a row-by-row basis: or
(e) generating a web page programmed to present a user interface enabled to receive a selection of a data file containing a text such that a copy of the data file is accessed, an Optical Character Recognition (OCR) process is performed on the copy of the file such that the text in the copy of the file is recognized, read the text in the copy of the file as recognized, create a summary of the text in the copy of the file as read, and present the summary in the user interface.
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