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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 19 recites the limitation "the pre-defined visibility". There is insufficient antecedent basis for this limitation in the claims.
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Claims 1-9, and 16-20 invoke USC 112(f).
Claim 1 limitations;
“a receiver module configured to receive a handwritten document”,
“a recognition module configured to recognize a plurality of potential terms”,
“a concept building module configured to: …”,
“and a knowledge graph building module configured to build a knowledge graph,”
and claim 16 limitations;
“a receiver module configured to receive,”,
“an entity recognition module configured to perform entity recognition”,
“a concept determination module configured to determine one or more conceptual terms”,
“an associative searching module configured to perform an associated searching”,
“and a rendering module configured to render ranked and selected search results to the user,”
and claims 2-9, which recite the modules of claim 1,
and claims 17-20, which recite the modules of claim 16,
invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. In above limitations 2, 3, 6, 7, 9, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Therefore, claims 1 and 16 are indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
According to the first part of the analysis, in the instant case, claims 1-20 are directed to method claims. Each of these claims fall within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Regarding claim 1,
Step 2A Prong One
a recognition module configured to recognize a plurality of potential terms for one or more objects in the handwritten document by employing a handwriting recognition technique, wherein the plurality of potential terms for each of the one or more objects includes a closest recognized term and at least one alternative recognized term;
(This step for recognizing terms from a document is considered a mental process)
a concept building module configured to: determine one or more conceptual terms from one or more potential recognized terms of the plurality of potential terms;
(This step for determining terms from recognized terms is considered a mental process)
and determine a multi-level relation between one or more potential recognized terms, out of the plurality of potential terms, and the handwritten document;
(This step for determining a relation between terms is considered a mental process)
and a knowledge graph building module configured to build a knowledge graph, wherein the knowledge graph is built based at least on one of: the plurality of potential terms, the one or more conceptual terms, the determined multi-level relation between the one or more potential recognized terms and the handwritten document, wherein the knowledge graph is used to enable at least a semantic searching of one or more handwritten documents.
(This step for constructing a knowledge graph is considered a mental process [it could be performed using pen and paper, for example] )
Step 2A Prong Two
A system for building a knowledge graph from handwritten document, the system comprising: a receiver module configured to receive a handwritten document along with dynamic handwritten data from an electronic device;
(This step for receiving information from a handwritten device is considered extra-solution activity. See MPEP § 2106.05(g) )
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered individually and in combination, they do not add significantly more (also known as an inventive concept) to the exception. The claim recites mental processes such as recognizing and determining terms and relations and building a knowledge graph while the additional elements of generically receiving data is a well-understood, routine, and conventional activity, as recognized by the court decisions listed in MPEP § 2106.05(d).
Regarding claim 2,
Step 2A Prong One
(Claim 2 depends on claim 1, which has been determined to recite abstract ideas including mental processes. Therefore, claim 2 also recites an abstract idea.)
Step 2A Prong Two
The system of claim 1, wherein the dynamic handwritten data is received in the form of one or more tuples having at least one of: data on x-axis, data on y-axis, pressure, speed of writing, or orientation.
(This step for limiting the selected information is considered insignificant extra-solution activity. See MPEP § 2106.05(g) )
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered individually and in combination, they do not add significantly more (also known as an inventive concept) to the exception. The claim recites mental processes while the additional elements of generically organizing and storing data are a well-understood, routine, and conventional activity, as recognized by the court decisions listed in MPEP § 2106.05(d).
Regarding claim 3,
Step 2A Prong One
(Claim 3 depends on claim 2, which has been determined to recite abstract ideas including mental processes. Therefore, claim 3 also recites an abstract idea.)
Step 2A Prong Two
The system of claim 2, wherein the handwriting recognition techniques analyze each of the received one or more tuples to identify the closest recognized term along with one or more alternative recognized terms that each of the received one or more tuples potentially represents.
(This step for identifying a closest recognized term with alternative terms does meaningfully integrate the abstract ideas of “recognizing a plurality of potential terms” and “building a knowledge graph” as it improves the accuracy of the system, as disclosed in the spec;
“The document 1 may be sent to the traditional knowledge graph building system 1204A which may be configured to recognize the handwritten word ‘Intuos’ and identifies ‘intros’ as the closest recognized word since it is better known in generally known English dictionaries. The traditional knowledge graph building system 1204A may be configured to add a node 1206A as ‘Intros’ in the knowledge graph 110 representing the document 1, such that if a user searches for ‘intros’ then the document 1 may be presented to the user. Therefore, the traditional knowledge graph building system 1204A lacks accuracy and is prone to give wrong results during searching.”)
However, the abstract idea of determining conceptual terms and a multi-level relation are still unintegrated, and are therefore still and issue under 35 USC § 101.
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered individually and in combination, they do not add significantly more (also known as an inventive concept) to the exception. The claim recites mental processes such as determining conceptual terms and a multi-level relation without any technological improvement or inventive step.
Regarding claim 4,
Step 2A Prong One
(Claim 4 depends on claim 1, which has been determined to recite abstract ideas including mental processes. Therefore, claim 4 also recites an abstract idea.)
Step 2A Prong Two
The system of claim 1, wherein the concept building module is configured to perform a named entity linking on the plurality of potential terms to determine the corresponding one or more conceptual terms.
(This step further limits the process of performing the abstract idea of determining conceptual terms with no improvement, and therefore does not meaningfully integrate the abstract idea)
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered individually and in combination, they do not add significantly more (also known as an inventive concept) to the exception. The claim recites mental processes such while the additional elements of generically performing named entity linking are a well-understood, routine, and conventional activity, as recognized by the court decisions listed in MPEP § 2106.05(d).
Regarding claim 5,
Step 2A Prong One
(Claim 5 depends on claim 1, which has been determined to recite abstract ideas including mental processes. Therefore, claim 5 also recites an abstract idea.)
Step 2A Prong Two
The system of claim 1, wherein the one or more conceptual terms include at least one of: terms corresponding to the recognized text in one or more languages, one or more synonym terms corresponding to the recognized text, one or more abbreviation terms corresponding to the recognized text, or one or more internally defined terms corresponding to the recognized text.
(This step for selecting a particular data type is considered insignificant extra-solution activity. See MPEP § 2106.05(g) ).
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered individually and in combination, they do not add significantly more (also known as an inventive concept) to the exception. The claim recites mental processes while the additional elements of defining data is a well-understood, routine, and conventional activity, as recognized by the court decisions listed in MPEP § 2106.05(d).
Regarding claim 6,
Step 2A Prong One
(Claim 6 depends on claim 1, which has been determined to recite abstract ideas including mental processes. Therefore, claim 6 also recites an abstract idea.)
Step 2A Prong Two
The system of claim 1, wherein each of the plurality of potential terms along with the one or more corresponding conceptual terms are placed as a node in the built knowledge graph, such that one node is connected to another node based on the determined multi-level relation between the corresponding potential recognized terms and the handwritten document.
(This step for representing the terms and conceptual terms as nodes in the knowledge graph based on the multi-level relation offers no improvement and therefore does not meaningfully integrate the abstract idea)
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered individually and in combination, they do not add significantly more (also known as an inventive concept) to the exception. The claim recites mental processes while the additional elements of representing data based on relations as nodes in a knowledge graph is a well-understood, routine, and conventional activity, as recognized by the court decisions listed in MPEP § 2106.05(d).
Regarding claim 7
Step 2A Prong One
(Claim 7 depends on claim 1, which has been determined to recite abstract ideas including mental processes. Therefore, claim 7 also recites an abstract idea.)
Step 2A Prong Two
The system of claim 1, wherein the knowledge graph building module is further configured to facilitate the user to set visibility of newly added nodes and their relationships in the knowledge graph.
(This step for allowing the user to alter the visibility of nodes of the nodes in the knowledge graph is considered extra-solution activity. See MPEP § 2106.05(g) )
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered individually and in combination, they do not add significantly more (also known as an inventive concept) to the exception. The claim recites mental processes while the additional elements of generically altering visibility of knowledge graph nodes are a well-understood, routine, and conventional activity, as recognized by the court decisions listed in MPEP § 2106.05(d).
Regarding claim 8,
Step 2A Prong One
(Claim 8 depends on claim 1, which has been determined to recite abstract ideas including mental processes. Therefore, claim 8 also recites an abstract idea.)
Step 2A Prong Two
The system of claim 1, wherein the knowledge graph building module is further configured to automatically set visibility of newly added nodes and their relationships in the knowledge graph based on historical visibilities of nodes and their relationships.
(This step for automatically altering the visibility of nodes of the nodes in the knowledge graph based on data is considered extra-solution activity. See MPEP § 2106.05(g) )
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered individually and in combination, they do not add significantly more (also known as an inventive concept) to the exception. The claim recites mental processes while the additional elements of generically altering visibility of knowledge graph nodes are a well-understood, routine, and conventional activity, as recognized by the court decisions listed in MPEP § 2106.05(d).
Regarding claim 9,
Step 2A Prong One
(Claim 9 depends on claim 8, which has been determined to recite abstract ideas including mental processes. Therefore, claim 9 also recites an abstract idea.)
Step 2A Prong Two
The system of claim 8, wherein based on the set visibility, the one or more nodes and their relationships are divided into at least one of: one or more public nodes and relationships corresponding to the documents publicly available to each user, one or more shared nodes and relationships corresponding to the documents on a subject to which the user is invited, and one or more private nodes and relationships corresponding to the documents that are specific to one user.
(This step for dividing nodes based on their determined visibility is considered extra-solution activity. See MPEP § 2106.05(g) )
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered individually and in combination, they do not add significantly more (also known as an inventive concept) to the exception. The claim recites mental processes while the additional elements of dividing nodes into groups based on a node property is a well-understood, routine, and conventional activity, as recognized by the court decisions listed in MPEP § 2106.05(d).
Claim 10 is a method claim corresponding directly to claim 1, and is likewise deficient.
Claim 11 is a method claim corresponding directly to claim 3, and is likewise deficient.
Claim 12 is a method claim corresponding directly to claim 4, and is likewise deficient.
Claim 13 is a method claim corresponding directly to claim 6, and is likewise deficient.
Claim 14 is a method claim corresponding directly to claim 7, and is likewise deficient.
Claim 15 is a method claim corresponding directly to claim 8, and is likewise deficient.
Regarding claim 17,
Step 2A Prong One
A semantic searching system for searching handwritten documents using a knowledge graph, the semantic searching system comprising: a receiver module configured to receive, from an electronic device, text data having one or more terms associated with a user’s intended search;
(This step for receiving text data from a device is considered a mental process)
an entity recognition module configured to perform entity recognition from the text data to determine one or more entities present in the text data;
(This step for recognizing entities in text data is considered a mental process)
a concept determination module configured to determine one or more conceptual terms for each of the determined one or more entities via a named entity linking;
(This step for determining conceptual terms for determined entities is considered a mental process)
an activation graph creation module configured to create an activation graph based on the determined one or more conceptual terms by adding nodes and their relationships corresponding to at least of: terms corresponding to the recognized entity in one or more languages, one or more synonym terms corresponding to the recognized entity, one or more abbreviation terms corresponding to the recognized entity, or one or more internally defined terms corresponding to the recognized entity;
(This step for creating an activation graph by adding nodes and relationships corresponding to an entity is considered a mental process as it can conceivably be performed with pen and paper)
an associative searching module configured to perform an associated searching for obtaining one or more search results based on matching of the one or more nodes of the activation graph with one or more nodes of the knowledge graph;
(This step for matching nodes of the activation and knowledge graph to obtain search results is considered a mental process)
Step 2A Prong Two
and a rendering module configured to render ranked and selected search results to the user, wherein the one or more ranked and selected search results include at least one of: shortcuts to open a handwritten document associated with the search results, or online links associated with the search results.
(This step for displaying results to a user is considered insignificant extra-solution activity. See MPEP § 2106.05(g) )
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered individually and in combination, they do not add significantly more (also known as an inventive concept) to the exception. The claim recites mental processes such while the additional elements of displaying search results to a user is a well-understood, routine, and conventional activity, as recognized by the court decisions listed in MPEP § 2106.05(d).
Regarding claim 17,
Step 2A Prong One
(Claim 17 depends on claim 16, which has been determined to recite abstract ideas including mental processes. Therefore, claim 17 also recites an abstract idea.)
Step 2A Prong Two
The semantic searching system of claim 16, wherein the associative searching module selects the search results based on an accessibility level of the user and visibility level of the one or more nodes and their relationships.
(This step for limiting what factors influence search results is without improvement and therefore does not integrate the abstract idea into a practical application)
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered individually and in combination, they do not add significantly more (also known as an inventive concept) to the exception. The claim recites mental processes such while the additional elements of providing search results based on node properties are a well-understood, routine, and conventional activity, as recognized by the court decisions listed in MPEP § 2106.05(d).
Regarding claim 18,
Step 2A Prong One
(Claim 18 depends on claim 16, which has been determined to recite abstract ideas including mental processes. Therefore, claim 18 also recites an abstract idea.)
Step 2A Prong Two
The semantic searching system of claim 16, wherein the visibility level of the one or more nodes and their relationships is at least one of: automatically defined based on historical visibilities of nodes and their relationships, or manually defined based on user inputs in a documents database.
(This step for choosing how node visibility is determined does not improve the technology and does not meaningfully integrate any of the abstract ideas)
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered individually and in combination, they do not add significantly more (also known as an inventive concept) to the exception. The claim recites mental processes such while the additional elements of determining methods for selecting visibility levels of nodes are a well-understood, routine, and conventional activity, as recognized by the court decisions listed in MPEP § 2106.05(d).
Regarding claim 19,
Step 2A Prong One
(Claim 19 depends on claim 18, which has been determined to recite abstract ideas including mental processes. Therefore, claim 19 also recites an abstract idea.)
Step 2A Prong Two
The semantic searching system of claim 18, wherein based on the pre-defined visibility, the documents database includes at least: one or more public documents corresponding to the documents publicly available to each user, one or more shared documents corresponding to the documents on a subject to which the user is invited, and one or more private documents corresponding to the documents that are specific to one user.
(This step for defining data groupings is without improvement and therefore does not meaningfully integrate the abstract idea)
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered individually and in combination, they do not add significantly more (also known as an inventive concept) to the exception. The claim recites mental processes while the additional elements of grouping data based on data characteristics is a well-understood, routine, and conventional activity, as recognized by the court decisions listed in MPEP § 2106.05(d).
Regarding claim 20,
Step 2A Prong One
(Claim 20 depends on claim 18, which has been determined to recite abstract ideas including mental processes. Therefore, claim 20 also recites an abstract idea.)
Step 2A Prong Two
The semantic searching system of claim 16, which further comprises a direct searching module configured to: pre-process the received text data by performing at least one of: tokenization, removal of stop words, removal of punctuation marks, or removal of spaces; and perform a direct searching by matching the pre-processed received text data with one or more nodes of the comprehensive knowledge graph for obtaining the one or more search results.
(This step for pre-processing data then searching using a knowledge graph is considered insignificant extra-solution activity. See MPEP § 2106.05(g) )
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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.
Claim(s) 1-6, 10-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vuang M. Ngo et al (hereinafter Ngo) (“A Semantic Search Engine for Historical Handwritten Document Images”, 2021) in view of Assadollahi, Ramin et al (Hereinafter Ramin) (EP 2 088 536 A1, 2009/12/08).
Regarding claim 1, Ngo teaches;
a concept building module configured to:
determine one or more conceptual terms from one or more
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NOTE: The above image teaches determining one or more conceptual terms from one or more recognized terms of the handwritten document.
and determine a multi-level relation between one or more
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NOTE: The above image teaches determining a multi-level relation (a knowledge graph with multiple levels) between one or more recognized terms, and the handwritten document (the terms in this graph are the recognized terms from the handwritten document).
([pg. 3] The KG collects structured data from various historical sources. Part of the data is manually curated by historians through spreadsheets. Other data sources (e.g. geographical data from OSi (Debruyne et al. 2017)) are imported automatically as RDF for direct insertion into KG. The schema (or ontology) used to structure KG, is mainly based on the popular CIDOC-CRM ontology (Doerr 2003). A short excerpt of KG is depicted in Figure 2. It shows a few main entities and relationships related to a person (of type CIDOC-CRM:E21 Person) named \William Sutton", who was member of a few relevant o_ces in Ireland.)
and a knowledge graph building module configured to build a knowledge graph, wherein the knowledge graph is built based at least on one of: the plurality of potential terms, the one or more conceptual terms, the determined multi-level relation between the one or more potential recognized terms and the handwritten document,
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NOTE: The excerpt teaches a knowledge graph building module configured to build a knowledge graph (methodologies for inserting and structuring data into a knowledge graph) wherein the knowledge graph is built based at least on the one or more conceptual terms, as shown in the above image.
wherein the knowledge graph is used to enable at least a semantic searching of one or more handwritten documents.
([pg. 4] We proposed a novel semantic full-text search system for images of historical handwritten manuscripts. Unlike the existing approach only using KW extracted from images, we exploited NE, KW and KG of increase search performance.)
NOTE: The knowledge graph is used to enable a semantic searching of one or more handwritten documents.
Ngo fails to teach but Ramin teaches;
a receiver module configured to receive a handwritten document along with dynamic handwritten data from an electronic device;
([0035] A character handwriting recognition component 330 receives handwriting input data from a touch sensitive input field 340, e.g. a touch screen.
[0036] The character handwriting recognition component 330 uses information what pixels (dots) were set on the touch sensitive input field 340 in what order or roughly speaking the time information available in the sequence of input samples. Therefore, the recognition component also involves the recording of the pixels activated by the user when writing on the touch sensitive input field 340 via a stylus or finger.)
NOTE: Teaches receiving dynamic handwritten data (handwriting input) from an electronic device (touch sensitive input field)
([0026] The character handwriting recognition component may comprise handwriting input data processing means for receiving a sequence of 2-dimensional input data from the touch sensitive input field (i.e. the dots activated by the user when writing on the input field) )
NOTE: Teaches the receiving module configured to additionally receive a handwritten document (2D handwriting input data is considered a handwritten document).
a recognition module configured to recognize a plurality of potential terms for one or more objects in the handwritten document by employing a handwriting recognition technique,
([Abstract] The present invention relates to a text input system and method involving finger-_based handwriting recognition and word prediction. A text input device (300) comprises: a text prediction component (310) for predicting a plurality of follow-up words based on a text context, the text prediction component (310) outputting a set of candidate words; a character handwriting recognition component (330) for recognizing a handwritten character candidate, the handwritten character candidate being determined based upon handwriting input received from a touch sensitive input field (340); a candidate word filtering component (350) for filtering the set of candidate words received from the text prediction component (310) based on the recognized handwritten character candidate;)
NOTE: Teaches a recognition module configured to recognize a plurality of potential terms (set of candidate words) for one or more objects in the handwritten document by employing a handwriting recognition technique (the final set of candidate words is based on the recognized handwritten character candidate).
wherein the plurality of potential terms for each of the one or more objects includes a closest recognized term and at least one alternative recognized term;
([0021] Preferably, the text prediction component outputs a ranked list of input candidate words according to respective scores that indicate the likelihood or probability of a candidate word to follow the text context.)
NOTE: Teaches wherein the plurality of potential terms for each of the one or more objects (aforementioned candidate words) includes a closest recognized term (term with the highest score) and at least one alternative recognized term (outputs a list of ranked words, indicating multiple alternative recognized terms having lower scores)
OBVIOUSNESS TO COMBINE RAMIN WITH NGO:
Ngo and Ramin are analogous art to each other and the present disclosure as they all pertain to methods for parsing and processing handwritten data. Specifically, Ngo pertains to a method of semantic searching for handwritten documents using a knowledge graph while Ramin pertains to methods of handwriting recognition.
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Additionally, Ngo recites the need for a handwriting text recognition module to be utilized in the system of their disclosure to digitize the handwritten document for further processing.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use the receiver and recognition module taught by Ramin to digitize and parse the handwritten document to allow the recognized terms of the plurality of potential terms to be processed as taught by Ngo.
Regarding claim 2, Ngo in view of Ramin teaches;
The system of claim 1,
(Using the same reasoning as claim 1)
Ngo fails to teach but Ramin teaches;
wherein the dynamic handwritten data is received in the form of one or more tuples…
([0026] The character handwriting recognition component may comprise handwriting input data processing means for receiving a sequence of 2-dimensional input data from the touch sensitive input field (i.e. the dots activated by the user when writing on the input field). The input data may be arranged in a one-dimensional input layer of the neural network.)
NOTE: Teaches the dynamic handwritten data is received (handwriting input data) in the form of one or more tuples (received in the form of an ordered list of x, y coordinates)
…having at least one of: data on x-axis, data on y-axis, pressure, speed of writing, or orientation.
([0052] The values of the re-sampled dots are arranged in a one-dimensional array such that the two position values of a dot are put side by side and dots are put side by side. This results in a data representation as: x1, y1, x2, y2, x3, y3, ..., where x1 is the first (offset compensated, normalized, re-sampled) sample value in x-direction, y1 is the first sample value in y-direction, x2 the second sample value in x-direction, etc.)
NOTE: Teaches the tuples (the input data is resampled into the recited one-dim array) having at least data on x-axis and y-axis (x1, y1, x2, y2, etc.)
Regarding claim 3, Ngo in view of Ramin teaches;
The system of claim 2,
(Using the same reasoning as the claim 2 rejection)
Ngo fails to teach but Ramin teaches;
wherein the handwriting recognition techniques analyze each of the received one or more tuples to identify the closest recognized term along with one or more alternative recognized terms that each of the received one or more tuples potentially represents.
([0021] Preferably, the text prediction component outputs a ranked list of input candidate words according to respective scores that indicate the likelihood or probability of a candidate word to follow the text context. This allows the word presentation component to present, e.g. on the display device, at least one candidate word from the filtered list of candidate words in accordance with its respective score. For instance, the candidate words are presented in descending order of probabilities so that the most likely candidate is presented in a first position of a list, the second most likely candidate in a second position, and the least likely candidate that matches the input character_(s) is arranged in last position of the list. Preferably, the list of candidate words is presented so that the user can easily select the most likely words, e.g. without scrolling. Less likely words may initially not be presented on the display, but may be displayed when the user scrolls down the list. This helps to select likely words and reduces the average number of interactions required by the user to select the intended word.)
NOTE: Teaches wherein the handwriting recognition techniques analyze each of the received one or more tuples to identify the closest recognized term (determine scores indicating the most likely [closest] term) along with one or more alternative recognized terms (includes second most likely as an alternative for the user to select) that each of the received one or more tuples potentially represents (the candidate words represent what the input data [which has already been determined to be tuples] potentially represents).
Regarding claim 4, Ngo teaches;
The system of claim 1,
wherein the concept building module is configured to perform a named entity linking on the
([pg. 2] Transkribus (Kahle et al. 2017) is used for training and deploying handwritten Text Recognition (HTR) models to derive text transcription from image scans. Given the rate at which transcriptions can be generated, NE Recognition (NER) and Entity Linking (EL) are required to automated annotate all instances of entities occurring in the transcription text. We used SpaCy (Honnibal et al. 2020) for NER and had highly results on 18th century English text. To provide flexibility, an NLP pipeline has been implemented as a thin layer over a number of standard NLP tools. The output of the pipeline is a NLP Interchange Format (Hellmann et al. 2013) in which a NER tool has annotated classes of entities and, where possible, an EL tool has connected the recognized entities to KG.)
NOTE: Teaches the concept building module configured to perform named entity linking on the terms to determine the one or more conceptual terms (uses named entity recognition to recognize the terms, and entity linking and entity linking to link entities to related conceptual terms in the knowledge graph).
Using the same reasoning to combine Ngo and Ramin in claim 1, is would be obvious to perform named entity linking as taught by Ngo on the plurality of potential terms as taught by Ramin.
Regarding claim 5, Ngo teaches;
The system of claim 1, wherein the one or more conceptual terms include at least one of:
terms corresponding to the recognized text in one or more languages, one or more synonym terms corresponding to the recognized text, one or more abbreviation terms corresponding to the recognized text, or one or more internally defined terms corresponding to the recognized text.
[pg. 3]
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NOTE: Teaches the conceptual terms including at least terms corresponding to the recognized text (internally defined terms include the occupation, title, etc. corresponding to the recognized entity [William Sutton] )
Ngo fails to teach but Ramin teaches;
plurality of potential terms
(Using the same teaching and combination reasoning from claim 1)
Regarding claim 6, Ngo teaches;
The system of claim 1,
wherein each of the
[pg. 3]
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NOTE: Teaches each of the terms (William, Sutton, etc.) along with the one or more corresponding conceptual terms (Surname, Male, etc.) are placed as a node in the built knowledge graph, such that one node is connected to another node (nodes have connections) based on the determined multi-level relation (the ‘surname’ and ‘name’ nodes are connected to Sutton_William by a degree of 2, therefore the relation is multi-level) between the corresponding recognized terms and the handwritten document (the terms in the pictured knowledge graph are the same recognized terms from the handwritten document).
Ngo fails to teach but Ramin teaches;
plurality of potential terms
(Using the same teaching and combination reasoning from claim 1)
Regarding claim 10;
Claim 10 is a method claim directly corresponding to claim 1 and is rejected for the same reasons.
Regarding claim 11;
Claim 11 is a method claim directly corresponding to claim 3 and is rejected for the same reasons.
Regarding claim 12;
Claim 12 is a method claim directly corresponding to claim 4 and is rejected for the same reasons.
Regarding claim 13;
Claim 13 is a method claim directly corresponding to claim 6 and is rejected for the same reasons.
Claim(s) 7-9, 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ngo (“A Semantic Search Engine for Historical Handwritten Document Images”, 2021) in view of Ramin (EP 2 088 536 A1, 12/08/2009) further in view of Corodescu A et al, (hereinafter Corodescu) (US 20220398331 A1, 12/15/2022).
Regarding claim 7, Ngo in view of Ramin teaches;
The system of claim 1,
(Using the same reasoning from claim 1)
Ngo and Ramin fail to teach but Corodescu teaches;
wherein the knowledge graph building module is further configured to facilitate the user to set visibility of newly added nodes and their relationships in the knowledge graph.
([0019] The visibility policy may be enforced against properties of knowledge-graph objects, which include nodes and edges.)
NOTE: Teaches that the nodes and relationships (edges) of a knowledge graph have a set visibility.
([0027] The visibility-policy collection system provides an interface through which users may specify their visibility preferences for the properties of an object. The preferences may be used to form a visibility policy for the object. The ability to create or edit a visibility policy may be governed at different levels in the system. For example, a user with full access to a node (e.g., document, file) may be able to edit the property visibility profile. A visibility record may comprise properties governed and a user or group of users with visibility to the property.)
NOTE: Teaches facilitating the user to set the visibility of newly added nodes and their relationships (users can edit visibility properties for knowledge graph objects, this could be any object in the KG, which includes newly added nodes and relationships)
OBVIOUSNESS TO COMBINE CORODESCU WITH NGO AND RAMIN:
Corodescu is analogous art to the present disclosure and Ngo as it pertains to knowledge graphs, and is analogous to Ramin as it pertains to data processing. Specifically, Corodescu pertains to a knowledge graph representation with visibility properties for knowledge graph objects.
Additionally, Corodescu states;
([Abstract] The technology described herein protects the privacy and security of data stored in a knowledge graph (“graph”) by enforcing visibility policies when returning property information in response to a query or other attempt to extract property information from the graph and/or about the graph.)
NOTE: This discloses that by enforcing visibility properties on knowledge graph objects, it allows protecting certain portions of the data from un-authorized data.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to implement the visibility properties taught by Corodescu to the objects of the generated knowledge graph of claim 1 to protect sensitive information contained in the handwritten data of the present disclosure.
Regarding claim 8, Ngo in view of Ramin teaches;
The system of claim 1,
(Using the same reasoning from claim 1)
Ngo and Ramin fail to teach but Corodescu teaches;
wherein the knowledge graph building module is further configured to automatically set visibility of newly added nodes and their relationships in the knowledge graph based on historical visibilities of nodes and their relationships.
([0020] A visibility policy may govern read-access to a property of a knowledge-graph object. A visibility policy with a default-restricted visibility may restrict access to all users, except those designated in the policy as having access. Alternatively, a visibility policy with a default-unrestricted visibility may grant access to all users, except those designated as not having access. The technology described herein may work with both default statuses.)
NOTE: Teaches automatically setting visibility of newly added nodes and their relationships (default visibility will automatically be applied to new objects, which includes nodes and their relationships, as previously mentioned) in the KG based on historical visibility of nodes and relationships (the visibility of new nodes is based on the rule which defines the visibility of historical nodes and their relationships, and is therefore itself based on the historical visibilities of nodes and their relationships).
Using the same reasoning from claim 7, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to implement the visibility properties taught by Corodescu to the objects of the generated knowledge graph of claim 1 to protect sensitive information contained in the handwritten data.
Regarding claim 9, Ngo in view of Ramin further in view of Corodescu teach;
The system of claim 8,
Ngo and Ramin fail to teach but Corodescu teaches;
wherein based on the set visibility, the one or more nodes and their relationships are divided into at least one of:
one or more public nodes and relationships corresponding to the documents publicly available to each user, one or more shared nodes and relationships corresponding to the documents on a subject to which the user is invited, and one or more private nodes and relationships corresponding to the documents that are specific to one user.
([0020] A visibility policy may govern read-access to a property of a knowledge-graph object. A visibility policy with a default-restricted visibility may restrict access to all users, except those designated in the policy as having access. Alternatively, a visibility policy with a default-unrestricted visibility may grant access to all users, except those designated as not having access. The technology described herein may work with both default statuses.)
NOTE: Teaches dividing the one or more nodes and their relationships [knowledge graph objects] into at least one or more shared nodes and relationships corresponding to the documents on a subject to which the user is invited (a group of objects pertaining to a subject can have the same visibility policy to allow only authorized users)
Regarding claim 14;
Claim 14 is a method claim directly corresponding to claim 7, and is therefore rejected for the same reasons.
Regarding claim 15;
Claim 15 is a method claim directly corresponding to claim 8, and is therefore rejected for the same reasons.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ngo (“A Semantic Search Engine for Historical Handwritten Document Images”, 2021) in view of Yang C et al. (Hereinafter Yang) (CN 112148851 A, 12/29/2020).
Regarding claim 16, Ngo teaches;
A semantic searching system for searching handwritten documents using a knowledge graph, the semantic searching system comprising:
a receiver module configured to receive, from an electronic device, text data having one or more terms associated with a user’s intended search;
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NOTE: Teaches a receiver module (user interface) configured to receive, from an electronic device, text data having one or more terms associated with the user’s intended search (in the search text box, we can see the terms Meath, silver, and shilling)
an entity recognition module configured to perform entity recognition from the text data to determine one or more entities present in the text data;
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([pg. 3] Figure 3 {above} presents an image of a handwritten medieval historical manuscript, its transcription and its concept set d, applied in the model. In the transcription, there are three kinds of words determined by our NER tool: (1) stop-words being the, to, of, we and you; (2) NEs being sheriff, Meath, clerk and William Sutton; and (3) KWs being king, &c, greeting, direct, pay, shilling and silver. The stop-words are not added into the concept set d.)
NOTE: Teaches an entity recognition module configured to perform entity recognition (NER here means ‘Named Entity Recognition’) from the text data to determine one or more entities present in the text data (some of the entities they determine include king, occu-sherrif, etc.).
a concept determination module configured to determine one or more conceptual terms for each of the determined one or more entities via a named entity linking;
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([pg. 2] The output of the pipeline is a NLP Interchange Format (Hellmann et al. 2013) in which a NER tool has annotated classes of entities and, where possible, an EL tool has connected the recognized entities to KG.)
NOTE: Teaches a concept determination module configured to determine one or more conceptual terms (male, surname, etc.) for each of the determined one or more entities (William_Sutton) via named entity linking (in Ngo, named entity recognition [NER] determines the entities in the text and entity linking [EL] is used to determine the conceptual terms connected to each entity in the knowledge graph [KG], as depicted in the above image).
and a rendering module configured to render ranked and selected search results to the user,
([pg. 2] Finally, the KW-NE-Based IR Model module compares the annotated query and the annotated documents to return the ranked transcriptions and images.)
NOTE: Teaches a module configured to render (images are considered renderings) ranked and selected search results.
([Abstract] In the next steps, we apply the named entity recognition and historical knowledge graph to build a semantic search model, which can understand the user's intent in the query and the contextual meaning of concepts in documents, to return correctly the transcriptions and their corresponding images for users.)
NOTE: Teaches returning the ranked (as taught above) renderings of search results to users.
wherein the one or more ranked and selected search results include at least one of: shortcuts to open a handwritten document associated with the search results, or online links associated with the search results.
([Abstract] In the next steps, we apply the named entity recognition and historical knowledge graph to build a semantic search model, which can understand the user's intent in the query and the contextual meaning of concepts in documents, to return correctly the transcriptions and their corresponding images for users.)
NOTE: Returning transcripts and corresponding images to users is considered a shortcut to the handwritten document.
Ngo fails to teach but Yang teaches;
an activation graph creation module configured to create an activation graph based on the determined one or more conceptual terms by adding nodes and their relationships corresponding to at least of:
terms corresponding to the recognized entity in one or more languages, one or more synonym terms corresponding to the recognized entity, one or more abbreviation terms corresponding to the recognized entity, or one or more internally defined terms corresponding to the recognized entity;
([pg. 3] Responding to the medical knowledge question answering system on the server, firstly dividing the question sentence input by the user through the jieba word, identifying the naming entity and the entity relation in the question sentence, further combining the syntax dependent tree to convert the natural language question sentence into the semantic query graph;)
NOTE: Teaches creating the activation graph (the query graph taught by Yang is considered to be an activation graph as it activates additional conceptually relevant nodes, further explained later) by adding nodes and their relationships corresponding to at least one or more internally defined terms corresponding to the recognized entity (adds recognized entities from the query sentence with their internally defined corresponding entities [other recognized entities from the query sentence connected to the recognized entity via internally defined relations] ).
an associative searching module configured to perform an associated searching for obtaining one or more search results based on matching of the one or more nodes of the activation graph with one or more nodes of the knowledge graph;
([pg. 3]; using the sub-graph matching mode for answer retrieval;)
NOTE: Teaches associated searching module for obtaining one or more search results (the sub-graph matching mode is used for answer retrieval, thereby obtaining at least one search result)
([pg. 4] for each node in the semantic query graph, constructing the node candidate set matched with the semantic query graph in the existing medical knowledge graph; starting from the node candidate set; using dynamic programming method to traverse the medical knowledge map; finding the sub-graph most likely to match.)
NOTE: The subgraph matching mode explained here matches nodes of the activation graph (query graph) with the knowledge graph to obtain relevant nodes corresponding to the activation graph (query graph), which then spread to other conceptually related nodes (here, activation of other nodes is taught based on relation to other nodes, which therefore makes the query graph an activation graph) to obtain search results (the closest matching sub-graph).
This therefore teaches performing associative searching (via the sub-graph matching mode) for obtaining one or more search results (closest sub-graph) based on matching of the one or more nodes of the activation graph (query graph) with one or more nodes of the knowledge graph.
OBVIOUSNESS TO COMBINE YANG WITH NGO:
Yang and Ngo are analogous to each other and to the present disclosure as they all pertain to semantic searching utilizing a knowledge graph. Specifically, Yang pertains to answering a query utilizing a query graph and a knowledge graph.
Additionally, Ngo further states;
([Abstract] This paper proposes a semantic search engine for full-text retrieval of historical handwritten document images based on named entity (NE), keyword (KW) and knowledge graph (KG). This would help not only in processing, storing and indexing automatically, but also would allow users to access quickly and retrieve efficiently manuscripts.)
NOTE: Ngo discloses that semantic searching of manuscripts using their system which includes NE, KW, and KG is effective in terms of efficient processing, storing, and accessibility.
The disclosure of Yang already utilizes named entities, keywords, and knowledge graphs, so it would be simple to substitute the searching method utilized in the system disclosed by Ngo with the searching method disclosed by Yang.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use the method of knowledge graph searching disclosed by Yang to enable the implementation of the efficient semantic searching system disclosed by Ngo.
Claim(s) 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ngo (“A Semantic Search Engine for Historical Handwritten Document Images”, 2021) in view of Yang (CN 112148851 A, 12/29/2020) further in view of Corodescu (US 20220398331 A1, 12/15/2022).
Regarding claim 17, Ngo in view of Yang teach;
The semantic searching system of claim 16,
(Using the same reasoning from claim 16)
Ngo and Yang fail to teach but Corodescu teaches;
wherein the associative searching module selects the search results based on an accessibility level of the user and visibility level of the one or more nodes and their relationships.
([0005] The technology described herein protects the privacy and security of data stored in a knowledge graph (“graph”) by enforcing visibility policies when returning property information in response to a query or other attempt to extract property information from the graph and/or about the graph. The visibility policy may be enforced against properties of knowledge-graph objects, which include nodes and edges.)
NOTE: The visibility policy of the disclosure of Corodescu is enforced against nodes and edges (relationships) of the knowledge graph.
([0028] The visibility-policy enforcement system compares an information request to applicable visibility policies. In an aspect, a query may be submitted with a security token that may identify a requestor of the query. Depending on the result of the comparison, all property information responsive the query or a portion thereof may be provided. If a portion of the responsive property information is protected by a visibility policy, then that portion may be omitted from the response, and the portion of information that is not protected by the visibility policy may be output to the requesting entity.)
NOTE: Teaches selecting the search results based on an accessibility level of the user and visibility level of the one or more nodes and their relationships (as previously mentioned, the visibility policy of the disclosure of Corodescu is enforced against nodes and relationships of the knowledge graph).
OBVIOUSNESS TO COMBINE CORODESCU WITH NGO AND YANG:
Corodescu is analogous art to the present disclosure, Ngo, and Yang as they all pertain to systems utilizing knowledge graphs. Specifically, Corodescu pertains to a knowledge graph representation with visibility properties for knowledge graph objects.
Additionally, Corodescu states;
([Abstract] The technology described herein protects the privacy and security of data stored in a knowledge graph (“graph”) by enforcing visibility policies when returning property information in response to a query or other attempt to extract property information from the graph and/or about the graph.)
NOTE: This discloses that by enforcing visibility properties on knowledge graph objects, it allows protecting certain portions of the data from un-authorized users.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to implement the visibility properties taught by Corodescu to the objects of the semantic searching system of claim 16 to protect sensitive information contained in the handwritten data.
Regarding claim 18, Ngo in view of Yang teach;
The semantic searching system of claim 16,
(Using the same reasoning from claim 16)
Ngo and Yang fail to teach but Corodescu teaches;
wherein the visibility level of the one or more nodes and their relationships is at least one of: automatically defined based on historical visibilities of nodes and their relationships, or manually defined based on user inputs in a documents database.
([0019] The visibility policy may be enforced against properties of knowledge-graph objects, which include nodes and edges.)
NOTE: Teaches that the nodes and relationships (edges) of a knowledge graph have a set visibility.
([0027] The visibility-policy collection system provides an interface through which users may specify their visibility preferences for the properties of an object. The preferences may be used to form a visibility policy for the object. The ability to create or edit a visibility policy may be governed at different levels in the system. For example, a user with full access to a node (e.g., document, file) may be able to edit the property visibility profile. A visibility record may comprise properties governed and a user or group of users with visibility to the property.)
NOTE: Teaches facilitating the user to manually define the visibility of nodes and their relationships in the document data base (knowledge graph).
Regarding claim 19, Ngo in view of Yang further in view of Corodescu teach;
The semantic searching system of claim 18,
(Using the same reasoning from claim 18)
Ngo and Yang fail to teach but Corodescu teaches;
wherein based on the pre-defined visibility, the documents database includes at least: one or more public documents corresponding to the documents publicly available to each user, one or more shared documents corresponding to the documents on a subject to which the user is invited, and one or more private documents corresponding to the documents that are specific to one user.
([0020] A visibility policy may govern read-access to a property of a knowledge-graph object. A visibility policy with a default-restricted visibility may restrict access to all users, except those designated in the policy as having access. Alternatively, a visibility policy with a default-unrestricted visibility may grant access to all users, except those designated as not having access. The technology described herein may work with both default statuses.)
NOTE: Teaches the documents database (knowledge graph) including at least one or more shared documents (nodes and relationships) corresponding to the documents on a subject to which the user is invited (a group of objects pertaining to a subject can have the same visibility policy to allow only authorized users).
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ngo (“A Semantic Search Engine for Historical Handwritten Document Images”, 2021) in view of Yang (CN 112148851 A, 12/29/2020) further in view of Cheng Chen et al. (Hereinafter Cheng) (CN 115757738 A, 03/07/2023).
Regarding claim 20, Ngo in view of Yang teaches;
The semantic searching system of claim 16,
(Using the same reasoning from claim 16)
Ngo teaches;
which further comprises a direct searching module configured to: pre-process the received text data by performing at least one of: tokenization, removal of stop words, removal of punctuation marks, or removal of spaces;
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NOTE: Teaches preprocessing received text data by tokenization of search terms.
Ngo and Yang fail to teach but Cheng teaches;
and perform a direct searching by matching [taught above by Ngo] data with one or more nodes of the comprehensive knowledge graph for obtaining the one or more search results.
([pg. 5] The above-mentioned file search method based on a knowledge graph, wherein the knowledge graph described in step 1 is established by importing text data into a graph database created using neo4j)
NOTE: The disclosure of Cheng uses a knowledge graph for direct searching.
([pg. 5] in step 3, the two keywords in the keyword and its type data are recorded as word1 and word2, and the query statement is generated according to the following rules, and the The query statement is queried in the graph database to obtain the query results as result1 and result2 or result1, and the search word set list is generated according to the query result processing, and the rules are as follows: If word1 and word2 are both instance data types, then result1 is a collection of all node names that can generate a one-degree relationship between the node whose node name is word1 and its alias and the node whose name is word2, and result2 is the node whose node name is word2 and A collection of all node names whose alias and node name word1 can generate a one-degree relationship;)
NOTE: Teaches performing direct searching by matching the input words to nodes of the knowledge graph for obtaining one or more search results (word1 from the query is matched with a node from the knowledge graph having the name word1, to return result1 containing search results).
OBVIOUSNESS TO COMBINE CHENG WITH NGO AND YANG:
Cheng, Ngo, and Yang are all analogous to each other and the present disclosure as they all pertain to searching using knowledge graph. Specifically, Cheng pertains to a file searching method and system based on knowledge graph.
Additionally, Cheng states;
([pg. 4] Knowledge graph is a data storage method based on graph database, which builds a knowledge network through the node-relationship-node model. With the development of artificial intelligence technology, it has become a trend to use knowledge graph search to answer or solve problems; in actual work, files are often an important way to carry content, so it is very important to use knowledge graphs to quickly retrieve files)
NOTE: This discloses that knowledge graph-based searching is an efficient means of quickly retrieving files. The present disclosure pertains to retrieving handwritten files by a user query, so a graph-based searching method would be well suited for this task.
Also, using the pre-processed text data taught by Ngo in the searching method taught by Cheng would be a simple substitution of input data.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use the method of knowledge graph-based searching method taught by Cheng using the preprocessed input taught by Ngo in order to provide an efficient means of direct searching of the handwritten documents of the present disclosure.
CONCLUSION
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/MATTHEW ALAN CADY/ Examiner, Art Unit 2145
/CESAR B PAULA/ Supervisory Patent Examiner, Art Unit 2145