Notice of Pre-AIA or AIA Status
The present application 19/073,387, filed on 3/7/2025 (or after March 16, 2013), is being examined under the first inventor to file provisions of the AIA (First Inventor to File).
In the event the determination of the status of the application as subject to AIA 35
U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application is a CON of 17/539,992 filed on 12/01/2021 is now US PAT 12,260,663 17/539,992 has DOM PRO 63/120,626 filed on 12/02/2020
DETAILED ACTION
Claims 1-17 are pending in this application.
Examiner acknowledges applicant’s preliminary amendment filed on 6/11/2025
Drawings
The Drawings filed on 3/7/2025 are acceptable for examination purpose.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 7/1/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner
Priority
Acknowledgment is made of applicant’s claim for domestic priority application
U.S. Provisional Patent application serial number # 63/120,626 filed on 12/02/2020 under 35 U.S.C. 119 (e)
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application.
Claim 17:
Claim 17 is rejected under 35 USC 101 because invention is directed to non-statutory subject matter.
Claim 17 directed to “A computer-readable media comprising one or more physical computer- readable storage media having stored thereon computer-executable instructions that, when executed at a processor, cause a computer system to perform” covers both statutory as well as non- statutory embodiments. The instantf specification para 0106 disclosed “Computer-readable media that carry computer-executable instructions are transmission media. Thus, by way of example, and not limitation, embodiments of the invention can comprise at least two distinctly different kinds of computer-readable media: storage media and transmission media, directed to “signal/wave” media, typically covers forms of non-transitory tangible media and transitory propagating signals per se." -1351 OG 212).
The examiner suggests amending the claim 17 to cover only statutory embodiments by adding the li "non-transitory" to the claim 17
Claim 1-17 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance with the 2019 Revised Patent Subject Matter Eligibility Guidance, Federal Register (84 FR 50) on January 7, 2019 hereinafter 2019 PEG
Step 1. In accordance with Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is noted that the method of claim 1,16, directed to one of the eligible categories of subject matter and therefore satisfy Step 1.
Step 2A. In accordance with Step 2A prong one of the 2019 PEG, the limitations reciting the abstract idea are highlighted, and the limitations directed to additional elements are highlighted, as set forth in exemplary claim 1
A computing system comprising:
One or more processors; and
One or more computer-readable media having stored thereon computer-executable instructions that are structured such that, when executed by the one or more processors, cause the computing system to perform at least:
use hierarchical classification to classify a plurality of patent documents into a plurality of categories, wherein using hierarchical classification to classify the plurality of patent documents into the plurality of categories includes:
for each of the plurality of patent documents, performing the following:
parse textual information of the patent document using a natural language processing (NLP) engine,
based upon the parsed textual information, extracting a first set of features representing the parsed textual information of the patent document,
transform the first set of features into a first feature vector, the first feature vector being a vector having a plurality of dimensions, each of which corresponds to a value of a feature contained in the first set of features
,use hierarchical classification to classify the plurality of feature vectors corresponding to the plurality of patent documents into the plurality of categories, and
calculate a different category vector for each of the plurality of categories;
receive an input indicating a source patent, the source patent being a patent application or an issued patent published by one of one or more first data systems that publish patent documents;
determining a similarity between a source feature vector corresponding to the source patent and a particular category feature vector corresponding to a particular category;
identify a plurality of candidate patents within the particular category;
for each of the plurality of candidate patents, performing the following:
retrieve the candidate patent from the one or more first data systems,
parse textual information of the candidate patent document using the NLP engine,
based upon the parsed textual information, extracting a second set of features representing the parsed textual information of the candidate patent,
transform the second set of features into a second feature vector, the second feature vector being a vector having a plurality of dimensions, each of which corresponds to a value of a feature contained in the second set of features, and
determine a similarity between the source feature vector corresponding to the source patent and the second feature vector corresponding to the candidate patent;
based on the similarities between the source feature vector and each second feature vector, identifying one or more target patents; and
visualize the source patent and the identified one or more target patents”, claim 1,16-17, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, in the context of this claim, this limitation encompasses the user thinking identifying patents, parse textual, transform, determine similarity, identifying more target patents,
visualize source and target patents
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas set forth in the 2019 PEG. Accordingly, the claim recites an abstract idea.
With respect to Step 2A prong two of the 2019 PEG, the judicial exception is not integrated into a practical application. The additional elements are directed to method steps, however, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular data structure of gallery images collect(ion) that identify particular match, to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
The limitation “calculate a different category vector for each of the plurality of categories”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea, further, “calculate” step, other than reciting “by a processor”, general-purpose computing, nothing in the claim element precludes the step from practically being performed in the mind. Consistent with the specification as at specification para 0100-0114 of the instant specification fig 13, one can mentally calculate vector values of categories, in the context of this claim limitation encompasses the user manually supplying parameter values covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “mental processes”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Furthermore, although these elements have been fully considered, they are directed to the use of generic computing elements (para: 0100-0114 of the instant specification make it clear that the disclosed functionality is implemented on well-known computing systems and general purpose computing devices) to perform the abstract idea, which is not sufficient to amount to a practical application (as noted in the 2019 PEG) and is amount to simply saying "apply it" using a general purpose computer, which merely serves to tie the abstract idea to a particular technological environment computer based operating environment) by using the computer as a tool to perform the abstract idea.
Since the analysis of Step 2A prong one and prong two results in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
Step 2B. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional method limitations are directed to a generic computer, at a very high level of generality and without imposing meaningful limitations on the scope of the claim. In addition para: 0100-0114 of the instant specification describe generic off-the-shelf computer-based elements for implementing the claimed invention which does not amount to significantly more than the abstract idea and is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. Further, See, e.g., Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94 (Fed. Cir. 2015) ("Just as Diehr could not save the claims in Alice, which were directed to 'implement[ing] the abstract idea of intermediated settlement on a generic computer', it cannot save O/P's claims directed to implementing the abstract idea of price optimization on a generic computer.") (citations omitted). See also, Affinity Labs of Texas LLC v. DirecTV LLC, 838 F.3d 1253, 1257-1258 (Fed. Cir. 2016) (mere recitation of a GUI does not make a claim patent-eligible); Intellectual Ventures I LLC v. Capital One Bank, 792 F.3d 1363, 1370 (Fed. Cir. 2015) ("the interactive interface limitation is a generic computer element".)
The additional elements are broadly applied to the abstract idea at a high level of generality ("similar to how the recitation of the computer in the claims in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer,") as explained in MPEP § 2106.05(f)) and they operate in a well-understood, routine, and conventional manner.
MPEP § 2106.05 (d)(II) sets forth the following:
The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g. at a high level of generality) as insignificant extra-solution activity.
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec...; TLI Communications LLC v. AV Auto. LLC...; OIP Techs., Inc., v. Amazon.com, Inc... ; buySAFE, Inc. v. Google, Inc...;
Performing repetitive calculations, Flook ... ; Bancorp Services v. Sun Life...;
Electronic recordkeeping, Alice Corp...; Ultramercial... ;
Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc...;
Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank...; and
A web browser's back and forward button functionality, Internet Patent Corp. v. Active Network, Inc...
Courts have held computer-implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amount to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking).
As to claim 2, further elaborates “wherein when the input indicates a court case,
retrieving a court document associated with the court case from one of one or more second data systems that publish court documents;
parsing textual information of the court document using the NLP engine; and
based upon the parsed textual information of the court document, identifying that a patent is associated with the court document and treating the patent as the source patent”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 3, further elaborates “the computing system further configured to parse the input of the source patent or the court case to determine which one of the one or more first data systems or which one of one or more second data system contains the source patent or the court document associated with the court case”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 4, further elaborates “wherein identifying one or more target patents includes:
when a similarity between the source feature vector corresponding to the source patent and a second feature vector corresponding to a candidate patent is greater than a predetermined threshold, determining that the candidate patent is a target patent”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 5, further elaborates “wherein determining whether the candidate patent is a target patent includes:
identifying a predetermined number of candidate patents that correspond to second feature vectors that have highest similarities to the source feature vector as target patents”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 6, further elaborates “wherein identifying the plurality of candidate patents includes at least one of:
identifying a plurality of patents that have an application date within a predetermined period; or
identifying a plurality of patents that have an assignee that is within a list of predetermined entities”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 7, further elaborates “wherein:
parsing textual information of the source patent using a NLP engine includes identifying a list of keywords associated with the source patent; and
identifying a plurality of patents that contain the list of keywords as the plurality of candidate patents”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 8, further elaborates “wherein:
parsing textual information of the source patent document using a NLP engine includes identifying at least one of (1) an international class, (2) a U.S. class, or (3) a field of search of the source patent; and
identifying the plurality of candidate patents includes identifying a plurality of patents that share at least one of (1) the international class, (2) a U.S. class, or (3) the field of search as the plurality of candidate patents”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 9, further elaborates” wherein identifying a plurality of candidate patents comprises:
determining that the source patent belongs to one of a plurality of categories in a machine learning generated taxonomy system; and
identifying a plurality of patents that belong to the one of the plurality of categories in the taxonomy system as the plurality of candidate patents”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 10, further elaborates “wherein the computing system is further caused to use machine learning to analyze and classify a plurality of patent documents in the one or more first data systems into the plurality of categories to generate the taxonomy system”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 11, further elaborates “the computing system further caused to:
identify a patent document in one of the one or more first data systems that is not categorized in the taxonomy system;
retrieve the patent document from the one of the one or more first data systems;
use the natural language engine to parse textual information from the retrieved patent;
extract a third set of features of the patent document from the textual information of the patent document;
transform the third set of features into a third feature vector, the third feature vector being a vector having a plurality of dimensions, each of which corresponds to a value of a feature contained in the third set of features;
compare the third feature vector corresponding to the patent document with each category feature vector to determine a similarity; and
assign the patent document to a category corresponding to a category feature vector that has a highest similarity to the third feature vector corresponding to the patent document”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 12, further elaborates “wherein:
parsing textual information of a patent document further includes identifying at least one of: (1) textual information of a title of the patent, (2) textual information of an abstract section of the patent, or (3) textual information of a claim section of the patent document, and
each of the first set of features, the second set of features, or the third set of features is extracted based on the identified (1) textual information of the title of the patent, (2) textual information of the abstract section of the patent, or (3) textual information of the claim section of the patent”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 13, further elaborates “wherein: parsing textual information of the source patent further includes identifying textual information of a cross-reference section of the source patent, and
based on the textual information of the cross-reference section of the source patent document, the computing system is further caused to:
identify a second source patent that is contained in the cross-reference section of the source patent and published by the first data system or a second data system;
retrieve a second source patent document associated with the second source patent from one of the one or more first data systems;
parse textual information of the second source patent using a natural language processing (NLP) engine;
based upon the parsed textual information, extract a fourth set of features representing the parsed textual information of the second source patent;
transform the fourth set of features into a fourth feature vector, the fourth feature vector having a plurality of dimensions, each of which corresponds to a value of a feature contained in the fourth set of features;
identify a plurality of second candidate patents based on the second source patent;
for each of the plurality of second candidate patents,
retrieve a second candidate patent document associated with the second candidate patent from one of the one or more first data systems;
parse textual information within the second candidate patent using the natural language processing engine;
based upon the parsed textual information, generating a fifth set of features representing the parsed textual information with the second candidate patent;
transform the fifth set of features into a fifth feature vector, the fifth feature vector being a vector having a plurality of dimensions, each of which corresponds to a value of a feature contained in the fifth set of features; and
determine a similarity between the fourth feature vector and the fifth feature vector;
based on the similarity between the fourth feature vector and each fifth feature vector, identify one or more second target patents, and
visualize the second source patent and the one or more second target patents”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 14, further elaborates “wherein parsing textual information of the source patent document further includes identifying textual information of cited references during prosecution of the source patent, and the computing system is further caused to :
identify one or more patents contained in the cited references as one or more third target patents;
for each of the one or more third target patents,
retrieve a third target patent document associated with the third target patent from one of the one or more first data systems;
parse textual information of the third target patent using the NPL engine;
based upon the parsed textual information, generating a sixth set of features representing the parsed textual information of the third target patent;
transform the sixth set of features into a sixth feature vector, the sixth feature vector being a vector having a plurality of dimensions, each of which corresponds to a value of a feature contained in the sixth set of features; and
determine a similarity between the first feature vector and the sixth feature vector; and
visualize the source patent and the one or more third target patents”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 15, further elaborates “the computing system is further caused to:
receive a user input accepting or rejecting the determined target patents; and
in response to a user input,
remove the rejected target patent from the visualization; and update classification of the rejected target patent in the taxonomy system”, which have been determined to be extra-solution activity that does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(b)(I). Even in combination, the additional details recited in these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 of US Application No. 19/073,387 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-13 of U.S. Patent No. 12,260,663. Although the claims at issue are not identical, they are not patentably distinct from each other because the patented claims perform the same steps as the claims in the instant application.
Instant US application: 19/073,387
US Patent No. 12,260,663
Claim 1,16-17, A computing system comprising:
One or more processors; and
One or more computer-readable media having stored thereon computer-executable instructions that are structured such that, when executed by the one or more processors, cause the computing system to perform at least:
use hierarchical classification to classify a plurality of patent documents into a plurality of categories, wherein using hierarchical classification to classify the plurality of patent documents into the plurality of categories includes:
for each of the plurality of patent documents, performing the following:
parse textual information of the patent document using a natural language processing (NLP) engine,
based upon the parsed textual information, extracting a first set of features representing the parsed textual information of the patent document,
transform the first set of features into a first feature vector, the first feature vector being a vector having a plurality of dimensions, each of which corresponds to a value of a feature contained in the first set of features
,use hierarchical classification to classify the plurality of feature vectors corresponding to the plurality of patent documents into the plurality of categories, and
calculate a different category vector for each of the plurality of categories;
receive an input indicating a source patent, the source patent being a patent application or an issued patent published by one of one or more first data systems that publish patent documents;
determining a similarity between a source feature vector corresponding to the source patent and a particular category feature vector corresponding to a particular category;
identify a plurality of candidate patents within the particular category;
for each of the plurality of candidate patents, performing the following:
retrieve the candidate patent from the one or more first data systems,
parse textual information of the candidate patent document using the NLP engine,
based upon the parsed textual information, extracting a second set of features representing the parsed textual information of the candidate patent,
transform the second set of features into a second feature vector, the second feature vector being a vector having a plurality of dimensions, each of which corresponds to a value of a feature contained in the second set of features, and
determine a similarity between the source feature vector corresponding to the source patent and the second feature vector corresponding to the candidate patent;
based on the similarities between the source feature vector and each second feature vector, identifying one or more target patents; and
visualize the source patent and the identified one or more target patents”,
claim 2:
claim 3:
claim 4
claim 5
claim 6:
claim 7:
claim 8
Claim 1,11, A computing system comprising:
one or more processors; and
one or more computer-readable media having stored thereon computer-executable instructions that are structured such that, when executed by the one or more processors, cause the computing system to perform at least:
use hierarchical classification to classify a plurality of patent documents into a plurality of categories, wherein using hierarchical classification to classify the plurality of patent documents into the plurality of categories includes:
for each patent document of the plurality of patent documents, perform the following:
parse textual information of the patent document using a natural language processing (NLP) engine,
based upon the textual information of the patent document, extract a third set of features representing the textual information of the patent document,
transform the third set of features into a third feature vector, the third feature vector being a vector having a plurality of dimensions, each of which corresponds to a value of a feature contained in the third set of features,
use hierarchical classification to classify the plurality of third feature vectors corresponding to the plurality of patent documents into the plurality of categories, and
calculate a centroid of all the plurality of third feature vectors corresponding to all the plurality of patent documents within each category as a category feature vector for the corresponding category;
identify a first patent document in one or more first data systems, the first patent document not categorized in a machine-learning-generated taxonomy system;
retrieve the first patent document from the one or more first data systems;
use the NLP engine to parse textual information from the first patent document;
extract a fourth set of features of the first patent document from the textual information of the first patent document;
transform the fourth set of features into a fourth feature vector, the fourth feature vector being a vector having a plurality of dimensions, each of which corresponds to a value of a feature contained in the fourth set of features;
compare the fourth feature vector corresponding to the first patent document with each category feature vector to determine a similarity;
assign the first patent document to a category corresponding to a category feature vector that has a highest similarity to the fourth feature vector corresponding to the first patent document;
receive an input indicating a source patent, the source patent being a patent application or an issued patent published by the one or more first data systems;
retrieve a source patent document associated with the source patent from the one or more first data systems;
parse textual information of the source patent document using the NLP engine;
based upon the textual information of the source patent document, extract a first set of features that represent the textual information of the source patent document;
transform the first set of features to a first feature vector, the first feature vector being a vector having a plurality of dimensions, each of which corresponds to a value of a feature contained in the first set of features;
determine a similarity between the first feature vector corresponding to the source patent document and a particular category feature vector corresponding to a particular category, wherein the particular category feature vector comprises a particular centroid of all the plurality of third feature vectors corresponding to all the patent documents within the particular category;
identify a plurality of candidate patents within the particular category;
for each of the plurality of candidate patents, perform the following:
retrieve a candidate patent document from one of the one or more first data systems,
parse textual information of the candidate patent document using the NLP engine,
based upon the textual information of the candidate patent document, extract a second set of features representing the textual information of the candidate patent document,
transform the second set of features into a second feature vector, the second feature vector being a vector having a plurality of dimensions, each of which corresponds to a value of a feature contained in the second set of features, and
determine a similarity between the first feature vector corresponding to the source patent document and the second feature vector corresponding to the candidate patent document;
based on the similarities between the first feature vector and each second feature vector, identify one or more target patents; and
visualize the source patent and the one or more target patents.
Claim 2:
Claim 3
Claim 4
Claim 5
Claim 6
Claim 7
Claim 8
Claim 9
It would have been obvious to a person of ordinary skill was made to modify and/or to omit the additional elements of claim 1-13 of U.S. Patent No. 12,260,663 to arrive at the claims 1-17 of the instant application 19/073,387 because the ordinary skilled person would have realized that the remaining element(s) would perform the same function as before and the only difference particularly claim 1,16-17 instant application 19/073,387 calculate a different category vector for each of the plurality of categories , determine a similarity between the source feature vector corresponding to the source patent and the second feature vector corresponding to the candidate patent while claim 1,11 of U.S. Patent No. 12,260,663, calculate a centroid of all the plurality of third feature vectors corresponding to all the plurality of patent documents within each category as a category feature vector for the corresponding category, determine a similarity between the first feature vector corresponding to the source patent document and a particular category feature vector corresponding to a particular category, wherein the particular category feature vector comprises a particular centroid of all the plurality of third feature vectors corresponding to all the patent documents within the particular category is/are absent of the limitation from instant application 19/073,387 claim 1,16-17, Omission and/or addition of elements and its function in combination is obvious expedient if the remaining elements perform same functions as before, as such instant application claim 1,16-17 are broader
It would have been obvious to a person of ordinary skill in the art at the time the invention was made to modify, add or omit the additional elements of claims 1, 11 to arrive at the claims 1,16-17 of the instant application because the person would have realized that the remaining element would perform the same functions as before. "Omission of element and its function in combination is obvious expedient if the remaining elements perform same functions as before." See In re Karlson (CCPA) 136 USPQ 184, decide Jan 16, 1963, Appl. No. 6857, U. S. Court of Customs and Patent Appeals.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nefedov et al., (hereafter Nefedov), US Pub. No. 2016/0350294 published Dec, 2016 in view of Grabau et al., (hereafter Grabau), US Pub. No. 2020/0073879 based on provisional application filed on Aug, 2018
As to Claim 1,13-14,16-17, A computing system comprising: (Nefedov: fig 5)
One or more processors; (Nefedov: fig 5) and
One or more computer-readable media having stored thereon computer-executable instructions that are structured such that, when executed by the one or more processors, cause the computing system to perform at least (Nefedov: fig 5,0069-0071)
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“use hierarchical classification to classify a plurality of patent documents into a plurality of categories, wherein using hierarchical classification to classify the plurality of patent documents into the plurality of categories includes” (Nefedov: Abstract, 0012 -0013 - Nefedov teaches hierarchal taxonomy trees defining sets of classification codes particularly trademarks, legal documents, scientific papers, lawsuits and like, further Nefedov teaches defining each fields of patent, patent title, patent abstract, patent IPC code(s), of patent documents categories);
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for each of the plurality of patent documents, performing the following: (Nefedov : fig 1: 0058 – Nefedov teaches search/databases of patent documents for example USPTO database, NOVUS distributed database, plurality of patent documents stored in the database element 103)
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“textual information of the patent document using a natural language processing (NLP) engine” (Nefedov: fig 2, element 208, 0061 – Nefedov teaches natural language search process of patent documents);
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“based upon the textual information, extracting a first set of features representing the textual information of the patent document” (Nefedov:0011, 0013 – Nefedov teaches fields of a patent for example patent title, patent abstract, patent IPC code corresponds to set of features related to patent textual information, sets or set of features of textual information corresponds to set of features of patent document),
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“transform the first set of features into a first feature vector, the first feature vector being a vector having a plurality of dimensions, each of which corresponds to a value of a feature contained in the first set of features” (Nefedov: 0014, 0016 – Nefedov teaches hieratical set of features and generates set of feature vectors, dynamically set threshold valu(s) on different dimension of patent structure view in similarity measure, further it is noted that dimension may corresponds to patents, trademarks, products, features and/or fields as detailed in 0014);
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,”use hierarchical classification to classify the plurality of feature vectors corresponding to the plurality of patent documents into the plurality of categories” (Nefedov: Abstract, fig 1, 0016 – Nefedov teaches hierarchical classification of documents particularly patent documents, trademarks, legal , and scientific documents, as hierarchical taxonomy tree, further set of weights of respective hieratical set of features and to generate a set of feature vectors); and
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“calculate a different category vector for each of the plurality of categories” (Nefedov: 0011 – Nefedov teaches calculating similarity between features vectors of the each plurality of patent document categories for example patents, trademarks, scientific document categories and specific feature vectors such as patent classification codes (IPC);
“ receive an input indicating a source patent, the source patent being a patent application or an issued patent published by one of one or more first data systems that publish patent documents” (Nefedov: fig 1-3, 0058,0060 – Nefedov teaches graphical user interface or GUI fig 1, element 118 receives the input query of typical source patent information or query comprising IPC code, patent number, or query terms related to patent document(s));
“determining a similarity between a source feature vector corresponding to the source patent and a particular category feature vector corresponding to a particular category” (Nefedov: fig 4, 0014-0015, page 3, 0016, col 2,0067 – Nefedov teaches determining the similarities of patent category features particularly set of feature vectors using similarity scoring support vector machine);
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“identify a plurality of candidate patents within the particular category” (Nefedov: fig 21, patents from company X, Y);
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for each of the plurality of candidate patents, performing the following: (Nefedov: fig 21)
“retrieve the candidate patent from the one or more first data systems” (Nefedov: fig fig 21, 0134),
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“textual information of the candidate patent document using the NLP engine” (Nefedov: fig 2, element 208, 0061 – Nefedov teaches natural language search process of patent documents);
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based upon the textual information, extracting a second set of features representing the textual information of the candidate patent (Nefedov:0011, 0013 – Nefedov teaches fields of a patent for example patent title, patent abstract, patent IPC code corresponds to set of features related to patent textual information, sets or set of features of textual information corresponds to set of features of patent document),
“transform the second set of features into a second feature vector, the second feature vector being a vector having a plurality of dimensions, each of which corresponds to a value of a feature contained in the second set of features” (Nefedov: 0014, 0016 – Nefedov teaches hieratical set of features and generates set of feature vectors, dynamically set threshold valu(s) on different dimension of patent structure view in similarity measure, further it is noted that dimension may corresponds to patents, trademarks, products, features and/or fields as detailed in 0014);
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, and
“determine a similarity between the source feature vector corresponding to the source patent and the second feature vector corresponding to the candidate patent” (Nefedov: fig 4,0066-0067 – Nefedov teaches similarity between generated vector entities of the patent IPC codes as a function of path distance via root on the taxonomy tree);
“ based on the similarities between the source feature vector and each second feature vector, identifying one or more target patents” (Nefedov: fig 4, 0066-0067); and
“visualize the source patent and the identified one or more target patents” (Nefedov: 0014 – Nefedov teaches hierarchical structure of patent documents displaying peer list for example peer list by peer graph visualization of patent information),
It is however, noted that Nefedov does not teach “parsed textual information”. On the other hand, Grabau disclosed “parsed textual information” (Grabau: 0038, 0047, fig 2A, element 218a document parser - Grabau teaches parsing textual patent documents as detailed in fig 2A)
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It would have been obvious to a person of ordinary skill in the art at the time of filing the claimed invention computer implemented patent search, particularly retrieving similar patent documents, ranking of Grabau et al., into hierarchical patent document provides particularly patents, trademarks, legal documents similarities defining hierarchical taxonomy tree of Nefedov et al., because both Nefedov, Grabau teaches searching, categorizing documents (Nefedov: fig 3, Abstract; Grabau: Abstract, fig 4A-B), and both Nefedov, Grabau teaches similarities of documents (Nefedov: fig 4, 0067; Grabau: fig 1, 0023) and both Nefedov, Grabau teaches document and/or word vectors (Nefedov: fig 1,0011, fig 21, 0134; Grabau: Abstract, fig 1) and they both Nefedov, Grabau are from the same field of endeavor. Because, both prior arts Nefedov, Grabau teaches document similarities, document and/or word vectors and supporting search documents, it would have been obvious to one skill ed in the art to substitute and/or modify one method for the other particularly parsing documents, using encoder vector to the set of words, indexing the document using the vector(s) thereby improves searchable document indexes and similarity of documents and while determining respective score associated with each document based on position of each document (Grabau: 0006-0007), thereby improves the search and identify similar documents
Claims 13-14 are rejected in the analysis of claim 1,1617, and claims 13-14 are rejected on that basis
As to claim 2, the combination of Nefedov, Grabau disclosed
“retrieving a court document associated with the court case from one of one or more second data systems that publish court documents” (Nefedov: 0008,0012, fig 1);
“textual information of the court document using the NLP engine” (Nefedov: fig 2, element 208, 0061); and
“based upon the textual information of the court document, identifying that a patent is associated with the court document and treating the patent as the source patent” (Nefedov: 0008,0012, 0053, fig 1).
On the other hand, Grabau disclosed “parsed textual information” (Grabau: 0038, 0047, fig 2A, element 218a document parser - Grabau teaches parsing textual patent documents as detailed in fig 2A)
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As to claim 3, the combination of Nefedov, Grabau disclosed “the computing system further configured to the input of the source patent or the court case to determine which one of the one or more first data systems or which one of one or more second data system contains the source patent or the court document associated with the court case” (Nefedov: 0008,0012, 0053, fig 1). On the other hand, Grabau disclosed “parsed textual information” (Grabau: 0038, 0047, fig 2A, element 218a document parser - Grabau teaches parsing textual patent documents as detailed in fig 2A)
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As to claim 4, the combination of Nefedov, Grabau disclosed
“when a similarity between the source feature vector corresponding to the source patent and a second feature vector corresponding to a candidate patent is greater than a predetermined threshold, determining that the candidate patent is a target patent” (Nefedov: fig 4, 0014-0015, page 3, 0016, col 2, 0067,0077 – Nefedov teaches determining the similarities of patent category features particularly set of feature vectors using similarity scoring support vector machine);
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As to claim 5, the combination of Nefedov, Grabau disclosed
“identifying a predetermined number of candidate patents that correspond to second feature vectors that have highest similarities to the source feature vector as target patents” (Nefedov: 0014, 0016,0135)
As to claim 6, the combination of Nefedov, Grabau disclosed
“identifying a plurality of patents that have an application date within a predetermined period” (Nefedov: 0049); or
“identifying a plurality of patents that have an assignee that is within a list of predetermined entities” (Nefedov: 0049))
As to claim 7, the combination of Nefedov, Grabau disclosed
“textual information of the source patent using a NLP engine includes identifying a list of keywords associated with the source patent” (Nefedov: fig 2, element 208, 0061 – Nefedov teaches natural language search process of patent documents);
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; and
“identifying a plurality of patents that contain the list of keywords as the plurality of candidate patents” (Nefedov: 0060,0062).
On the other hand, Grabau disclosed “parsed textual information” (Grabau: 0038, 0047, fig 2A, element 218a document parser - Grabau teaches parsing textual patent documents as detailed in fig 2A)
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As to claim 8, the combination of Nefedov, Grabau disclosed
“textual information of the source patent document using a NLP engine includes identifying at least one of (1) an international class, (2) a U.S. class, or (3) a field of search of the source patent: (Nefedov: 0049,0058); and
“identifying the plurality of candidate patents includes identifying a plurality of patents that share at least one of (1) the international class, (2) a U.S. class, or (3) the field of search as the plurality of candidate patents” (fig 1, 0011,0013, fig 22).
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On the other hand, Grabau disclosed “parsed textual information” (Grabau: 0038, 0047, fig 2A, element 218a document parser - Grabau teaches parsing textual patent documents as detailed in fig 2A)
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As to claim 9, the combination of Nefedov, Grabau disclosed
“determining that the source patent belongs to one of a plurality of categories in a machine learning generated taxonomy system”( Grabau: fig 3A-3B, 0060-0061; and
“identifying a plurality of patents that belong to the one of the plurality of categories in the taxonomy system as the plurality of candidate patents” (Grabau: fig 3A-3B, 0060-61)
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.
As to claim 10, the combination of Nefedov, Grabau disclosed “ wherein the computing system is further caused to use machine learning to analyze and classify a plurality of patent documents in the one or more first data systems into the plurality of categories to generate the taxonomy system” (Grabau: fig 3A-3B, 0060-0062): .
As to claim 11, the combination of Nefedov, Grabau disclosed
“identify a patent document in one of the one or more first data systems that is not in the taxonomy system” (Nefedov: 0073-0074)
“retrieve the patent document from the one of the one or more first data systems” (fig 2-3, 0060-0061);
“use the natural language engine to parse textual information from the retrieved patent” (Nefedov: fig 2, element 208, 0061);
“extract a third set of features of the patent document from the textual information of the patent document” (Nefedov: fig 4, 0014-0015, page 3, 0016, col 2,0067);
“transform the third set of features into a third feature vector, the third feature vector being a vector having a plurality of dimensions, each of which corresponds to a value of a feature contained in the third set of features” (Nefedov: 0014, 0016,0134);
“compare the third feature vector corresponding to the patent document with each category feature vector to determine a similarity” (Nefedov: 0073-0074, 0124,0134); and
“assign the patent document to a category corresponding to a category feature vector that has a highest similarity to the third feature vector corresponding to the patent document” (Nefedov: 0014, 0016,0135)
As to claim 12, the combination of Nefedov, Grabau disclosed
“textual information of a patent document further includes identifying at least one of: (1) textual information of a title of the patent, (2) textual information of an abstract section of the patent, or (3) textual information of a claim section of the patent document” (Nefedov: 0013, 0016) and
“each of the first set of features, the second set of features, or the third set of features is extracted based on the identified (1) textual information of the title of the patent, (2) textual information of the abstract section of the patent, or (3) textual information of the claim section of the patent” (Nefedov: 0013, 0016, 0049)
On the other hand, Grabau disclosed “parsed textual information” (Grabau: 0038, 0047, fig 2A, element 218a document parser - Grabau teaches parsing textual patent documents as detailed in fig 2A)
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As to claim 15, the combination of Nefedov, Grabau disclosed
“receive a user input accepting or rejecting the determined target patents” (Nefedov: fig 1, 0058,0060) ; and
“in response to a user input” (Nefedov: fig 1, 0058,0060),
remove the rejected target patent from the visualization (Nefedov : 0014, 0072-0074) ; and “update classification of the rejected target patent in the taxonomy system” (0067-0068,0072).
Conclusion
The prior art made of record
a. US Pub. No. 2016/0350294
b. US Pub. No. 2020/0073879
Examiner's Note: Examiner has cited particular columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
SEE MPEP 2141.02 [R-5] VI. PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, INCLUDING DISCLOSURES THAT TEACH AWAY FROM THE CLAIMS: A prior art reference must be considered in its entirety, i.e., as a whole, including portions that would lead away from the claimed invention. W.L. Gore & Associates, Inc. v. Garlock, Inc., 721 F.2d 1540, 220 USPQ 303 (Fed. Cir. 1983), cert. denied, 469 U.S. 851 (1984) In re Fulton, 391 F.3d 1195, 1201,73 USPQ2d 1141, 1146 (Fed. Cir. 2004). >See also MPEP §2123.
In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
The prior art made of record, listed on form PTO-892, and not relied upon, if any, is considered pertinent to applicant's disclosure
Authorization for Internet Communications
The examiner encourages Applicant to submit an authorization to communicate with the examiner via the Internet by making the following statement (from MPEP 502.03):
“Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.”
Please note that the above statement can only be submitted via Central Fax (not Examiner's Fax), Regular postal mail, or EFS Web using PTO/SB/439.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Srirama Channavajjala whose telephone number is 571-272-4108. The examiner can normally be reached on Monday-Friday from 8:00 AM to 5:30 PM Eastern Time.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Gorney, Boris, can be reached on (571) 270- 5626. The fax phone numbers for the organization where the application or proceeding is assigned is 571-273-8300 Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free)
/Srirama Channavajjala/Primary Examiner, Art Unit 2154