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.
Claims 17-20, 23-33 and 36 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As to claim 17, it recites “…wherein the classification model and the least one quality index model of the first kind are based on the at least one classification, and obtained using at least one of the following: machine learning, artificial intelligence…” in which it is not clear the words “obtained using” are actually referred to what. In fact, the words “obtained using” are not grammatically corrected. Since the claim cannot be interpreted properly, the scope of the claim is not clear. Therefore, it renders the claim indefinitely.
Claims 18-20, 23-27 are dependent from claim 17, and thus inherited the above deficiency.
As to claim 28, it is rejected for similar reason as claim 17.
As to claim 29, it is rejected for similar reason as claim 17.
Claims 30-34 and 36 are dependent from claim 29 and thus inherited the above deficiency.
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 17, 19-29, 32-36 are rejected under 35 U.S.C. 103 as being unpatentable over O'Malley (US 12094018 B1), published on September 17, 2024 in view of Flemming et al (“Flemming” US 20230409647 A1), published on December 21, 2023 in view of Lane et al (“Lane” US 2013/0212030 A1), published on August 15, 2013.
As to claim 17, O’Malley teaches “A computer-implemented method for producing an indicator, wherein the method comprises:
a. providing at least one classification;
b. obtaining at least one quality index model of the first kind;
c. providing at least one intellectual property product” in col. 259: 35-40 (“…inputting a patent claim input (which might be a partial patent claim or a set of multiple patent claims); and electronically running a patentability module that evaluates the patent claim input to determine a patentability profile for the patent claim input…”; a patentability module corresponds to at least one quality index model of the first kind; “a patent claim corresponds to providing at least one intellectual property product).
It appears O’Malley does not explicitly teach “b. obtaining a classification model, wherein the classification model and the least one quality index model of the first kind are based on the at least one classification, and obtained using at least one of the following: machine learning, artificial intelligence”.
However, Flemming teaches “b. obtaining a classification model, wherein the classification model and the least one quality index model of the first kind are based on the at least one classification, and obtained using at least one of the following: machine learning, artificial intelligence” in claim 1 (“…the machine-learning classification models configured to receive documents and predict a classification of documents in a document set utilizing predictive analytical techniques, the individual ones of the machine-learning classification models trained based at least in part on a document dataset indicated to be in class from first user input data, the documents comprising patents and patent applications, wherein the individual ones of the machine-learning classification models differ from the documents that the individual ones of the machine-learning classification models are configured to receive and analyze…”).
O’Malley and Flemming are analogous art because they are in the same field of endeavor, patent analysis application. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claim invention to process patent product (disclosed by O’Malley) including “c. obtaining a classification model, wherein the classification model and the least one quality index model of the first kind are based on the at least one classification, and obtained using at least one of the following: machine learning, artificial intelligence” in order to determine what document should be in a particular class (see Flemming par. 0035).
Flemming teaches “d. classifying the at least one Intellectual Property product according to at least one classification using the classification model” in par. 0035 (“…In light of this, a document analysis platform that is configured to intake documents, receive marginal user input to train classification models, and then to use those classification models to determine which documents in a set of documents are in class would be beneficial…”).
O’Malley teaches “e. labelling the at least one intellectual property product, using the at least one quality index model of the first kind, with at least one quality index of the first kind” in col. 259: 35-40 (“…running the patentability module by using an information extraction algorithm to generate a plurality of claim components of the patent claim input. The claim components can be generated by parsing elements of the patent claim input to establish classifications of claim components…”; “classifications of claim components” corresponds to labelling the at least one IP product).
O’Malley teaches “f. obtaining the indicator using the at least one quality index of the first kind” in col. 259: 35-40 (patentability module determines patentability profile) and in col. 260: 5-10 (patentability profile includes “an estimated likelihood or predictive evaluation of novelty of the patent claim input”; noting that an estimated likelihood of novelty corresponds to the indicator using the at least one quality index of the first kind).
O’Malley teaches “wherein the at least one intellectual property product is labelled with the quality index of the first kind independently of at least one other intellectual property product” in col. 260: 5-10 (“…patentability profile includes one or more of (1) an estimated likelihood or predictive evaluation of novelty of the patent claim input; (2) an evaluated contribution of each of the authorized participants to the patent claim input; and (3) a determined compensation for one or more of the authorized participants…”; noting that different patents have different likelihood of novelty).
It appears O’Malley and Flemming do not explicitly teach “wherein the quality index of the first kind comprises at least two of the following:
a likelihood of an outcome for the at least one Intellectual Property product;
a likelihood that a type of objection will be raised against the at least one Intellectual Property product during prosecution proceedings;
a likelihood that a geographical scope (e.g., the countries or regions where the at least one Intellectual Property product has been filed in, the countries or regions where the at least one intellectual property product is in force, or a combination thereof) of the at least one Intellectual Property product will change;
a likelihood that the at least one Intellectual Property product will receive a certain number of citations;
an estimate of a lifetime of the at least one Intellectual Property product (e.g., how long the at least one Intellectual Property product will remain in force); and an estimate of a value of the at least one Intellectual Property product (e.g., a monetary value)”.
However, Lane teaches “wherein the quality index of the first kind comprises at least two of the following:
a likelihood of an outcome for the at least one Intellectual Property product;
a likelihood that a type of objection will be raised against the at least one Intellectual Property product during prosecution proceedings;
a likelihood that a geographical scope (e.g., the countries or regions where the at least one Intellectual Property product has been filed in, the countries or regions where the at least one intellectual property product is in force, or a combination thereof) of the at least one Intellectual Property product will change;
a likelihood that the at least one Intellectual Property product will receive a certain number of citations;
an estimate of a lifetime of the at least one Intellectual Property product (e.g., how long the at least one Intellectual Property product will remain in force); and
an estimate of a value of the at least one Intellectual Property product (e.g., a monetary value)” in par. 0005 (“…This invention addresses the objective of predicting the future adoption of a patent by predicting a future number of forward citations… It also comprises the combination of predicting both the level of adoption and the future market size of a target patent, thereby better predicting a future patent value estimate for said target patent”. Noting that the prediction of “number of forward citations” corresponding to “a likelihood that the at least one Intellectual Property product will receive a certain number of citations”, and “predicting a future patent value estimate” corresponding to “an estimate of a value of the at least one Intellectual Property product)”.
O’Malley, Flemming and Lane are analogous art because they are in the same field of endeavor, patent analysis application. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claim invention to process patent product (disclosed by O’Malley) including “wherein the quality index of the first kind comprises at least two of the following:
a likelihood of an outcome for the at least one Intellectual Property product;
a likelihood that a type of objection will be raised against the at least one Intellectual Property product during prosecution proceedings;
a likelihood that a geographical scope (e.g., the countries or regions where the at least one Intellectual Property product has been filed in, the countries or regions where the at least one intellectual property product is in force, or a combination thereof) of the at least one Intellectual Property product will change;
a likelihood that the at least one Intellectual Property product will receive a certain number of citations;
an estimate of a lifetime of the at least one Intellectual Property product (e.g., how long the at least one Intellectual Property product will remain in force); and
an estimate of a value of the at least one Intellectual Property product (e.g., a monetary value)” in order to provide better patent evaluations (see Lane par. 0005).
As to claim 29, it is rejected for similar reasons as claim 17.
As to claim 19, O’Malley teaches “wherein the at least one intellectual property product comprises text data” in col. 259: 35-40 (patent claim corresponds to text data).
O’Malley teaches “and wherein the at least one intellectual property product is labelled with the quality index of the first kind using the text data of the at least one IP product” in col. 259: 35-40 (patentability profile corresponds to a label with the quality index of the first kind using the text data of the at least one IP product).
As to claim 32, it is rejected for similar reasons as claim 19.
As to claim 20, O’Malley teaches “wherein the at least one quality index model of the first kind is obtained using a plurality of intellectual property disclosures of a first kind” in col. 308: 30-35 (specification corresponds to IP disclosures).
As to claim 33, it is rejected for similar reasons as claim 20.
As to claim 23, O’Malley teaches “wherein the indicator ” in col. 75: 15-20 (“…a claim might be determined to be an apparatus claim or a method claim, and different rules might be established for analyzing the claim dependent upon its classification…”; noting that claim is segmenting in order for the system to analyze claim for patentability that is the indicator).
As to claim 36, it is rejected for similar reasons as claim 23.
As to claim 24, O’Malley teaches “wherein at least one of the following applies: a. the plurality of IP disclosures of the first kind is classified according to the at least one classification; b. the at least one intellectual property product is classified according to the at least one classification” in col. 2: 19-22 (“…second information extraction algorithm that parses elements of the patent claim input and uses semantic roles and relationships of words within the patent claim input to establish classifications of claim components…”).
As to claim 25, O’Malley teaches “wherein the indicator
is obtained using at least one of the following: a. machine learning; b. artificial intelligence” in col. 82: 15-20 (“…wherein the performance of the persistent searching according to the searching policy includes any items missing of/from/by the negotiation elements comprises an artificial intelligence system, machine learning, knowledge base management module, METER module, natural language processing, negotiation management module, IPACE court, peers, witnesses, experts, user…”).
As to claim 26, O’Malley teaches “wherein the method further comprises: a. obtaining at least one quality index model of the further kind; b. labelling the at least on intellectual property product, using the at least one quality index model of the further kind, with at least one quality index of the further kind” in col. 259: 35-40 (“…inputting a patent claim input (which might be a partial patent claim or a set of multiple patent claims); and electronically running a patentability module that evaluates the patent claim input to determine a patentability profile for the patent claim input…”; a patentability module corresponds to obtaining at least one quality index model of the further kind; “a patent claim corresponds to at least one IP product).
As to claim 27, O’Malley teaches “wherein an indicator is obtained” in col. 259: 35-40 (patentability profile includes an obtained indicator).
As to claim 28, O’Malley teaches “a. providing at least one classification;
b. obtaining at least one quality index model of the first kind;
c. providing at least one intellectual property product” in col. 259: 35-40 (“…inputting a patent claim input (which might be a partial patent claim or a set of multiple patent claims); and electronically running a patentability module that evaluates the patent claim input to determine a patentability profile for the patent claim input…”; a patentability module corresponds to at least one quality index model of the first kind; “a patent claim corresponds to providing at least one intellectual property product).
It appears O’Malley does not explicitly teach “b. obtaining a classification model, wherein the classification model and the least one quality index model of the first kind are based on the at least one classification, and obtained using at least one of the following: machine learning, artificial intelligence”.
However, Flemming teaches “b. obtaining a classification model, wherein the classification model and the least one quality index model of the first kind are based on the at least one classification, and obtained using at least one of the following: machine learning, artificial intelligence” in claim 1 (“…the machine-learning classification models configured to receive documents and predict a classification of documents in a document set utilizing predictive analytical techniques, the individual ones of the machine-learning classification models trained based at least in part on a document dataset indicated to be in class from first user input data, the documents comprising patents and patent applications, wherein the individual ones of the machine-learning classification models differ from the documents that the individual ones of the machine-learning classification models are configured to receive and analyze…”).
O’Malley and Flemming are analogous art because they are in the same field of endeavor, patent analysis application. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claim invention to process patent product (disclosed by O’Malley) including “c. obtaining a classification model, wherein the classification model and the least one quality index model of the first kind are based on the at least one classification, and obtained using at least one of the following: machine learning, artificial intelligence” in order to determine what document should be in a particular class (see Flemming par. 0035).
Flemming teaches “d. classifying the at least one Intellectual Property product according to at least one classification using the classification model” in par. 0035 (“…In light of this, a document analysis platform that is configured to intake documents, receive marginal user input to train classification models, and then to use those classification models to determine which documents in a set of documents are in class would be beneficial…”).
O’Malley teaches “e. labelling the at least one intellectual property product, using the at least one quality index model of the first kind, with at least one quality index of the first kind” in col. 259: 35-40 (“…running the patentability module by using an information extraction algorithm to generate a plurality of claim components of the patent claim input. The claim components can be generated by parsing elements of the patent claim input to establish classifications of claim components…”; “classifications of claim components” corresponds to labelling the at least one IP product).
O’Malley teaches “f. obtaining the indicator using the at least one quality index of the first kind” in col. 259: 35-40 (patentability module determines patentability profile) and in col. 260: 5-10 (patentability profile includes “an estimated likelihood or predictive evaluation of novelty of the patent claim input”; noting that an estimated likelihood of novelty corresponds to the indicator using the at least one quality index of the first kind).
O’Malley teaches “wherein the at least one intellectual property product is labelled with the quality index of the first kind independently of at least one other intellectual property product” in col. 260: 5-10 (“…patentability profile includes one or more of (1) an estimated likelihood or predictive evaluation of novelty of the patent claim input; (2) an evaluated contribution of each of the authorized participants to the patent claim input; and (3) a determined compensation for one or more of the authorized participants…”; noting that different patents have different likelihood of novelty).
O’Malley teaches “and ” in par. 0061 (“…patent analysis system 110 may generate a graphical user interface (GUI) displaying the patentability metric…”) and in par. 0019 (“…The patentability metric may be a numerical value, percentage, score, letter grade, and/or other indicator of patentability related to the received idea disclosure…”).
It appears O’Malley and Flemming do not explicitly teach “wherein the quality index of the first kind comprises at least two of the following:
a likelihood of an outcome for the at least one Intellectual Property product;
a likelihood that a type of objection will be raised against the at least one Intellectual Property product during prosecution proceedings;
a likelihood that a geographical scope (e.g., the countries or regions where the at least one Intellectual Property product has been filed in, the countries or regions where the at least one Intellectual Property product is in force, or a combination thereof) of the at least one Intellectual Property product will change;
a likelihood that the at least one Intellectual Property product will receive a certain number of citations;
an estimate of a lifetime of the at least one Intellectual Property product (e.g., how long the at least one Intellectual Property product will remain in force); and an estimate of a value of the at least one Intellectual Property product (e.g., a monetary value)”.
However, Lane teaches “wherein the quality index of the first kind comprises at least two of the following:
a likelihood of an outcome for the at least one Intellectual Property product;
a likelihood that a type of objection will be raised against the at least one Intellectual Property product during prosecution proceedings;
a likelihood that a geographical scope (e.g., the countries or regions where the at least one Intellectual Property product has been filed in, the countries or regions where the at least one Intellectual Property product is in force, or a combination thereof) of the at least one Intellectual Property product will change;
a likelihood that the at least one Intellectual Property product will receive a certain number of citations;
an estimate of a lifetime of the at least one Intellectual Property product (e.g., how long the at least one Intellectual Property product will remain in force); and
an estimate of a value of the at least one Intellectual Property product (e.g., a monetary value)” in par. 0005 (“…This invention addresses the objective of predicting the future adoption of a patent by predicting a future number of forward citations… It also comprises the combination of predicting both the level of adoption and the future market size of a target patent, thereby better predicting a future patent value estimate for said target patent”. Noting that the prediction of “number of forward citations” corresponding to “a likelihood that the at least one Intellectual Property product will receive a certain number of citations”, and “predicting a future patent value estimate” corresponding to “an estimate of a value of the at least one Intellectual Property product)”.
O’Malley, Flemming and Lane are analogous art because they are in the same field of endeavor, patent analysis application. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claim invention to process patent product (disclosed by O’Malley) including “wherein the quality index of the first kind comprises at least two of the following:
a likelihood of an outcome for the at least one Intellectual Property product;
a likelihood that a type of objection will be raised against the at least one Intellectual Property product during prosecution proceedings;
a likelihood that a geographical scope (e.g., the countries or regions where the at least one Intellectual Property product has been filed in, the countries or regions where the at least one Intellectual Property product is in force, or a combination thereof) of the at least one Intellectual Property product will change;
a likelihood that the at least one Intellectual Property product will receive a certain number of citations;
an estimate of a lifetime of the at least one Intellectual Property product (e.g., how long the at least one Intellectual Property product will remain in force); and
an estimate of a value of the at least one Intellectual Property product (e.g., a monetary value)” in order to provide better patent evaluations (see Lane par. 0005).
Claims 18 and 31 are rejected under 35 U.S.C. 103 as being unpatentable over O’Malley (US 12,094,018 B1), published on September 17, 2024 in view of Lane et al (“Lane” US 2013/0212030 A1), published on August 15, 2013, Flemming et al (“Flemming” US 20230409647 A1), published on December 21, 2023 and in further view of Adibowo (US 20190279073 A1), published on September 12, 2019.
As to claim 18, it appears O’Malley, Flemming and Lane do not explicitly teach “wherein the at least one intellectual property product comprises text data, and wherein the method further comprises the step of vectorizing the text data of the at least one intellectual product”.
However, Adibowo teaches “wherein the at least one intellectual property product comprises text data, and wherein the method further comprises the step of vectorizing the text data of the at least one intellectual property product” in par. 0038 (“…In an embodiment, after retrieving a new patent application publication, patent scraping subsystem 235 may vectorize the patent application publication and/or store the vectorized data in patent publication database 230…”).
O’Malley, Flemming, Lane and Adibowo are analogous art because they are in the same field of endeavor, patent analysis application. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claim invention to process patent product (disclosed by O’Malley) by vectorizing patent text data, as suggested by Adibowo including “wherein the at least one intellectual property product comprises text data, and wherein the method further comprises the step of vectorizing the text data of the at least one intellectual property product” in order to generate/update patentability model (see Adibowo par. 0038).
As to claim 31, it is rejected for similar reasons as claim 18.
Claims 30 is rejected under 35 U.S.C. 103 as being unpatentable over O’Malley (US 12,094,018 B1), published on September 17, 2024 in view of Lane et al (“Lane” US 2013/0212030 A1), published on August 15, 2013, Flemming et al (“Flemming” US 20230409647 A1), published on December 21, 2023 and in further view of Ollila et al (“Ollila” US 2008/0170130 A1), published on July 17, 2008.
As to claim 30, O’Malley teaches “a. at least one processing unit; b. at least one display device; c. at least one input device; d. at least one primary storage medium; e. at least one secondary storage medium” in par. 0061 (disclosed of display device).
It appears O’Malley, Flemming and Lane do not explicitly teach “and wherein at least one or all of the following applies to the non-transitory computer- readable medium: I./ the at least one processing unit has a clock rate that is less than 6 GHz; II./ the at least one primary storage medium has less than 8 Gb of storage space”.
However, Ollila teaches “and wherein at least one or all of the following applies to the non-transitory computer- readable medium: I./ the at least one processing unit has a clock rate that is less than 6 GHz; II./ the at least one primary storage medium has less than 8 Gb of storage space” in par. 0039 (noting 2Gb is less than 8Gb).
O’Malley, Flemming, Lane and Ollila are analogous art because they are in the same field of endeavor, display device application. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claim invention to display patent information (disclosed by O’Malley) by in devices including “and wherein at least one or all of the following applies to the non-transitory computer- readable medium: I./ the at least one processing unit has a clock rate that is less than 6 GHz; II./ the at least one primary storage medium has less than 8 Gb of storage space”, as suggested by Ollila in order to take advantage of high clock rate computer storage medium (see Ollila par. 0039).
Response To Arguments
Applicant’s arguments have been considered but are moot in view of the new ground of rejection.
Conclusion
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/LOC TRAN/
Primary Examiner, Art Unit 2164
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