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 .
Response to Arguments
Applicant's arguments filed 11/13/2025 have been fully considered but they are not persuasive.
With respect to the 112 arguments (Remarks 7-8), the amendments require new 112 rejections.
Applicant argues “according to the above-recited features [pipelines] of independent claims 1 and 11, the change in the current lifecycle phases can be properly determined. Furthermore, the problem of ‘the inability to forecast when a particular technology’ can be overcome based on the above-recited features of independent claims 1 and 11.” Remarks 8. If an abstract idea is incorporated into a practical application by improving a computing system, the claim as a whole is patent eligible. MPEP 2106.04(d). Respectfully, even if the pipelines were claimed in a way that was supported by the specification, the pipelines don’t automatically “properly” determine current lifecycle phases. This argued improvement is not recognized as improvement resulting from the use of pipelines. Applicant’s argued problem, “inability to forecast when a particular technology[ may transition]” belongs to the abstract idea of organizing human activity, which is patent ineligible. Solving a problem, such as math problems or managing human activity, doesn’t automatically make a claim patent-eligible subject matter.
The amendments and arguments related to the 103 rejections require new claim mapping and render the arguments moot.
Claim Objections
Claim 11 is objected to because of the following informalities: Applicant claims “comparing the forecast of each features…” It should be comparing each of the features. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1, 3, 5, 7, 8, 10, 11, 13-15, 17, 18 and 20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Every reference to pipelines in the claims is new matter and it doesn’t make sense. The models are in the pipelines, not the other way around, see Spec. 211 and 222.
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.
Claims 1, 3, 5, 7, 8, 10, 11, 13-15, 17, 18 and 20 are 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.
Applicant claims using a first pipeline of a CSO to extract features. Claims 1 and 11. The CSO is in the pipeline3 it doesn’t have its own pipelines.4
Applicant claims a “second pipeline of a convolutional neural network…” Claim 1 and 11. There is no first pipeline of the CNN and there is no second pipeline embodiment in the specification to clear up the confusion.
Applicant claims “using a second pipeline of a convolution neural network to train a forecasting model…” Claim 1 and 11. This is not in the specification and it doesn’t make sense. The specification says “using a convolutional neural network to generate a forecast for the features…” Spec. 71.
Applicant claims using a “third pipeline of a K-means clustering algorithm…” Claim 1 and 11. There is no first or second pipeline, so it’s unclear how we get to a third pipeline.
Applicant claims a third pipeline of the K-means clustering algorithm. It’s unclear how a K-means algorithm would have its own pipelines without some examples in the specification.
The term “quickly” in claims 1 and 11 is a relative term which renders the claim indefinite. The term “quickly” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 10, 14 and 20 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends. Claim 14 should be cancelled because it is a repeat of a limitation that was added to claim 11 by the amendments filed 11/13/2025. Claims 10 and 20 claim the K-means algorithm that is now in the independent claims. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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, 3-5, 7, 8, 10, 11, 13-15, 17, 18 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. The claims recite the mental concept of selecting features, extracting features, forecasting, clustering and transitioning. This judicial exception is not integrated into a practical application because the additional elements of obtaining database information and training a CNN merely link the abstract idea to the field of accessing scientific literature. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the training step, and obtaining step merely link the abstract idea to a technical field, and the storage medium is a generic computer part.5 Training and evaluating neural networks with a split data set is the usual way that neural networks are trained.6 Generically training a neural network is well understood routine and conventional and not significantly more than the abstract idea alone.
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.
Claims 1, 5, 7, 10, 11, 14, 15, 17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over KR 20140146439A to Choe et al, The CSO Classifier: Ontology-Driven Detection of Research Topics in Scholarly Articles by Salatino et al, CN 110990569 A to Wang et al and US20230205757A1 to Gupta et al.
Claims 3, 8, 13 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over KR 20140146439A to Choe et al, The CSO Classifier: Ontology-Driven Detection of Research Topics in Scholarly Articles by Salatino et al, CN 110990569 A to Wang et al, US20230205757A1 to Gupta et al and How to use Wardley Mapping to understand how you deliver customer value by Willemse.
Choe teaches claims 1 and 11. A method, (Choe abs “method for forecasting emerging technology”) comprising:
obtaining historic data from one or more databases of information about one or more technologies, wherein the one or more databases are open source databases that include patent and technical literature; (Choe abs “storage unit stores patent information including bibliographic items and publications.”)
selecting features for extraction from the historic data, wherein the historic data comprises a first set during a first period and a second set during a second period directly following the first period;(Choe abs “A preprocessing unit selects words with higher frequency weights as keywords to be used in the analysis from the entire document among words described in the title of the invention and the abstract included in the bibliographic items of the patent information.” The selected features are the words with higher frequency weights. All data sets with time have a first period and second period.)
extracting the features from the historic data using a first pipeline of (Choe abs “selects words with higher frequency weights as keywords to be used in the analysis from the entire document…” this is an extraction.)
using the forecasting model to generate a forecast of each of the features, indicating when one or more of the technologies is expected to reach maturity, for the second period based on the model forecast; (Choe abs “method may obtain forecasting results which reflect changes in the rapidly changing technology trends, compared to a conventional technology forecasting method, by considering time-series information of the keywords extracted from the patent document.” Changes in technology trends are when a technology is expected to reach maturity, inasmuch as applicant has defined maturity. Applicant does not claim generating a forecast for only the second period. Choe generates a forecast for both periods.)
clustering the one or more technologies based on their respective current (Choe abs “company. Among these patent information, it is possible to find the blank technology or the injury technology and to find out the trend of the technology by using the patent map or the patent index or the patent clustering using the text mining.”)
based on the current lifecycle phases of the technologies, and the forecast, (Choe abs “company. Among these patent information, it is possible to find the blank technology or the injury technology and to find out the trend of the technology by using the patent map or the patent index or the patent clustering using the text mining.”) determining when and how quickly a computing system should be transitioned from a first one of the technologies to a second one of the technologies that is newer than the first technology. (Choe abs “According to the present invention, it is possible to obtain a prediction result that reflects a rapidly changing trend of a technology trend…” The new technology is the “change” in the technology trend. Detecting the rapidly changing trend is determining a system should be transitioned.)
Choe doesn’t teach a Computer Science Ontology Classifier (CSO).
However, Salatino teaches Computer Science Ontology Classifier (CSO). (Salatino abs. “we present the CSO Classifier, a new unsupervised approach for automatically classifying research papers according to the Computer Science Ontology (CSO), a comprehensive ontology of research areas in the field of Computer Science.”)
Choe, Salatino and the claims are all directed to extracting semantic meaning. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to use a Salatino’s CSO because it yields “yielding a significant improvement over alternative methods.” Salatino abs.
Choe doesn’t a CNN or K-means clustering.
However, Wang teaches a convolutional neural network to generate a forecast for the features… (Wang abs “the writing style features centralized text of the text to be predicted input digital vector target convolutional neural network CNN model to obtain the text concentrated text to be predicted…”)
Training using a second pipeline of a convolutional neural network to train a forecasting model by using the first set of the historical data to generate a model forecast; (The CNN is the second pipeline according to Spec. 21 and 22. And the CNN is trained, the CNN doesn’t train a separate forecasting model. Wang p. 4 “obtaining training text set text; model training module for writing style features based on the training text set text, training to obtain the target CNN model.”)
… including a K-means clustering algorithm… (Wang p. 8 “the preset clustering algorithm may be a conventional clustering algorithm including a K-means clustering algorithm”)
The claims, Choe and Wang all extract text features. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to use a CNN to forecast text trends because using a CNN to predict features can “enhance the accuracy of the clustering result.” Wang p. 4. It would have been obvious to use K-means to cluster because it is a well-known, “conventional” and effective algorithm. Wang p. 8.
Wang doesn’t teach a later time window for comparing outputs.
However, Gupta teaches that evaluating the forecasting model by comparing the forecast of each of the features for the second period with an actual value of a corresponding feature based on the second set of the historical data; (Gupta para 49 “reader 146 may train ML model 510 using data from a time period back in time, and then assess the effectiveness of the training by providing more recent input into the ML model 510 and comparing the results (e.g., output) with current data (using the current master branch). This allows evaluation of the effectiveness, accuracy, etc. of the ML model 510.”)
The claims, Wang and Gupta are all processing data with a ML algorithm. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to use Gupta’s testing of multiple outputs based on multiple time windows because it “allows evaluation of the effectiveness, accuracy, etc. of the ML model 510.” Gupta para 49.
Choe teaches claims 3 and 13. The method as recited in claim 1, wherein the current (Choe abs “According to the present invention, the apparatus and method may obtain forecasting results which reflect changes in the rapidly changing technology trends, compared to a conventional technology forecasting method, by considering time-series information of the keywords extracted from the patent document.” The reflected changes include at least two states and the current state is equivalent to the claimed current phase.) Choe doesn’t teach a lifecycle phase.
However, Willemse teaches current (Willemse p. 5 “This concept is not a new one: there has been widespread acceptance of the idea of technology s-curves or life cycles, which has a long history in academia.”)
The claims, Wang and Gupta are all processing data with a ML algorithm. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to use Gupta’s testing of multiple outputs based on multiple time windows because it “allows evaluation of the effectiveness, accuracy, etc. of the ML model 510.” Gupta para 49.
Choe teaches claim 14. The non-transitory storage medium as recited in claim 11, wherein the databases are open source databases that include patents and/or technical literature. (Choe abs “storage unit stores patent information including bibliographic items and publications.”)
Choe teaches claims 5 and 15. The method as recited in claim 1, wherein the forecast is made for all of the features concurrently. (Choe abs “According to the present invention, the apparatus and method may obtain forecasting results which reflect changes in the rapidly changing technology trends, compared to a conventional technology forecasting method, by considering time-series information of the keywords extracted from the patent document.”)
Choe teaches claims 7 and 17. The method as recited in claim 1, wherein the forecast indicates, for each of the features, how that feature is expected to change during a defined time period. (Choe abs emphasis added “According to the present invention, the apparatus and method may obtain forecasting results which reflect changes in the rapidly changing technology trends, compared to a conventional technology forecasting method, by considering time-series information of the keywords extracted from the patent document.”)
Choe teaches claims 8 and 18. The method as recited in claim 1, wherein the forecast for the features indicate when one or more of the technologies are expected to change from a first (Choe abs “According to the present invention, the apparatus and method may obtain forecasting results which reflect changes in the rapidly changing technology trends, compared to a conventional technology forecasting method, by considering time-series information of the keywords extracted from the patent document.”) Choe doesn’t teach a lifecycle phase.
However, Willemse teaches lifecycle phase. (Willemse p. 5 “This concept is not a new one: there has been widespread acceptance of the idea of technology s-curves or life cycles, which has a long history in academia.”)
The claims, Choe and Willemse are all directed to mapping technology changes. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to use Wardley lifecycle phases to represent the technologies “to help organisations understand the different parts of their value chain…” Willemse p. 2 sec. “What is Wardley Mapping?” Furthermore, this lifecycle is well known and “has a long history in academia.”
Wang teaches claims 10 and 20. The method as recited in claim 9, wherein the technologies are clustered using a k-means clustering algorithm. (Wang bottom of p. 12 “wherein the first clustering result of K times the hierarchical clustering in the text clustering to be predicted is the K clusters, the K is more than 1 and less than or equal to integer multiple of M.”)
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/AUSTIN HICKS/Primary Examiner, Art Unit 2124
1 “The first machine learning model used in example implementations of such a pipeline is a Computer Science Ontology Classifier (CSO).” Spec. 21.
2 “example pipelines according to some embodiments may employ a CSO model, a CNN forecasting model, and a clustering model, or clustering algorithm.” Spec. 22.
3 “The first machine learning model used in example implementations of such a pipeline is a Computer Science Ontology Classifier (CSO).” Spec. 21.
4 “ example pipelines according to some embodiments may employ a CSO model, a CNN forecasting model, and a clustering model, or clustering algorithm.” Spec. 22.
5 US20180032492A1 to Altshuller para 75 “computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.”
6 US 20190166024 A1 Abs. “The network anomaly analysis apparatus stores a plurality of network status data and is configured to dimension-reduce each network status datum into a principal component datum, select a first subset and a second subset of the principal component data as the training data and the testing data respectively,”