DETAILED ACTION
1. This action is in response the communications filed on 04/16/2026 in which claims 1, 14 and 27 are amended, claims 2, 13, 15, 26, 28 and 39 have been cancelled, and therefore claims 1, 3-12, 14, 16-25, 27 and 29-38 are pending.
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 § 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, 3-12, 14, 16-25, 27 and 29-38 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more
Step 1: Claims 1 and 3-12 recite a method. Claims 14 and 16-25 recite a system comprising one non-transitory memory and one processor. Claims 27 and 29-38 recites a non-transitory computer storage medium. Therefore, claims 1 and 3-12 are directed to a process, claims 14 and 16-25 are directed to a machine, and claims 27 and 29-38 are directed to a manufacture.
With respect to claims 1, 14 and 27:
2A Prong 1: the claim recites a judicial exception.
to classify each of the one or more content items of the labeled data using one or more attributes derived from the knowledge representation as a feature (mental process – evaluation or judgement)
modifying the knowledge representation based on a comparison of the classification… for each content item of the labeled data to the known label of each respective content item, the modifying comprising at least one of: adding an additional concept to the knowledge representation; modifying a weight associated with the at least one concept in the knowledge representation; and forming a new relationship between two or more concepts in the knowledge representation (mental process – evaluation or judgement)
repeating the classifying and modifying until a target threshold is achieved for at least one of: a precision value; and a recall value (mental process – evaluation or judgement)
2A Prong 2: This judicial exception is not integrated into a practical application.
(Claim 1) executing via a computer system comprising at least one processor and at least one non-transitory memory… by the processor… by the processor… by the processor… by the processor… by the processor… (Claim 14) at least one processor, at one or more server computers, and at least one non- transitory memory (claim 27) storing processor-executable instructions that, when executed by at least one processor, cause the at least one processor, at one or more server computers (mere instructions to apply an exception, (2) Whether the claim invokes computers - see MPEP 2106.05(f))
receiving… the knowledge representation encoded as a non-transitory computer-readable data structure, the knowledge representation based on an object of interest and comprising at least one concept and/or relationship between two or more concepts (insignificant extra-solution activity – MPEP 2106.05(g), (3) data gathering and outputting)
receiving… labeled data comprising one or more content items external to the object of interest, the one or more content items having a known label that classifies each content item into one or more categories (insignificant extra-solution activity – MPEP 2106.05(g), (3) data gathering and outputting)
executing… the machine-learning classifier… by the machine-learning classifier (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
(Claim 1) executing via a computer system comprising at least one processor and at least one non-transitory memory… by the processor… by the processor… by the processor… by the processor… by the processor… (Claim 14) at least one processor, at one or more server computers, and at least one non- transitory memory (claim 27) storing processor-executable instructions that, when executed by at least one processor, cause the at least one processor, at one or more server computers (mere instructions to apply an exception, (2) Whether the claim invokes computers - see MPEP 2106.05(f))
receiving… the knowledge representation encoded as a non-transitory computer-readable data structure, the knowledge representation based on an object of interest and comprising at least one concept and/or relationship between two or more concepts (insignificant extra-solution activity – MPEP 2106.05(g), (3) data gathering and outputting, and WURC: receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 - MPEP 2106.05(d)(II)(i))
receiving… labeled data comprising one or more content items external to the object of interest, the one or more content items having a known label that classifies each content item into one or more categories (insignificant extra-solution activity – MPEP 2106.05(g), (3) data gathering and outputting, and WURC: receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 - MPEP 2106.05(d)(II)(i))
executing… the machine-learning classifier… by the machine-learning classifier (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception)
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
With respect to claims 3, 16 and 29:
2A Prong 1: the claim recites a judicial exception.
wherein the knowledge representation is calibrated and the calibrating comprises generating the concept or the relationship between two or more concepts, wherein at least one of the concept and relationships are not recited in the object of interest (evaluation, generating the concept between two concepts)
With respect to claims 4, 17 and 30:
2A Prong 1: the claim recites a judicial exception.
wherein the knowledge representation includes weights associated with the at least one concept (mental process – evaluation or judgement. Claim 1 recites “modifying a weight… in the knowledge representation” which is an abstract idea. Specifying the weights associated with a concept does not change the scope of the claim.)
With respect to claims 5, 18 and 31:
2A Prong 1: the claim recites a judicial exception.
wherein classifying… is based on an intersection of the one or more content items and the one or more features. (mental process – evaluation or judgement)
2A Prong 2: This judicial exception is not integrated into a practical application.
with the machine-learning classifier… (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
with the machine-learning classifier… (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception)
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
With respect to claims 6, 19 and 32:
2A Prong 2: This judicial exception is not integrated into a practical application.
wherein the object of interest comprises at least one of a topic, a tweet, a webpage, a website, a document, a collection of documents, a document title, a message, an advertisement, and a search query (a particular technological environment or field of use – MPEP 2106.05(h))
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
wherein the object of interest comprises at least one of a topic, a tweet, a webpage, a website, a document, a collection of documents, a document title, a message, an advertisement, and a search query (a particular technological environment or field of use – MPEP 2106.05(h))
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
With respect to claims 7, 20 and 33:
2A Prong 1: the claim recites a judicial exception.
wherein the repeating the classifying and modifying comprises: re-classifying each of the content items of the labeled data using the modified knowledge representation (mental process – evaluation or judgement)
modifying the knowledge representation based on a comparison of the re-classification… for each content item of the labeled data to the known label of each respective content item in the labeled data (mental process – evaluation or judgement)
2A Prong 2: This judicial exception is not integrated into a practical application.
by the machine-learning classifier… (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
by the machine-learning classifier… (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception)
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
With respect to claims 8, 21 and 34:
2A Prong 1: the claim recites a judicial exception.
wherein achieving the target threshold for the precision value includes obtaining a predetermined ratio of a number of the one or more content items correctly classified as being relevant to the object of interest to a total number of content items in the labeled data (mental process – evaluation or judgement, obtaining/evaluating a ratio of a number of correct items to a total number of content items in the labeled data)
With respect to claims 9, 22 and 35:
2A Prong 1: the claim recites a judicial exception.
wherein achieving the target threshold for the recall value includes obtaining a predetermined ratio of a number of the one or more content items correctly classified as being relevant to the object of interest to a total number of the one or more content items classified as being relevant to the object of interest (mental process – evaluation or judgement, obtaining/evaluating a ratio of a number of correct items to a total number of content items relevant to the object of interests)
With respect to claims 10, 23 and 36:
2A Prong 2: This judicial exception is not integrated into a practical application.
wherein the received knowledge representation is a synthesized knowledge representation (insignificant extra-solution activity – MPEP 2106.05(g), (3) data gathering and outputting) (Claim 1 recites “receiving the knowledge representation” which is insignificant extra-solution activity. A ‘synthesized’ representation is not indicative of integration into a practical application.)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
wherein the received knowledge representation is a synthesized knowledge representation (insignificant extra-solution activity – MPEP 2106.05(g), (3) data gathering and outputting, and WURC: receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 - MPEP 2106.05(d)(II)(i))
(Claim 1 recites “receiving the knowledge representation” which is insignificant extra-solution activity. A ‘synthesized’ representation is not significantly more than the judicial exception.)
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
With respect to claims 11, 24 and 37:
2A Prong 1: the claim recites a judicial exception.
wherein modifying the knowledge representation based on the comparing comprises modifying the knowledge representation when a ratio of a number of the one or more content items correctly classified as being relevant to the object of interest to a total number of the one or more content items in the labeled data is less than the target threshold for the precision value (evaluation, modifying the knowledge representation when a ratio is less than a threshold)
With respect to claims 12, 25 and 38:
2A Prong 1: the claim recites a judicial exception
wherein modifying the knowledge representation based on the comparison comprises modifying the knowledge representation when a ratio of a number of the one or more content items correctly classified as being relevant to the object of interest to a total number of the one or more content items classified as being relevant to the object of interest is less than the target threshold for the recall value (evaluation, modifying the knowledge representation when a ratio is less than a threshold)
Response to Arguments
Applicant's arguments with respect to the rejection of the claims under 35 U.S.C. 101 have been fully considered but they are not persuasive:
Argument: (p. 12) The Claims Provide an Improvement in the Field of Machine Learning and to Machine Classification Systems As a preliminary note, claim 1 (and similarly claims 14 and 27) has been amended to recite a computer-implemented method directed towards improving machine learning by modifying a knowledge representation based on a machine-learning classifier.
Response: Applicant’s arguments rely on language solely recited in preamble recitations in claim 1. When reading the preamble in the context of the entire claim, the recitation “improving machine-learning by modifying a knowledge representation based on a machine-learning classifier” is not limiting because the body of the claim “modifying… the knowledge representation based on a comparison of the classification by the machine-learning classifier for each content item of the labeled data to the known label of each respective content item, the modifying comprising…” describes a complete invention and the language recited solely in the preamble does not provide any distinct definition of any of the claimed invention’s limitations. Thus, the preamble of the claim(s) is not considered a limitation and is of no significance to claim construction. See Pitney Bowes, Inc. v. Hewlett-Packard Co., 182 F.3d 1298, 1305, 51 USPQ2d 1161, 1165 (Fed. Cir. 1999). See MPEP § 2111.02.
Further, the recitation “improving machine-learning” is an intended result, therefore the feature is not given weight. The specific process steps for achieving the claimed improvement will be given patentable weight. However, language reciting the intended result will not be given weight.
The recitation “modifying… the knowledge representation based on a comparison…” is an abstract idea, which is not sufficient to provide improvement. If the limitation is directed to an exception, it cannot provide an improvement. (MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement…”)
Argument: (p. 12) The Applicant respectfully submits that the subject matter of the claims is not that which may be practically performed in the human mind or by pen and paper. The solution provided by the present application and recited in the claims is intended for environments with vast arrays of content… A challenge in the present application is to how to improve machine learning, in a large set of content… By automating the repetitive execution of machine-learning classifiers and modification of knowledge representations of large-scale…
Response: Applicant argues the claims cannot be performed mentally due to scale or large amount of data. However, mental process is not whether a human can perform the steps at scale, but whether the concept is a mental process or abstract idea.
Argument: (p. 13) The current rejection characterizes the claims at a high level as merely "classifying," "modifying," and "repeating…" As disclosed, the invention is not a disembodied evaluation process… The specification repeatedly describes this as a feedback- driven process for modifying a knowledge representation based on observed classifier results… It can also be said that the claimed subject matter improves the operation of a broader machine learning pipeline… This describes a recursive refinement mechanism (in other words, an improvement) within the machine learning workflow…
Response: Under step 2A, prong Two, each of the "classifying," "modifying," and "repeating…" steps is a mental process. Therefore, those steps are directed to mental processes. (If a claim recites a limitation that can practically be performed in the human mind, with-- or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea – MPEP 2106.04(a)(2)(III)(B).) Further, as explained above, mental processes are not sufficient to provide improvement.
Under step 2A, prong Two and step 2B, when incorporating the exceptions with the additional elements to evaluate the claim as a whole, the combination of mental steps is still a combined mental step of those mental steps, i.e. the combination of mental steps does not make it patent-eligible.
Argument: (p. 14-15) Further, another solution provided by the present application is intended for sparse data environments… Such feature engineering aspects of the novel technique can directly improve the training of a machine learning model… Using this prepared featurized data sourced from the knowledge representation would provide a greater amount of data for use in data analysis or training of machine learning models… Improvements that the claimed techniques provide in the training of machine learning models is described throughout the instant application, for example at paragraph 69… paragraph 78… paragraph 88…
Response: Paragraph 69 describes the data sparsity problem, and by leveraging attributes from the knowledge representation for the sparsity problem to generate more data. The paragraph may be related to the limitation “classify each of the one or more content items of the labeled data using one or more attributes derived from the knowledge representation as a feature” which is an abstract idea, therefore is not sufficient to provide an improvement.
Paragraph 78 describes training a classifier with intended result of higher accuracy with less training data. The claim does not require “training” a machine-learning classifier. Instead, it merely recites “executing the machine-learning classifier.”
Paragraph 88 describes the precision and recall of the machine-learning classifier have been improved. The paragraph may be related to the limitation “repeating… the classifying and modifying until a target threshold is achieved for at least one of: a precision value; and a recall value” which is an abstract idea, therefore is not sufficient to provide an improvement. Further, precision and recall are not parameters that are tuned or adjusted during the training of a machine-learning classifier. Instead, they are evaluation metrics used to measure the quality or performance of the model.
Argument: (p. 15) Claim 1 has been amended to recite the processor performs the steps of the method and, therefore, claim 1 is specifically directed to only a method which can be performed by a computing device and is not directed to a method which could be performed by a human…
Response: The mere presence of a physical device (i.e., the processor) does not preclude the claim from being considered ineligible under 35 U.S.C. 101, if the physical device is claimed in a generic manner. From the 2019 PEG: "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 is still in the mental processes grouping unless the claim limitation cannot practically be performed in the mind".
Argument: (p. 16-17) Applicant respectfully submits that the present claims provide clear improvements in computer functioning and in the technical field of computer technology (in particular, machine learning and to machine classification systems). The claimed subject matter addresses challenges of stagnant or inaccurate feature engineering… This is at a scale that would be not just impractical, but impossible for human operators to handle manually… As an example, the claimed invention improves, among other aspects, the technical process of machine learning by modifying a knowledge representation based on a machine-learning classifier in order to refine the knowledge representation in a large volume of content… As a part of the solution to these problems, the claimed invention uses semantic analysis techniques to improve the refinement of information… This is a technical transformation of an encoded symbolic data structure - not a mental process or evaluation.
Response: Applicant argues the claims cannot be performed mentally due to scale or large amount of data. However, mental process is not whether a human can perform the steps at scale, but whether the concept is a mental process or abstract idea.
Argument: (p. 17-18) Moreover, the modified knowledge representation represents new data that has been created based on the addition of concepts or modification of weights in the knowledge representation… This process may continue iteratively until a target precision or recall threshold is achieved… the claimed knowledge representation modification component is used to improve downstream classifier operation through repeated machine-driven refinement of the encoded representation.
Response: The argument may be related to the limitation “modifying the knowledge representation based on a comparison of the classification… repeating the classifying and modifying…” which are abstract ideas, therefore are not sufficient to provide an improvement.
Argument: (p. 18) As another example, the claimed invention may be used in a computer system and by a digital information storage and/or retrieval system to better identify digital content. This ability inherently increases the efficiency of the computer system. It can be also said that the claimed invention provides an improvement in the technical field of digital information searching and retrieval.
Response: The argument states the invention can be better identify digital content, which is an intended result, therefore the feature is not given weight. The specific process steps for achieving the claimed improvement will be given patentable weight. However, features for the intended result will not be given weight.
Argument: (p. 19-20) Claims Provide an Inventive Concept… Specifically, and as claimed, modifying the knowledge representation is based on a comparison of the classification by the machine-learning classifier for each content item of the labeled data to the known label of each respective content item to obtain a modified knowledge representation… In this way, the claimed limitations generate new data and do not merely recite an abstract idea as alleged by the Examiner.
Response: The argument may be related to the limitation “modifying the knowledge representation based on a comparison of the classification…” which are abstract ideas, therefore are not sufficient to provide an improvement.
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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The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Xu ("Multi-Modality Transfer based on Multi-Graph Optimization for Domain Adaptive Video Concept Annotation" 20101114) teaches using labeled samples in the target domain [labeled data comprising one or more content items external to the object of interest].
Xu further teaches Eq.(2), Yi is the initial label of the samples from the target domain [the known label of each respective content item] and fgi is the predicted score (the classification result of a classification task) generated from the auxiliary classifiers Fg(g=1:G) [the classification by the machine-learning classifier] in the source domain. However, Yi is not compared to fgi in Eq.(2). (Yi is in the second term, and fgi is in the third term in Eq.(2).) Therefore, Xu doesn't teach: [a comparison of the classification by the machine-learning classifier for each content item of the labeled data to the known label of each respective content item].
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SU-TING CHUANG whose telephone number is (408)918-7519. The examiner can normally be reached Monday - Thursday 8-5 PT.
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/S.C./Examiner, Art Unit 2146
/USMAAN SAEED/Supervisory Patent Examiner, Art Unit 2146
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