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 .
Claims 1-20 are pending for examination. Claims 1, 9, and 16 are independent.
Specification
The disclosure is objected to because of the following informalities: Para 0006, 0057, 0064 states "neutral network", Examiner believes the specification is meant to state "neural network" instead.
Appropriate correction is required.
Claim Objections
Claims 5 and 13 objected to because of the following informalities: Claim 5 states "neutral network", Examiner believes the . Appropriate correction is required.
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 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
According to the first part of the analysis, in the instant case, claims 1-8 are directed to a method, claims 9-15 are directed to a system, and claims 16-20 is directed to a non-transitory computer-readable medium. Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Regarding Claim 1:
2A Prong 1:
generating, (This step for generating first data is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation/judgment).)
generating, (This step for generating a set of labels is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation/judgment).)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
A computer-implemented method for classing a user response statement, comprising: by at least one computer processor (The computer-implemented method comprising processor is understood to be generic computer elements - See MPEP 2106.05(f).)
providing a set of target words to a supervised machine learning classifier, wherein a target word of the set of target words is determined by a language model for a cluster of prior user response statements generated by an unsupervised machine learning clustering classifier based on a second data set generated for a plurality of prior user response statements provided by a group of users to a set of prior question statements, wherein the plurality of prior user response statements is classified by the unsupervised machine learning clustering classifier into a plurality of clusters including the cluster of prior user response statements; (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
by the supervised machine learning classifier (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a generic machine learning as a tool to perform the abstract idea (e.g., generate labels) - see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A computer-implemented method for classing a user response statement, comprising: by at least one computer processor (The computer-implemented method comprising processor is understood to be generic computer elements - See MPEP 2106.05(f).)
providing a set of target words to a supervised machine learning classifier, wherein a target word of the set of target words is determined by a language model for a cluster of prior user response statements generated by an unsupervised machine learning clustering classifier based on a second data set generated for a plurality of prior user response statements provided by a group of users to a set of prior question statements, wherein the plurality of prior user response statements is classified by the unsupervised machine learning clustering classifier into a plurality of clusters including the cluster of prior user response statements; (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity of transmitting and receiving data as identified by the court (MPEP2106.05(d)(ll)(i))))
by the supervised machine learning classifier (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a generic machine learning as a tool to perform the abstract idea (e.g., generate labels) - see MPEP 2106.05(f).)
The additional elements as disclosed above in combination of the abstract idea
are not sufficient to amount to significantly more than the judicial exception as they are
well, understood, routine and conventional activity as disclosed in combination
of generic computer functions that are implemented to perform the disclosed abstract idea above.
Regarding Claim 9: see the rejection of claim 1 above. Same rationale applies.
2A Prong 2: The claim recites another additional element “A system, comprising: one or more memories configured to store a question statement and a user response statement provided by a user in response to the question statement (The memory is understood to be a generic computer element - See MPEP 2106.05(f). The step directed to storing information, is understood to be insignificant extra- solution activity and data gathering. See MPEP 2106.05(g).); and at least one processor each coupled to at least one of the memories and configured to perform operations comprising: (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))”
2B:
A system, comprising: one or more memories configured to store a question statement and a user response statement provided by a user in response to the question statement (The memory is understood to be a generic computer element - See MPEP 2106.05(f). This step is directed to storing information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity as identified by the court (MPEP 2106.05(d)(ll)(IV))))); and at least one processor each coupled to at least one of the memories and configured to perform operations comprising: (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))”
Regarding Claim 16: see the rejection of claim 1 above. Same rationale applies.
2A Prong 2 & 2B: The claim recites another additional element “A non-transitory computer-readable medium having instructions stored thereon that, when executed by at least a computing device, cause the computing device to perform operations comprising:” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claims 2, 11, and 17
2A Prong 1:
wherein the second data set is generated by word embedding for the plurality of prior user response statements and the set of prior question statements. (This step for is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation/judgment).)
2A Prong 2 & 2B: The claim does not recite any additional elements.
Regarding Claim 3
2A Prong 1:
wherein the word embedding further includes truncated embedding to reduce a dimensionality of the word embedding to generate the second data set. (This step for is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation/judgment).)
2A Prong 2 & 2B: The claim does not recite any additional elements.
Regarding Claims 4, 12, and 18
2A Prong 1:
wherein the first data set is generated by sentence embedding of the user response statement and the question statement, (This step for is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation/judgment).)
2A Prong 2 & 2B:
wherein the sentence embedding includes SentenceBERT, Universal Sentence Encoder, FastText, or a conditional masked language modelling. (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies sentence embedding - See MPEP 2106.05(h).)
Regarding Claim 5, 13, and 19
2A Prong 1: The claim does not recite any Abstract idea.
2A Prong 2 & 2B:
wherein the supervised machine learning classifier includes a supervised neutral network, a support vector machine (SVM) classifier, a random forest classifier, or a K nearest neighbors supervised machine learning classifier. (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the supervised machine learning classifier - See MPEP 2106.05(h).)
Regarding Claim 6
2A Prong 1: The claim does not recite any Abstract idea.
2A Prong 2 & 2B:
wherein the language model includes a probabilistic language model or a neural network based language model. (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the language model - See MPEP 2106.05(h).)
Regarding Claims 7, 14, and 20
2A Prong 1: The claim does not recite any Abstract idea.
2A Prong 2 & 2B:
wherein the unsupervised machine learning clustering classifier includes an Ordering Points To Identify the Clustering Structure (OPTICS) algorithm, a density-based cluster ordering algorithm, or a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the unsupervised machine learning clustering classifier - See MPEP 2106.05(h).)
Regarding Claims 8 and 15
2A Prong 1: The claim does not recite any Abstract idea.
2A Prong 2 & 2B:
wherein the unsupervised machine learning clustering classifier includes the OPTICS algorithm, and further includes an Agglomerative clustering algorithm to classify noises generated by the OPTICS clustering algorithm. (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the unsupervised machine learning clustering classifier - See MPEP 2106.05(h).)
Regarding Claim 10
2A Prong 1: The claim does not recite any Abstract idea.
2A Prong 2 & 2B:
wherein the user response statement and the plurality of prior user response statements are user response statements to an open-ended survey question statement, and the question statement and the set of prior question statements are open-ended survey question statements. (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the user response statement - See MPEP 2106.05(h).)
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim(s) 1, 5-6, 9, 13, 16, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khani et al. (US 20240370662 A1, hereinafter "Khani") in view of Wilson et al. (US 20240037583 A1, hereinafter "Wilson") .
Regarding Claim 1
Khani discloses: A computer-implemented method for classing a ([Para 0021, 0050-0067, Fig 1 and Fig 6-7] disclose a computer implement method for classifying.), comprising:
generating, by at least one computer processor, a first data set based on ([Para 0026] describes training datasets for an NLP model (i.e. a first data set).)
providing a set of target words to a supervised machine learning classifier, wherein a target word of the set of target words is determined by a language model for a cluster of learning clustering classifier based on a second data set generated for a plurality of ([Para 0001, 0026-0029, 0045-0049, Fig 1-2 and Fig 5] describes providing synthetic training data (i.e. target words) to a NLP model (i.e. supervised model) determined by a LLM (i.e. language model) for a cluster based on a validation dataset (i.e. second dataset). [Para 0028-0029, 0033, 0044] describes clustering (i.e. unsupervised machine learning). [Para 0042, 0049] describes labeled datasets to train the NPL model (i.e. supervised).)
generating, by the supervised machine learning classifier, a set of labels selected from the set of target words for the ([Para 0023 0026] describes the NLP model generating language related predictions (i.e. probabilities) represented by the training data (i.e. first dataset).)
Khani does not explicitly disclose: a first data set based on a question statement and a user response statement provided by a user in response to the question statement; a cluster of prior user response statements generated by an unsupervised machine learning clustering classifier […] a plurality of prior user response statements provided by a group of users to a set of prior question statements;
However, Wilson discloses in the same field of endeavor: a first data set based on a question statement and a user response statement provided by a user in response to the question statement; ([Para 0009, 0110-0112] describes training data (i.e. first dataset) comprising personal data set of each of the plurality of the first users including a data entry regarding a response given by each respective first user to a survey.)
a cluster of prior user response statements generated by an unsupervised machine learning clustering classifier based on a second data set generated for a plurality of prior user response statements provided by a group of users to a set of prior question statements, wherein the plurality of prior user response statements is classified by the unsupervised machine learning clustering classifier into a plurality of clusters including the cluster of prior user response statements; ([Para 0114] describes clustering users with similar response to one of the queries of a survey. [Para 0085-0086, 0173, and Fig 6-9] describes test/validation data. [Para 0008 and Para 0010] describes performing cluster analysis via unsupervised learning.)
generating, by the supervised machine learning classifier, a set of labels selected from the set of target words for the user response statement represented by the first data set, wherein the set of labels includes a first label with a first probability and a second label with a second probability. ([Para 0070, 0119-0123, 0128-0130, and Fig 7] describes a supervised model for predicting survey responses.)
It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the function of training machine learning models based on survey datasets disclosed by Wilson into the method of Natural Language Processing Models disclosed by Khani to analyze datasets-based question statements and user response statements. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of training machine learning models based on survey datasets disclosed by Wilson as all the references are in the field of machine learning. A person of ordinary skill of the art would have been motivated to perform the combination for being able to use of machine learning processes to predict survey related data.
Regarding Claim 9
Khani in view of Wilson discloses: A system, comprising: one or more memories configured to store a question statement and a user response statement provided by a user in response to the question statement; and at least one processor each coupled to at least one of the memories and configured to perform operations ([Para 0055, 0089-0100, and Fig 2], Wilson describes survey data in storage device 224 (i.e. memory).) comprising: (Claim 9 is a system claim that corresponds to claim 1 and the rest of the limitations are rejected on the same ground)
Regarding Claim 16
Khani in view of Wilson discloses: A non-transitory computer-readable medium having instructions stored thereon that, when executed by at least a computing device, cause the computing device to perform operations ([Para 0006, 0061, and Fig 6-7], Khani) comprising: (Claim 16 is a non-transitory computer-readable medium claim that corresponds to claim 1 and the rest of the limitations are rejected on the same ground)
Regarding Claim 5
Khani in view of Wilson discloses: The computer-implemented method of claim 1, wherein the supervised machine learning classifier includes a supervised neutral network, a support vector machine (SVM) classifier, a random forest classifier, or a K nearest neighbors supervised machine learning classifier. ([Para 0026], Khani “the training mechanism 170 uses labeled training data to train the NLP model 120 via deep neural network(s)”. [Para 0070], Wilson describes a supervised machine learning classifier.)
Regarding Claim 6
Khani in view of Wilson discloses: The computer-implemented method of claim 1, wherein the language model includes a probabilistic language model or a neural network based language model. ([Para 0019, 0024 and Fig 1], Khani describe probabilistic LLM models.)
Regarding Claim 13
(Claim 13 recites analogous limitations to claim 5 and therefore is rejected on the same ground as claim 5.)
Regarding Claim 19
(Claim 19 recites analogous limitations to claim 5 and therefore is rejected on the same ground as claim 5.)
Claim(s) 2-4, 11-12, and 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khani in view of Wilson and Pouran Ben Veyseh et al. (US 20230252237 A1, hereinafter "Pouran").
Regarding Claim 2
Khani in view of Wilson discloses: The computer-implemented method of claim 1, wherein the second data set is generated by ([Para 0029 and Claim 7], Khani describes clustering of the validation dataset 240 by using general purpose embeddings.)
Khani in view of Wilson does not explicitly disclose: word embedding;
However, discloses in the same field of endeavor: wherein the second data set is generated by word embedding; ([Para 0056, 0073, 0112, 0119 and Fig 5] discloses word embeddings.)
It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the function of natural language processing disclosed by Pouran into the method of Khani in view of Wilson to generate word embeddings. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of natural language processing disclosed by Pouran as all the references are in the field of machine learning. A person of ordinary skill of the art would have been motivated to perform the combination for being able to evaluate text at a word level for a more nuanced understanding of language.
Regarding Claim 3
Khani in view of Wilson and Pouran discloses: The computer-implemented method of claim 2, wherein the word embedding further includes truncated embedding to reduce a dimensionality of the word embedding to generate the second data set. ([para 0112, 0119-0120], Pouran)
Regarding Claim 4
Khani in view of Wilson and Pouran discloses: The computer-implemented method of claim 1, wherein the first data set is generated by sentence embedding of the user response statement and the question statement, and wherein the sentence embedding includes SentenceBERT, Universal Sentence Encoder, FastText, or a conditional masked language modelling. ([Para 0029 and Claim 7], Khani discloses sentence embeddings. [Para 0109 0090], Pouran describes sentence embeddings input to a pre-trained BERT.)
Regarding Claim 11
(Claim 11 recites analogous limitations to claim 2 and therefore is rejected on the same ground as claim 2.)
Regarding Claim 12
(Claim 12 recites analogous limitations to claim 4 and therefore is rejected on the same ground as claim 4.)
Regarding Claim 17
(Claim 17 recites analogous limitations to claim 2 and therefore is rejected on the same ground as claim 2.)
Regarding Claim 18
(Claim 18 recites analogous limitations to claim 4 and therefore is rejected on the same ground as claim 4.)
Claim(s) 7-8, 14-15, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khani in view of Wilson and Rizk (US 20230078260 A1, hereinafter "Rizk").
Regarding Claim 7
Khani in view of Wilson discloses: The computer-implemented method of claim 1, wherein the unsupervised machine learning clustering classifier;
Khani in view of Wilson does not explicitly disclose: wherein the unsupervised machine learning clustering classifier includes an Ordering Points To Identify the Clustering Structure (OPTICS) algorithm, a density-based cluster ordering algorithm, or a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm.
However, Rizk discloses in the same field of endeavor: wherein the unsupervised machine learning clustering classifier includes an Ordering Points To Identify the Clustering Structure (OPTICS) algorithm, a density-based cluster ordering algorithm, or a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. ([Para 0007, 0045, and 0080])
It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the function of Clustering disclosed by Rizk into the method of Khani in view of Wilson to the particular clustering methods. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of Clustering disclosed by Rizk as all the references are in the field of machine learning. A person of ordinary skill of the art would have been motivated to perform the combination for being able to perform various clustering techniques.
Regarding Claim 8
Khani in view of Wilson and Rizk discloses: The computer-implemented method of claim 7, wherein the unsupervised machine learning clustering classifier includes the OPTICS algorithm, and further includes an Agglomerative clustering algorithm to classify noises generated by the OPTICS clustering algorithm. ([Para 0007, 0045, and 0080], Rizk)
Regarding Claim 14
(Claim 14 recites analogous limitations to claim 7 and therefore is rejected on the same ground as claim 7.)
Regarding Claim 15
(Claim 15 recites analogous limitations to claim 8 and therefore is rejected on the same ground as claim 8.)
Regarding Claim 20
(Claim 20 recites analogous limitations to claim 7 and therefore is rejected on the same ground as claim 7.)
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khani in view of Wilson and Long et al. (US 20200074294 A1, hereinafter "Long").
Regarding Claim 10
Khani in view of Wilson discloses: The system of claim 9, wherein the user response statement and the plurality of prior user response statements are user response statements to an open-ended survey question statement, and the question statement and the set of prior question statements are open-ended survey question statements.
Khani in view of Wilson does not explicitly disclose: wherein the user response statement and the plurality of prior user response statements are user response statements to an open-ended survey question statement, and the question statement and the set of prior question statements are open-ended survey question statements.
However, Long discloses in the same field of endeavor: wherein the user response statement and the plurality of prior user response statements are user response statements to an open-ended survey question statement, and the question statement and the set of prior question statements are open-ended survey question statements. ([Para 0046, 0170, Fig 2, and Fig 7-8] describes open ended survey question.)
It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the function of Survey Creation for Machine learning disclosed by Long into the method of Khani in view of Wilson to comprise open-ended survey question statements. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of Survey Creation for Machine learning disclosed by Long as all the references are in the field of machine learning model. A person of ordinary skill of the art would have been motivated to perform the combination for being able to have survey questions in various formats.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Singh et al. (US 20240144921 A1) also describes unsupervised learning with LLMs.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TEWODROS E MENGISTU whose telephone number is (571)270-7714. The examiner can normally be reached Mon-Fri 9:30-5:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ABDULLAH KAWSAR can be reached at (571)270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/TEWODROS E MENGISTU/ Examiner, Art Unit 2127