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, 8, and 15 are independent.
Claim Objections
Claims 13 and 19 objected to because of the following informalities: Claim 13 and 19 recite "a supervised neutral network". Examiner believes this is meant to state "neural network" instead. Appropriate correction is required.
Specification
The disclosure is objected to because of the following informalities: Para 0007, 0063, 0070, 0101, and 0108 recite "neutral network". Examiner believes this is meant to state a "neural network" instead.
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 1-7 are directed to a method, claims 8-14 are directed to a system, and claims 15-20 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).)
classifying, (This step for classifying first data is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
A computer-implemented method for managing user reports, 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 data agents to a supervised machine learning classifier, wherein a data agent of the set of data agents is determined for a cluster of prior report statements generated by an unsupervised machine learning clustering classifier based on one or more second data sets generated for a plurality of prior user reports, wherein the plurality of prior user reports is classified by the unsupervised machine learning clustering classifier into a plurality of clusters including the cluster of prior report 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 managing user reports, 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 data agents to a supervised machine learning classifier, wherein a data agent of the set of data agents is determined for a cluster of prior report statements generated by an unsupervised machine learning clustering classifier based on one or more second data sets generated for a plurality of prior user reports, wherein the plurality of prior user reports is classified by the unsupervised machine learning clustering classifier into a plurality of clusters including the cluster of prior report 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 8: see the rejection of claim 1 above. Same rationale applies.
2A Prong 2 & 2B: The claim recites another additional element “A system, comprising: one or more memories configured to store a plurality of prior user reports including multiple prior report statements; and at least one processor coupled to the one or more 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 15: 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, and 9
2A Prong 1: The claim does not recite an Abstract idea.
2A Prong 2 & 2B:
wherein the data agent includes a description of user reports included in the database managed by the data agent, and the description of user reports is determined by a language model for the cluster of prior report statements. (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the data agent- See MPEP 2106.05(h).)
Regarding Claims 3 and 10
2A Prong 1: The claim does not recite an 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 4, 11, and 17
2A Prong 1:
determining, by the supervised machine learning classifier, that the user query corresponds to a second data agent including a second description of user reports in a second database managed by the second data agent. (This step is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., judgment/evaluation).)
2A Prong 2:
wherein the data agent is a first data agent, and the method further comprises: receiving a user query; (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).)
2B:
wherein the data agent is a first data agent, and the method further comprises: receiving a user query; (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))))
Regarding Claim 5, 12, and 18
2A Prong 1:
wherein the one or more second data sets are generated by word embedding for a set of keywords and a set of summaries corresponding to the plurality of prior user reports. (This step is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., judgment/evaluation).)
2A Prong 2 & 2B: The claim does not recite any additional elements.
Regarding Claim 6
2A Prong 1: The claim does not recite an Abstract idea.
2A Prong 2 & 2B:
wherein the word embedding further includes truncated embedding to reduce a dimensionality of the word embedding to generate the second data set. (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the word embedding - See MPEP 2106.05(h).)
Regarding Claim 7
2A Prong 1:
wherein the first data set is generated by sentence embedding of the list of keywords and the summary of the user report, and wherein the sentence embedding includes SentenceBERT, Universal Sentence Encoder, FastText, or a conditional masked language modelling. (This step is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., judgment/evaluation).)
2A Prong 2 & 2B: The claim does not recite any additional elements.
Regarding Claims 13 and 19
2A Prong 1: The claim does not recite an 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 Claims 14 and 20
2A Prong 1: The claim does not recite an 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 Claim 16
2A Prong 1: The claim does not recite an Abstract idea.
2A Prong 2 & 2B:
wherein the data agent includes a description of user reports included in the database managed by the data agent, and the description of user reports is determined by a language model for the cluster of prior report statements (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the data agent- See MPEP 2106.05(h).), and 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).)
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) 1-6, and 8-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cakir et al. (US 20240086445 A1, hereinafter "Cakir") in view of Xu et al. (US 20240378225 A1, hereinafter "Xu").
Regarding Claim 1
Cakir discloses: A computer-implemented method for managing user reports ([Para 0031-0033, 0056, and Fig 1] describes a computer for trouble tickets (i.e. user reports).), comprising:
generating, by at least one computer processor, a first data set ([Para 0069-0070 and Fig 8-9] describes user input 816 for generating trouble tickets (i.e. first data set). [Para 0079-0081 and Fig 11] also describes input data 1102 (i.e. first data set).);
providing a set of data agents to a supervised machine learning classifier [Para 0074 and Fig 8-9] describes topic clusters 914 which would be the agents provided to the trained model 916. In figure 8 examiner interprets 806 as the agents. ([Para 0068-0073 and Fig 8-9] Examiner also interprets the annotated database 810 (i.e. data agent) provided to recommender system 818 (i.e. supervised machine learning). [0077 and Fig 8] describes the recommender system 818 as a supervised learning technique. [Para 0080-0081 and Fig 11] describes labeled data clusters and assignments 1108 (i.e. data agents) that are provided to weakly supervised SVM classification (i.e. supervised machine learning classifier).), wherein a data agent of the set of data agents is determined for a cluster of prior report statements generated by an unsupervised machine learning clustering classifier based on one or more second data sets generated for a plurality of prior user reports ([Para 0067-0068, 0073, and Fig 8-9] describes the annotated database 810 (i.e. data agent) determined by clustered tickets (i.e. unsupervised machine learning clustering) based on a database tickets 802 (i.e. second data sets and user reports.). [Para 0079-0081 and fig 11] describes input data 1102 related to trouble tickets (i.e. second data sets) clustered by an unsupervised machine learning model 1104.), wherein the plurality of prior user reports is classified by the unsupervised machine learning clustering classifier into a plurality of clusters including the cluster of prior report statements ([Para 0067-0068, 0073, and Fig 8-9] describes the database tickets (i.e. prior user reports) classified by a clustering model (unsupervised machine learning) into clusters. [Para 0079-0081 and fig 11] describes input data 1102 related to trouble tickets (i.e. second data sets) clustered by an unsupervised machine learning model 1104.); and
classifying, by the supervised machine learning classifier, based on the first data set, the user report into a database managed by the data agent. ([0068-0072, 0077, and Fig 8-9] describes classifying by the recommender system 818 (i.e. supervised learning) based on user input 816 (i.e. first data set) tickets (i.e. user report) into look-up table 812 (i.e. a database managed by the data agent ) . [Para 0074] states “The agent application 902 searches an emulation failures look up table (LUT) 920 (also referred to as the expert generated resolution LUT 812 in FIG. 8) to map the issue to relevant corrective steps associated with another user with a similar or identical setup.” [Para 0079-0081 and Fig 11] also describes classifying with SVM 1110 (i.e. supervised learning).)
Cakir does not explicitly disclose: generating, by at least one computer processor, a first data set based on a list of keywords and a summary of a user report;
However, Xu discloses in the same field of endeavor: generating, by at least one computer processor, a first data set based on a list of keywords and a summary of a user report ([Para 0001, 0018, 0026, 0029, 0034, 0040-0041, 0049, 0056-0061, and Fig 1-2] describes generating a dataset based on keywords and summary of text data. [Para 0011] discloses report data for text data.)
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 Keyword analysis disclosed by Xu into the method of Machine learning and natural language processing disclosed by Cakir to generate a user report based on keywords and a summary. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of Keyword analysis disclosed by Xu 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 providing an improved system for document searching and analysis.
Regarding Claim 8
Cakir in view of Xu discloses: A system, comprising: one or more memories configured to store a plurality of prior user reports including multiple prior report statements; and at least one processor coupled to the one or more memories and configured to perform operations ([Para 0031-0033 and Fig 1], Cakir discloses the system.) comprising: (Claim 8 is a system claim that corresponds to claim 1 and the rest of the limitations are rejected on the same ground)
Regarding Claim 15
Cakir in view of Xu 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 0031-0033, 0116, and Fig 1], Cakir discloses the non-transitory computer-readable medium.) comprising: (Claim 15 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 2
Cakir in view of Xu discloses: The computer-implemented method of claim 1, wherein the data agent includes a description of user reports included in the database managed by the data agent, and the description of user reports is determined by a language model for the cluster of prior report statements. ([Para 0067, 0073-0076, and Fig 8-11], Cakir describes natural language processing.)
Regarding Claim 3
Cakir in view of Xu discloses: The computer-implemented method of claim 2, wherein the language model includes a probabilistic language model or a neural network based language model. ([Para 0047, 0067, 0073-0076, and Fig 8-11], Cakir describes natural language processing probabilistic language model or a neural network based.)
Regarding Claim 4
Cakir in view of Xu discloses: The computer-implemented method of claim 2, wherein the data agent is a first data agent, and the method further comprises: receiving a user query; and determining, by the supervised machine learning classifier, that the user query corresponds to a second data agent including a second description of user reports in a second database managed by the second data agent. ([0068-0072, 0077, and Fig 8-9], Cakir describes user input 816 with search string (i.e. user query) and determining with recommender system (i.e. supervised learning) corresponding to look-up table 812 (i.e. second description in second database) . [Para 0074] states “The agent application 902 searches an emulation failures look up table (LUT) 920 (also referred to as the expert generated resolution LUT 812 in FIG. 8) to map the issue to relevant corrective steps associated with another user with a similar or identical setup.”
Regarding Claim 5
Cakir in view of Xu discloses: The computer-implemented method of claim 1, wherein the one or more second data sets are generated by word embedding for a set of keywords and a set of summaries corresponding to the plurality of prior user reports. ([Para 0080 and Fig 11], Cakir describes an embedding model 1106 for the input data 1102. [Para 0015, 0017, 0046, 0059], Xu also describes tokenization of text input.)
Regarding Claim 6
Cakir in view of Xu discloses: The computer-implemented method of claim 5, wherein the word embedding further includes truncated embedding to reduce a dimensionality of the word embedding to generate the second data set. ([Para 0080 and Fig 11], Cakir describes an embedding model 1106 for the input data 1102. [ 0015 0017 0046 0059], Xu also describes tokenization of text input. Examiner interprets tokenization and embedding of input text as reducing dimensionality.)
Regarding Claim 9
(Claim 9 recites analogous limitations to claim 2 and therefore is rejected on the same ground as claim 2.)
Regarding Claim 10
(Claim 10 recites analogous limitations to claim 3 and therefore is rejected on the same ground as claim 3.)
Regarding Claim 11
(Claim 11 recites analogous limitations to claim 4 and therefore is rejected on the same ground as claim 4.)
Regarding Claim 12
(Claim 12 recites analogous limitations to claim 5 and therefore is rejected on the same ground as claim 5.)
Regarding Claim 17
(Claim 17 recites analogous limitations to claim 4 and therefore is rejected on the same ground as claim 4.)
Regarding Claim 18
(Claim 18 recites analogous limitations to claim 5 and therefore is rejected on the same ground as claim 5.)
Regarding Claim 13
Cakir in view of Xu discloses: The system of claim 9, 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 0077 and Fig 8], Cakir describes the recommender system as a K-nearest neighbors (KNN). [Para 0081 and Fig 11], Cakir describes Weakly supervised support vector machine (SVM).)
Regarding Claim 14
Cakir in view of Xu discloses: The system of claim 9, 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 0096 and claim 8], Cakir describes unsupervised density based clustering.)
Regarding Claim 16
Cakir in view of Xu discloses: The non-transitory computer-readable medium of claim 15, wherein the data agent includes a description of user reports included in the database managed by the data agent, and the description of user reports is determined by a language model for the cluster of prior report statements ([Para 0067, 0073-0076, and Fig 8-11], Cakir describes natural language processing.), and wherein the language model includes a probabilistic language model or a neural network based language model. ([Para 0047, 0067, 0073-0076, and Fig 8-11], Cakir describes natural language processing probabilistic language model or a neural network based.)
Regarding Claim 19
(Claim 19 recites analogous limitations to claim 13 and therefore is rejected on the same ground as claim 13.)
Regarding Claim 20
(Claim 20 recites analogous limitations to claim 14 and therefore is rejected on the same ground as claim 14.)
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cakir in view of Xu and Rath (US 20210328888 A1, hereinafter "Rath").
Regarding Claim 7
Cakir in view of Xu discloses: The computer-implemented method of claim 1, wherein the first data set is generated by sentence embedding of the list of keywords and the summary of the user report ([Para 0017 0059], Xu describes the input text may be split into sentences and token representations.),
Cakir in view of Xu does not explicitly disclose: wherein the sentence embedding includes SentenceBERT, Universal Sentence Encoder, FastText, or a conditional masked language modelling.
However, Rath discloses in the same field of endeavor: wherein the sentence embedding includes SentenceBERT, Universal Sentence Encoder, FastText, or a conditional masked language modelling. ([Para 0048 and Fig 3C] describes sentence embedding (such as Universal Sentence Encoder or Sent2vec).)
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 Ticket Summarizer disclosed by Rath into the method of Cakir in view of Xu to includes a Universal Sentence Encoder. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of Ticket Summarizer disclosed by Rath 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 generate a communication representation vector.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Givental et al. (US 20210281592 A1) describes a hybrid learning method with agents (Fig 1).
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.
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/TEWODROS E MENGISTU/Examiner, Art Unit 2127
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