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 .
Specification
The disclosure is objected to because of the following informalities: para [0072] last line “word-topic matrix of probabilities 154” should read “word-topic matrix of probabilities 152” and para [0091] line 1 “Block 508” should read “Block 510”.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Using the subject matter eligibility test from page 74621 of the Federal Register Notice titled “2014 Interim Guidance on Patent Subject Matter Eligibility,” a two-step process is performed. Under step 1, the claims are analyzed to determine if the claim is directed to a process, machine, article of manufacture, or composition of matter. In this case, claims 1-10 are directed to a system, which is a machine or an article of manufacture; claims 11-19 are directed to a method, which is a process; and claim 20 is directed to a computer readable medium, which is a machine or an article of manufacture. Step 2A (part 1 of the Mayo test), using the guidance from pages 50-57 of the Federal Register Vol. 84 No. 4 from Monday, January 7, 2019, requires applying a two-prong inquiry. In Prong One, examiners evaluate whether the claim recites a judicial exception, determining if the claim is directed to a law of nature, a natural phenomenon, or an abstract idea. In this case, claim 1 recites ingesting documents, obtaining vectors, clustering vectors, identifying topics, computing matrices, and computing a topic distribution, which are mental processes and mathematical calculations. In Prong Two, examiners evaluate whether the judicial exception is integrated into a practical application that imposes a meaningful limit on the judicial exception. In this case, additional elements of processor, memory, network interface, and storage medium are generic computing components, while limitations of receiving and outputting data are mere extrasolution activity, and neither integrate the abstract ideas into a practical application.
Step 2B (part 2 of the Mayo test) requires analyzing the claims to determine if they recite additional elements that amount to significantly more than the judicial exception. In this case, the claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea itself.
Regarding claims 1, 11, and 20, ingesting documents, obtaining vectors, clustering vectors, identifying topics, computing matrices, and computing a topic distribution are mental processes and mathematical calculations, which are abstract ideas. For example, a human can receive documents, cluster vectors, and identify topics, while embedding and computation of matrices as well as clustering are mathematical operations. Additional elements of processor, memory, network interface, and storage medium are generic computing components, while limitations of receiving and outputting data are mere extrasolution activity, and neither integrate the abstract ideas into a practical application or constitute significantly more.
Regarding claims 2 and 12, factorizing a matrix is a mathematical operation, which is an abstract idea without integration into a practical application and without significantly more.
Regarding claims 3, 6, 13, and 16, the limitations are further clarifications of the above abstract ideas.
Regarding claims 4 and 14, use of a neural network is a generic computing component, and does not integrate the abstract ideas into a practical application or constitute significantly more.
Regarding claims 5 and 15, transforming documents to vectors and reducing dimensionality are mathematical operations, which is an abstract idea without integration into a practical application and without significantly more.
Regarding claims 7 and 17, computing a graph is a mathematical operation, which is an abstract idea without integration into a practical application and without significantly more.
Regarding claims 8, 10, and 18, rendering a GUI is mere extrasolution activity, and does not integrate the abstract idea into a practical application or constitute significantly more.
Regarding claims 9 and 19, determining topics and generating an output are mental processes, which is an abstract idea. Additional limitations of receiving data and rendering a GUI are mere extrasolution activity, and does not integrate the abstract idea into a practical application or constitute significantly more.
The limitations of the claims, taken alone, do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. Applicable case law cited in the Federal Register includes, but is not limited to: Alice Corp., 134 S. Ct. at 2355-56, Digitech Image Tech., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344 (Fed. Cir. 2014), Benson, 409 U.S. at 63.
See "Preliminary Examination Instructions in view of the Supreme Court Decision in Alice Corporation Pty. Ltd. v. CLS Bank International, et al.," dated June 25, 2014, and the Federal Register notice titled "2014 Interim Guidance on Patent Subject Matter Eligibility" (79 FR 74618).
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.
Claim(s) 1-4, 6-7, 9, 11-14, 16-17, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Anerousis et al. (US 2019/0188319 A1), in view of Starr et al. (US 10,339,160 B2), hereinafter referred to as Starr.
Regarding claim 1, Anerousis teaches:
A server system for automatically generating a plurality of topics from a plurality of digital documents, the server system comprising:
a memory, a network interface, and a processor, the processor operably coupled to the memory and the network interface (Fig. 10 elements 1014, 1016, 1048, para [0069], [0073], where a processor, memory, and network interface are used), the processor configured to:
ingest the plurality of digital documents (para [0027], where service descriptions are received, and para [0049], where the nodes represent services or documents);
process the plurality of digital documents using an encoder to respectively obtain a plurality of vectors (para [0031], [0064], where an encoder is used to determine embeddings or vectors for the services or documents);
cluster the plurality of vectors to identify a plurality of clusters from amongst the plurality of vectors (para [0031], where the vectors are grouped using k-means clustering);
identify the plurality of topics that are respectively associated with the plurality of clusters (para [0031], where topic modeling is applied to the clusters);
compute a word-document matrix of probabilities of words detected in each one of the plurality of digital documents (para [0032], where a term-document matrix is decomposed);
compute a word-topic matrix of probabilities of words detected in each topic (para [0033], where a term-topic matrix is found);
compute a topic-document matrix of probabilities of each one of the plurality of topics in each one of the plurality of digital documents based on the word- document matrix of probabilities and the word-topic matrix of probabilities (para [0034], where a topic-document matrix is determined from the term-document matrix and the term-topic matrix); and
Anerousis does not teach:
compute and output a topic distribution for each of the plurality of digital documents, wherein a given topic distribution comprises one or more probabilities of one or more topics, from amongst the plurality of topics, that are associated with a given digital document.
Starr teaches:
compute and output a topic distribution for each of the plurality of digital documents, wherein a given topic distribution comprises one or more probabilities of one or more topics, from amongst the plurality of topics, that are associated with a given digital document (col. 20 lines 11-36, where the topic-document matrix includes values for a Dirichlet distribution).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Anerousis by using the distribution of Starr (Starr col. 20 lines 11-36) for the documents of Anerousis (Anerousis para [0027]), in order to determine the topic probabilities for individual text documents (Starr col. 20 lines 11-36).
Regarding claim 2, Anerousis in view of Starr teaches:
The server system of claim 1, wherein the processor is configured to compute the topic- document matrix of probabilities of each one of the plurality of topics in each one of the plurality of the digital documents by at least factorizing the word-document matrix of probabilities by the word-topic matrix of probabilities (Anerousis para [0032-34], where matrix factorization is used to determine the topic-document matrix).
Regarding claim 3, Anerousis in view of Starr teaches:
The server system of claim 1, wherein a given topic, from amongst the plurality of topics, comprise one or more statistically significant words found in a given cluster of documents from amongst the plurality of clusters (Anerousis para [0031-32], where a cluster or community has a topic determined by examining the terms co-occurring).
Regarding claim 4, Anerousis in view of Starr teaches:
The server system of claim 1, wherein each of the plurality of digital documents comprises natural language, and the plurality of digital documents are transformed into the plurality of vectors using a natural language neural network model (Anerousis para [0064-65], where a neural network LSTM is used for the embedding of the documents, and para [0027], where the device descriptions are in natural language).
Regarding claim 6, Anerousis in view of Starr teaches:
The server system of claim 1, wherein the processor is configured to compute the topic distribution for each one of the plurality of digital documents as a Dirichlet distribution (Starr col. 20 lines 11-36, where the topic-document matrix includes values for a Dirichlet distribution).
Regarding claim 7, Anerousis in view of Starr teaches:
The server system of claim 1, wherein the processor is further configured to compute a graph of the plurality of vectors, each one of the plurality of vectors associated with a set of statistically significant words observed respectively in each one of the plurality of documents, wherein the each one of the plurality of vectors respectively corresponds to the each one of the plurality of digital documents (Anerousis Fig. 5, para [0054-56], where a graph is determined mapping documents or vectors to keywords using matching, para [0066], where words are encoded into a vector, and para [0031], [0064], where an encoder is used to determine embeddings or vectors for the services or documents).
Regarding claim 9, Anerousis in view of Starr teaches:
The server system of claim 1, wherein the processor is further configured to ingest an additional plurality of digital documents; process the additional plurality of digital documents along with the plurality of digital documents to determine if there are one or more new topics; and, when there are one or more new topics, generate a new output comprising the one or more new topics (Anerousis para [0046], where new inputs are pre-processed to generate additional services, topics, or communities by recursively modifying the ontologies).
Regarding claim 11, Anerousis teaches:
A method for automatically generating a plurality of topics from a plurality of digital documents, the method executed in a computing environment comprising one or more processors and memory (Fig. 10 elements 1014, 1016, para [0069], where a processor and memory are used), the method comprising:
ingesting the plurality of digital documents (para [0027], where service descriptions are received, and para [0049], where the nodes represent services or documents);
processing the plurality of digital documents using an encoder to respectively obtain a plurality of vectors (para [0031], [0064], where an encoder is used to determine embeddings or vectors for the services or documents);
clustering the plurality of vectors to identify a plurality of clusters from amongst the plurality of vectors (para [0031], where the vectors are grouped using k-means clustering);
identifying the plurality of topics that are respectively associated with the plurality of clusters (para [0031], where topic modeling is applied to the clusters);
computing a word-document matrix of probabilities of words detected in each one of the plurality of digital documents (para [0032], where a term-document matrix is decomposed);
computing a word-topic matrix of probabilities of words detected in each topic (para [0033], where a term-topic matrix is found);
computing a topic-document matrix of probabilities of each one of the plurality of topics in each one of the plurality of digital documents based on the word-document matrix of probabilities and the word-topic matrix of probabilities (para [0034], where a topic-document matrix is determined from the term-document matrix and the term-topic matrix); and
Anerousis does not teach:
computing and outputting a topic distribution for each of the plurality of digital documents, wherein a given topic distribution comprises one or more probabilities of one or more topics, from amongst the plurality of topics, that are associated with a given digital document.
Starr teaches:
computing and outputting a topic distribution for each of the plurality of digital documents, wherein a given topic distribution comprises one or more probabilities of one or more topics, from amongst the plurality of topics, that are associated with a given digital document (col. 20 lines 11-36, where the topic-document matrix includes values for a Dirichlet distribution).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Anerousis by using the distribution of Starr (Starr col. 20 lines 11-36) for the documents of Anerousis (Anerousis para [0027]), in order to determine the topic probabilities for individual text documents (Starr col. 20 lines 11-36).
Regarding claim 12, Anerousis in view of Starr teaches:
The method of claim 11, further comprising computing the topic-document matrix of probabilities of each one of the plurality of topics in each one of the plurality of the digital documents by at least factorizing the word-document matrix of probabilities by the word- topic matrix of probabilities (Anerousis para [0032-34], where matrix factorization is used to determine the topic-document matrix).
Regarding claim 13, Anerousis in view of Starr teaches:
The method of claim 11, wherein a given topic, from amongst the plurality of topics, comprises one or more statistically significant words found in a given cluster of documents from amongst the plurality of clusters (Anerousis para [0031-32], where a cluster or community has a topic determined by examining the terms co-occurring).
Regarding claim 14, Anerousis in view of Starr teaches:
The method of claim 11, wherein each of the plurality of documents comprises natural language, and the plurality of digital documents are transformed into the plurality of vectors using a natural language neural network model (Anerousis para [0064-65], where a neural network LSTM is used for the embedding of the documents, and para [0027], where the device descriptions are in natural language).
Regarding claim 16, Anerousis in view of Starr teaches:
The method of claim 11, further comprising computing the topic distribution for each one of the plurality of digital documents as a Dirichlet distribution (Starr col. 20 lines 11-36, where the topic-document matrix includes values for a Dirichlet distribution).
Regarding claim 17, Anerousis in view of Starr teaches:
The method of claim 11, further comprising computing a graph of the plurality of vectors, each one of the plurality of vectors associated with a set of statistically significant words observed respectively in each one of the plurality of documents, wherein the each one of the plurality of vectors respectively corresponds to the each one of the plurality of digital documents (Anerousis Fig. 5, para [0054-56], where a graph is determined mapping documents or vectors to keywords using matching, para [0066], where words are encoded into a vector, and para [0031], [0064], where an encoder is used to determine embeddings or vectors for the services or documents).
Regarding claim 20, Anerousis teaches:
A non-transitory computer readable medium storing computer executable instructions which, when executed by at least one computer processor, cause the at least one computer processor to carry out a method for generating a plurality of topics from a plurality of digital documents (Fig. 10 elements 1014, 1016, 1024, para [0069], [0071], where a processor, memory, and storage media are used), the method comprising:
ingesting the plurality of digital documents (para [0027], where service descriptions are received, and para [0049], where the nodes represent services or documents);
processing the plurality of digital documents using an encoder to respectively obtain a plurality of vectors (para [0031], [0064], where an encoder is used to determine embeddings or vectors for the services or documents);
clustering the plurality of vectors to identify a plurality of clusters from amongst the plurality of vectors (para [0031], where the vectors are grouped using k-means clustering);
identifying the plurality of topics that are respectively associated with the plurality of clusters (para [0031], where topic modeling is applied to the clusters);
computing a word-document matrix of probabilities of words detected in each one of the plurality of digital documents (para [0032], where a term-document matrix is decomposed);
computing a word-topic matrix of probabilities of words detected in each topic (para [0033], where a term-topic matrix is found);
computing a topic-document matrix of probabilities of each one of the plurality of topics in each one of the plurality of digital documents based on the word-document matrix of probabilities and the word-topic matrix of probabilities (para [0034], where a topic-document matrix is determined from the term-document matrix and the term-topic matrix); and
Anerousis does not teach:
computing and outputting a topic distribution for each of the plurality of digital documents, wherein a given topic distribution comprises one or more probabilities of one or more topics, from amongst the plurality of topics, that are associated with a given digital document.
Starr teaches:
computing and outputting a topic distribution for each of the plurality of digital documents, wherein a given topic distribution comprises one or more probabilities of one or more topics, from amongst the plurality of topics, that are associated with a given digital document (col. 20 lines 11-36, where the topic-document matrix includes values for a Dirichlet distribution).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Anerousis by using the distribution of Starr (Starr col. 20 lines 11-36) for the documents of Anerousis (Anerousis para [0027]), in order to determine the topic probabilities for individual text documents (Starr col. 20 lines 11-36).
Claim(s) 5 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Anerousis, in view of Starr, and further in view of Khachatryan et al. (US 12,650,997 B1), hereinafter referred to as Khachatryan.
Regarding claim 5, Anerousis in view of Starr teaches:
The server system of claim 1, wherein the processor is configured to transform the plurality of documents into the plurality of vectors by at least:
transform the plurality of high dimensional vectors to the plurality of vectors, wherein the plurality of vectors has a second number of dimensions less than the first number of dimensions (Anerousis para [0040], [0056], where SVD reduces the dimensionality of a matrix).
Anerousis in view of Starr does not teach:
transforming the plurality of documents into a plurality of high dimensional vectors with a first number of dimensions, using a bidirectional encoder representations from transformers (BERT) model in the encoder;
Khachatryan teaches:
transforming the plurality of documents into a plurality of high dimensional vectors with a first number of dimensions, using a bidirectional encoder representations from transformers (BERT) model in the encoder (col. 5 lines 19-29, where BERT is used to convert documents into embedding vectors of dimension n);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Anerousis in view of Starr by using the BERT model of Khachatryan (Khachatryan col. 5 lines 19-29) as the encoder of Anerousis in view of Starr (Anerousis para [0064]), in order to ensure consistent and contextually rich representations (Khachatryan col. 17 lines 13-37).
Regarding claim 15, Anerousis in view of Starr teaches:
The method of claim 11, further comprising processing the plurality of documents to generate the plurality of vectors by at least:
transforming the plurality of high dimensional vectors to the plurality of vectors, wherein the plurality of vectors has a second number of dimensions less than the first number of dimensions (Anerousis para [0040], [0056], where SVD reduces the dimensionality of a matrix).
Anerousis in view of Starr does not teach:
transforming the plurality of documents into a plurality of high dimensional vectors with a first number of dimensions, using a bidirectional encoder representations from transformers (BERT) model in the encoder;
Khachatryan teaches:
transforming the plurality of documents into a plurality of high dimensional vectors with a first number of dimensions, using a bidirectional encoder representations from transformers (BERT) model in the encoder (col. 5 lines 19-29, where BERT is used to convert documents into embedding vectors of dimension n);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Anerousis in view of Starr by using the BERT model of Khachatryan (Khachatryan col. 5 lines 19-29) as the encoder of Anerousis in view of Starr (Anerousis para [0064]), in order to ensure consistent and contextually rich representations (Khachatryan col. 17 lines 13-37).
Claim(s) 8, 10, and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Anerousis, in view of Starr, and further in view of Zhang (US 9,015,153 B1).
Regarding claim 8, Anerousis in view of Starr teaches:
The server system of claim 1,
Anerousis in view of Starr does not teach:
wherein the processor is further configured to render a graphical user interface that displays at least the given topic in association with the given digital document, or an identifier of the given digital document (col. 19 line 62 - col. 20 line 32, where the user interface includes a column for new topics).
Zhang teaches:
wherein the processor is further configured to render a graphical user interface that displays at least the given topic in association with the given digital document, or an identifier of the given digital document.
It would have been obvious to one of ordinary skill in the art to modify the system of Anerousis in view of Starr by including the GUI of Zhang (Zhang Fig. 15a,b) to display the topics of Anerousis in view of Starr (Anerousis para [0031]), so that a user can immediately know the topic terms without having to open each email or document (Zhang col. 19 line 62 - col. 20 line 32).
Regarding claim 10, Anerousis in view of Starr teaches:
The server system of claim 9,
Anerousis in view of Starr does not teach:
wherein the processor is further configured to render a graphical user interface that displays an indicator identifying the one or more new topics from amongst the plurality of topics.
Zhang teaches:
wherein the processor is further configured to render a graphical user interface that displays an indicator identifying the one or more new topics from amongst the plurality of topics (col. 19 line 62 - col. 20 line 32, where the user interface includes a column for new topics).
It would have been obvious to one of ordinary skill in the art to modify the system of Anerousis in view of Starr by including the GUI of Zhang (Zhang Fig. 15a,b) to display the topics of Anerousis in view of Starr (Anerousis para [0031]), so that a user can immediately know the topic terms without having to open each email or document (Zhang col. 19 line 62 - col. 20 line 32).
Regarding claim 18, Anerousis in view of Starr teaches:
The method of claim 11,
Anerousis in view of Starr does not teach:
further comprising rendering a graphical user interface that displays at least the given topic in association with the given digital document, or an identifier of the given digital document.
Zhang teaches:
further comprising rendering a graphical user interface that displays at least the given topic in association with the given digital document, or an identifier of the given digital document (col. 19 line 62 - col. 20 line 32, where the user interface includes a column for new topics).
It would have been obvious to one of ordinary skill in the art to modify the system of Anerousis in view of Starr by including the GUI of Zhang (Zhang Fig. 15a,b) to display the topics of Anerousis in view of Starr (Anerousis para [0031]), so that a user can immediately know the topic terms without having to open each email or document (Zhang col. 19 line 62 - col. 20 line 32).
Regarding claim 19, Anerousis in view of Starr teaches:
The method of claim 11, further comprising ingesting an additional plurality of digital documents; process the additional plurality of digital documents along with the plurality of digital documents to determine if there are one or more new topics; when there are one or more new topics, generating a new output comprising the one or more new topics (Anerousis para [0046], where new inputs are pre-processed to generate additional services, topics, or communities by recursively modifying the ontologies); and
Anerousis in view of Starr does not teach:
rendering a graphical user interface that displays an indicator identifying the one or more new topics from amongst the plurality of topics.
Zhang teaches:
rendering a graphical user interface that displays an indicator identifying the one or more new topics from amongst the plurality of topics (col. 19 line 62 - col. 20 line 32, where the user interface includes a column for new topics).
It would have been obvious to one of ordinary skill in the art to modify the system of Anerousis in view of Starr by including the GUI of Zhang (Zhang Fig. 15a,b) to display the topics of Anerousis in view of Starr (Anerousis para [0031]), so that a user can immediately know the topic terms without having to open each email or document (Zhang col. 19 line 62 - col. 20 line 32).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 2023/0136726 A1 para [0037], [0040] teaches the three claimed types of matrices as well as use of SVD; US 2024/0004912 A1 Fig. 3, para [0034-36] also teaches the three claimed types of matrices.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRYAN S BLANKENAGEL whose telephone number is (571)270-0685. The examiner can normally be reached 8:00am-5:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Richemond Dorvil can be reached at 571-272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRYAN S BLANKENAGEL/Primary Examiner, Art Unit 2658
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