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 .
DETAILED ACTION
Information Disclosure Statement
Applicants’ Information Disclosure Statement, filed 01/23/2025, has been received, entered into the record, and considered. See attached form PTO-1449.
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 are rejected under 35 U.S.C. 101 because the claimed invention are directed to non-statutory subject matter.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention are directed to an abstract idea without significantly more.
Claims 1, 8, 15 recite a method/system/medium for processing and distributing text data from a dataset, the method comprising: receiving raw data and converting the raw data into a set of text chunks; determining a set of classifications for the raw data, the set of classifications corresponding to a set of data stores; augmenting a text chunk in the set of text chunks with metadata, the augmenting comprising: extracting retrieval metadata from the text chunk; determining and assigning, with a machine learning model configured to classify text information, a classification in the set of classifications for the text chunk; and sequencing the text chunk; generating a windowed chunk by appending context to the augmented chunk; embedding the windowed chunk and the extracted retrieval metadata into a chunk embedding; and distributing the chunk embedding by: determining a data store in the set of data stores corresponding to the assigned classification; and assigning the chunk embedding to the determined data store”.
These limitations are processes that, under their broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting "a machine learning model, a memory, a processor", nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper. For example, but for the “a machine learning model, a memory, a processor” language, “receiving raw data and converting … text chunks; determining a set of classifications…; augmenting a text chunk …: extracting retrieval metadata …; determining and assigning…; and sequencing the text chunk; generating a windowed chunk …; embedding the windowed chunk and the extracted retrieval metadata ..; and distributing the chunk embedding by: determining a data store …; and assigning the chunk …” in the context of this claim encompasses a user gathering data, collecting and organizing data by identifying data to be stored mentally, with the aid of pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
This judicial exception is not integrated into a practical application. In particular, the claims recite additional element – using “a machine learning model, a memory, a processor” to “receiving raw data and converting … text chunks; determining a set of classifications…; augmenting a text chunk …: extracting retrieval metadata …; determining and assigning…; and sequencing the text chunk; generating a windowed chunk …; embedding the windowed chunk and the extracted retrieval metadata ..; and distributing the chunk embedding by: determining a data store …; and assigning the chunk …”, these limitations amount to data gathering which is considered to be insignificant extra solution activity (MPEP 2106.05(g).
“assigning the chunk embedding to the determined data store”; this limitation is a mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (MPEP 2106.05(g).
The machine learning model, the memory, the processor are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of “receiving raw data and converting … text chunks; determining a set of classifications…; augmenting a text chunk …: extracting retrieval metadata …; determining and assigning…; and sequencing the text chunk; generating a windowed chunk …; embedding the windowed chunk and the extracted retrieval metadata ..; and distributing the chunk embedding by: determining a data store …; and assigning the chunk …”. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (see MPEP 2106.05(f)). The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and in combination, they do not add significantly more to the exception. Considered separately and as an ordered combination, the claim elements do not provide an improvement to another technology or technical field; do not provide an improvement to the functioning of the computer itself.
The limitations “receiving raw data and converting … text chunks; determining a set of classifications…; augmenting a text chunk …: extracting retrieval metadata …; determining and assigning…; and sequencing the text chunk; generating a windowed chunk …; embedding the windowed chunk and the extracted retrieval metadata ..; and distributing the chunk embedding by: determining a data store …; and assigning the chunk …” amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Dependent claims 2-7, 9-14, 16-20 merely add further details of the abstract steps recited in claims 1, 8, 15 without including an improvement to another technology or technical field, an improvement to the functioning of the abstract idea to a particular technological environment. Therefore, dependent claims 2-7, 9-14, 16-20 are also directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over He et al. (US Pub No. 2024/0362286), in view of ZHANG et al. (US Pub. 2022/0147818).
As to claims 1, 8, 15, He teaches a method for processing and distributing text data from a dataset (i.e. FIG. 12 illustrates an embodiment of a logic flow 1200. The logic flow 1200 may be representative of some or all of the operations executed by one or more embodiments described herein. For example, the logic flow 1200 may include some or all of the operations performed by devices or entities within the system 100 or the system 200. More particularly, the logic flow 1200 illustrates an example where the server device 102 prepares an electronic document 706 to support search operations in an offline phase, [0193]; In block 1212, logic flow 1200 stores the document index with the document vectors in a database. For example, the search manager 124 may store the document index 730 with the document vectors 726 in a database 708, [0199]), the method comprising:
receiving raw data and converting the raw data into a set of text chunks (i.e. In block 1202, logic flow 1200 receives an electronic document having document content ... The document corpus 508 may be associated with a defined entity, and as such, contain confidential and proprietary information, [0194]; encode a set of electronic documents 706 to create a set of contextualized embeddings (e.g., sentence embeddings) for information or document content contained within each electronic document 706 ... Each of the information blocks 710 may comprise a defined amount of textual information of any feature size suitable for a given token, such as an n-gram, a word, a sentence, a phrase, a paragraph, a section, and so forth ... to encode the information blocks 710 into corresponding contextualized embeddings depicted as a set of M document vectors 726, where M represents any positive integer, [0129]; In block 1206, logic flow 1200 splits the document content into multiple information blocks ... split the document content into multiple information blocks 710. Each information blocks 710 may comprise a partial word, a word, a sentence, a phrase, a paragraph, a section, or other discrete unit of document content, [0196]);
determining a set of classifications for the raw data, the set of classification corresponding to a set of data stores (i.e. Neural networks can be classified into different types, which are used for different purposes. The artificial neural network 400 may be implemented as a feedforward neural network, or multi-layer perceptrons (MLPs), comprised of an input layer 426, hidden layers 428, and an output layer 430, [0105]; process the document content to prepare for ingest by a machine learning model, [0195]; One or more of the information blocks 710 and/or the document vectors 726 may optionally include block labels assigned using a machine learning model, such as a classifier. A block label may represent a type or content type for information or data contained within each of the information blocks 710, such as a semantic meaning, a standard clause, a provision, customer data, buyer information, seller information, product information, service information, licensing information, financial information, cost information, revenue information, profit information, sales information, purchase information, accounting information, milestone information, [0152]; such as text documents, that are typically used for natural language processing (NLP) tasks such as text classification, sentiment analysis, topic modeling, and information retrieval ... it may be annotated with metadata or labels to facilitate analysis. Document corpora are commonly used in research and industry to train machine
learning models and to develop NLP applications, [0108]);
augmenting a text chunk in the set of text chunks with metadata, the augmenting comprising (i.e. In block 1208, logic flow 1200 generates a contextualized embedding for each information block to form a corresponding document vector, each contextualized embedding to comprise a vector representation of a sequence of words in the electronic document that includes contextual information for the sequence of words, [0197]; A block label may represent a type or content type for information or data contained within each of the information blocks 710, [0152]; Semi-structured data is characterized by the presence of tags or metadata that provide some structure and context for the data, [0088]):
extracting retrieval metadata from the text chunk (i.e. the contextualized embedding includes a vector representation of a sequence of words that includes contextual information for the sequence of words. In language processing, contextual information refers to the words or phrases that surround a particular word or sentence, and which can provide important clues to its meaning. In the same way, when analyzing a particular piece of data or information, understanding its contextual information can help provide a more accurate interpretation and prevent misunderstandings, [0209]; The document ingest and processing 1420 may include text extraction 1422, sentence splitting 1424, and metadata processing 1426, [0224]; the document container 128 may also include metadata for the electronic document 142. In one embodiment, the metadata may comprise signature tag marker element (STME) information 132 for the electronic document 142. The STME information 130 may comprise one or more STME 132, which are graphical user interface (GUI) elements superimposed on the electronic document 142, [0058]);
determining and assigning, with a machine learning model configured to classify text information, a classification in the set of classifications for the text chunk (i.e. The artificial neural network 400 ... text recognition or classification. The artificial neural network 400 may leverage supervised learning, or labeled datasets, to train the algorithm, [0101]; each trained BERT model will be different based on a different document corpus 508 associated with a different defined entity, [0210]; A supervised algorithm is a type of machine learning algorithm that uses labeled data to train a machine learning model. In supervised learning, the machine learning algorithm is given a set of input data and corresponding output data, which are used to train the model to make predictions or classifications. The input data is also known as the features, and the output data is known as the target or label. The goal of a supervised algorithm is to learn the relationship between the input features and the target labels, so that it can make accurate predictions or classifications for new, unseen data. Examples of supervised learning algorithms include: (1) linear regression which is a regression algorithm used to predict continuous numeric values, such as stock prices or temperature; (2) logistic regression which is a classification algorithm used to predict binary outcomes, such as whether a customer will purchase or not purchase a product; (3) decision tree which is a classification algorithm used to predict categorical outcomes by creating a decision tree based on the input features; or (4) random forest which is an ensemble algorithm that combines multiple decision trees to make more accurate predictions, [0082]); and
sequencing the text chunk (i.e. The logic flow 1300 may also include generating the contextualized embeddings using a bidirectional encoder representation from transformers (BERT) language model, indexing the contextualized embeddings for the electronic document to form the document index, and storing the document index in a database. There are different ways of indexing information for an electronic document 706. There are different types of indices that can be used to organize and retrieve information from a database. An index is a data structure that allows fast and efficient retrieval of data based on specific criteria, such as a particular field or attribute ... The choice of index structure will depend on the specific application requirements, such as the type and size of data being indexed, the desired query performance, and the available system resources, [0212]; a vector representation of a sequence of words in the electronic document that includes contextual information for the sequence of words, [0041]);
generating a windowed chunk by appending context to the augmented chunk (i.e. The computing apparatus may also include where the contextualized embeddings are a word level vector, a sentence level vector, or a paragraph level vector, [0319]; the contextualized embedding includes a vector representation of a sequence of words that includes contextual information for the sequence of words. In language processing, contextual information refers to the words or phrases that surround a particular word or sentence, and which can provide important clues to its meaning. In the same way, when analyzing a particular piece of data or information, understanding its contextual information can help provide a more accurate interpretation and prevent misunderstandings, [0209]);
embedding the windowed chunk and the extracted retrieval metadata into a chunk embedding (i.e. in the offline phase, the search manager 124 may encode a set of electronic documents 706 to create a set of contextualized embeddings (e.g. sentence embeddings) for information or document content contained within each electronic document 706 ... Each of the information blocks 710 may comprise a defined amount of textual information of any feature size suitable for a given token, such as an n-gram, a word, a sentence, a phrase, a paragraph, a section, and so forth, [0150]; There are different ways of indexing information for an electronic document 706. There are different types of indices that can be used to organize and retrieve information from a database. An index is a data structure that allows fast and efficient retrieval of data based on specific criteria, such as a particular field or attribute ... These are just a few examples of the different types of indices that can be used to organize and retrieve information from a database, such as the database 708. The choice of index structure will depend on the specific application requirements, such as the type and size of data being indexed, the desired query performance, and the available system resources, [0212]); and
distributing the chunk embedding by (i.e. FIG. 8 illustrates an operating environment 800. The operating environment 800 illustrates an example of encoding an electronic document 706 into a set of document vectors 726, [0149]):
determining a data store in the set of data stores corresponding to the assigned classification (i.e. to encode the information blocks 710 into corresponding contextualized embeddings depicted as a set of M document vectors 726, where M represents any positive integer. As depicted in FIG. 8 , the search manager 124 may use the search model 704 to encode the information block 712 into a document vector 802, the information block 714 into a document vector 804, the information block 716 into the document vector 806, and the information block N into the document vector M, [0151]); and
assigning the chunk embedding to the determined data store (i.e. In block 1212, logic flow 1200 stores the document index with the document vectors in a database. For example, the search manager 124 may store the document index 730 with the document vectors 726 in a database 708, [0199]).
Although He implicitly teaches the term "augmented" (i.e. The computing apparatus may also include where the contextualized embeddings are a word level vector, a sentence level vector, or a paragraph level vector, [0319]; the contextualized embedding includes a vector representation of a sequence of words that includes contextual information for the sequence of words. In language processing, contextual information refers to the words or phrases that surround a particular word or sentence, and which can provide important clues to its meaning. In the same way, when analyzing a particular piece of data or information, understanding its contextual information can help provide a more accurate interpretation and prevent misunderstandings, [0209]), He does not clearly state this term.
ZHANG specifically teaches this term (i.e. For all experiments, we train the CHN to output accurate feature parameters based on a range of context set sizes kϵ[0, . . . , 32] by randomly sampling k on each occurrence of a meta-training set feature. We then evaluate the performance of the CHN and baselines on the meta-test set features for a fixed range of context set sizes, ensuring that the same context sets are revealed to the CHN and each baseline, [0106]; The CHN may be used to augment a range of different types of deep learning models. For instance, a CHN may be used to augment a partial variational autoencoder (P-VAE). The result is a flexible deep learning model able to rapidly adapt to new features, even when the data is sparsely-observed, e.g. in recommender systems. As shown below, such a model outperforms a range of baselines in both predictive accuracy and speed when used for prediction in recommender system, e-learning and healthcare settings, [0022]).
It would have been obvious to one of ordinary skill of the art having the teaching of He, ZHANG before the effective filing date of the claimed invention to modify the system of He to include the limitations as taught by ZHANG. One of ordinary skill in the art would be motivated to make this combination in order to augment a range of different types of deep learning models in view of ZHANG ([0022]), as doing so would give the added benefit of providing a model which outperforms a range of baselines in both predictive accuracy and speed when used for prediction in recommender system, e-learning and healthcare settings, as taught by ZHANG ([002]).
As to claims 2, 9, 16, ZHANG teaches training the machine learning model with ground truth data (i.e. estimate the log-likelihood of the CHN parameters ψ given the ground truths for the hidden values of the feature n in the data points in its target set, [0096]).
As to claims 3, 10, 17, He teaches ordering the augmented text chunk (i.e. The search manager 124 may store the document vectors 726 in a database 708, and index the document vectors 726 into a searchable document index 730. The document index 730 allows for rapid retrieval of relevant document vectors 726 by the search manager 124 during the online search phase. The document index 730 may comprise any data structure that stores these embeddings in a way that allows for efficient retrieval. For example, the document index 730 may be implemented as a hash table or a tree structure to index the embeddings by the words or phrases they represent, [0130]).
As to claims 4, 11, 18, ZHANG teaches a general data store, and based on a determination that the text chunk is not assigned to a classification in the set of classifications, assigning the text chunk to the general data store (i.e. In general the primary model 901 may be used to in any setting where predictions based on observed data are beneficial. For instance, the primary model 901 may be used in a medical setting in order to predict or diagnose conditions of a living being (e.g. a human being or other animal). The features input to the primary model 901 may relate to medical data supplied to the primary model 901 by or on behalf of a patient, e.g. age, height, weight, blood pressure, heart rate, etc. The medical data may be supplied automatically via sensors, e.g. a heart rate monitor. The primary model 901 may use the observed features to predict a condition of the patient. The new feature may relate to newly available medical data, e.g. a new medical test may become available. The metadata may be descriptive of the test, [0071]).
As to claims 5, 12. 19, He teaches the determined data store comprises
a repository (i.e. In block 1212, logic flow 1200 stores the document index with the document vectors in a database. For example, the search manager 124 may store the document index 730 with the document vectors 726 in a database 708, [0199]).
As to claims 6, 13, 20, ZHANG teaches the determined data store comprises a second machine learning model (i.e. The second neural network 702 is configured to receive, as an input, the context vector, [0063]; As shown in FIG. 8, the auxiliary model 700 may comprise a third neural network 801. The third neural network 801 may be configured to receive, as an input, a second input vector. The second input vector 801 comprises a set of metadata values associated with the new feature. For instance, the metadata values may comprise a category of the new feature (e.g. type of medical test, movie category, etc.), image or text data associated with the new feature, etc. The second neural network 801 is configured to transform (i.e. encode) from the second input data to a metadata vector that is a representation of the metadata values. The third neural network 801 is configured to supply the metadata vector to the second neural network, [0068]).
As to claims 7, 14, ZHANG teaches the raw data comprises medical record data (i.e. performing a task on a human may include performing a medical surgery on the human or supplying a medicament to the human. Note that outputting an action may comprise outputting a request or suggestion to perform the action, or in some examples, actually performing the action. For instance, the sequential model may be used to control a connected device that is configured to observe a measurement or perform
a task, e.g. to supply a drug via an intravenous injection, [0159]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
VLADIMIROVA et al. (US Pat. 11,462,325) discloses techniques for performing a clinical prediction based on multimodal clinical data using a machine learning model.
Ezen Can et al. (US Pat. 11,392,764) discloses a method for classifying text to determine a goal type used to select machine learning algorithm outcomes. Natural language processing of text is performed to determine features in the text and their relationships. A classifier classifies the text based on the relationships and features to determine a goal type. The determined features and relationships from the text are inputted into a plurality of different machine learning algorithms to generate outcomes. For each of the machine learning algorithms, a determination is made of performance measurements resulting from the machine learning algorithms generating the outcomes. A determination is made of at least one machine learning algorithm having performance measurements that are highly correlated to the determined goal type. An outcome is determined from at least one of the outcomes.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MIRANDA LE whose telephone number is (571)272-4112. The examiner can normally be reached M-F 7AM-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kavita Stanley can be reached on 571-272-8352. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MIRANDA LE/Primary Examiner, Art Unit 2153