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
1. The pending claims 1-20 are presented for examination.
Claim Rejections - 35 USC § 101
2. 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.
3. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis below of the claims’ subject matter eligibility follows the guidance set forth in MPEP 2106 which has incorporated the 2019 PEG.
Regarding to claim 12,
Step 1 Analysis: Claim 12 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 1 recites: A method for integrating transformer architecture, the method comprising:
“tokenizing, by at least one processor, dynamic schema database data, the tokenization including positional information associated with the source data”. This element reads on a person tokenizes dynamic schema database data, the tokenization including positional information associated with the source data which could be considered a mental process of an observation or evaluation.
“training a transformer model on the tokenized dynamic schema database data and the positional information as an input”. This element reads on a person trains a transformer model on the tokenized dynamic schema database data and the positional information as an input which could be considered a mental process of an observation or evaluation.
“predicting, using the transformer model, distributions corresponding to key value pairs and a valid format for dynamic schema database data”. This element reads on a person predicts, using the transformer model, distributions corresponding to key value pairs and a valid format for dynamic schema database data which could be considered a mental process of an observation or evaluation.
“producing key value pairs associated with the prediction of the valid format”. This element reads on a person produces key value pairs associated with the prediction of the valid format which could be considered a mental process of an observation or evaluation.
Overall, the limitations directed to produce key value pairs and a valid format for dynamic schema database data and the various mental process limitations in the context of this claim encompasses limitations that are not only considered to be directed to limitations that could be practically performed in the human mind (including observations and preform an evaluation, judgment, and opinion) aided by the use of pen and paper. If the claim limitations, under their broadest reasonable interpretations, cover performance of the limitation in the mind but for the recitation of generic computer components, then they fall within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: In Step 2A Prong 2, we are directed to Identify whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluate those additional elements to determine whether they integrate the exception into a practical application of the exception.
In particular, the claim only recites the additional elements of “a computer-implemented”
Regarding the computer- implemented,
The processor of a computer system for generating and storing in all steps is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing data (generating and storing). This generic processor limitation is no more than mere instructions to apply the exception using a generic computer components. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The additional element “a computer- implemented” is simply applying the abstract idea, and there is nothing done with results. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea, and does not provide any improvement in computer technology (see MPEP2106.05(a)).
Regarding the at least one processor,
The processor of a computer system for generating and storing in all steps is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing data (generating and storing). This generic processor limitation is no more than mere instructions to apply the exception using a generic computer components. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The additional element “the at least one processor” is simply applying the abstract idea, and there is nothing done with results. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea, and does not provide any improvement in computer technology (see MPEP2106.05(a)).
Therefore, the additional elements do not integrate the judicial exception into a practical application.
Step 2B Analysis: In Step 2B, we are directed to Identify whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluate those additional elements to determine whether the additional elements, taken individually and in combination, result in the claim as a whole amounting to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, The additional elements “a computer-implemented” and “at least one processor” are simply applying the abstract idea, and there is nothing done with results.
Accordingly, these additional elements, taken individually and in combination, do not result in the claim as a whole amounting to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 13,
Step 1 Analysis: Claim 13 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 13 is dependent on claim 12, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 13 recites “tokenizing includes maintaining data architecture information associated with the source data. " That is, the claim recites tokenizing includes maintaining data architecture information associated with the source data. The above-noted limitation of claim 13, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 14,
Step 1 Analysis: Claim 14 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 14 is dependent on claim 12, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 14 recites “tokenizing includes preserving key and value information stored in a source document without reduction to sub-words" That is, the claim recites tokenizing includes preserving key and value information stored in a source document without reduction to sub-words. The above-noted limitation of claim 14, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 15,
Step 1 Analysis: Claim 15 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 15 is dependent on claim 12, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 15 recites “instantiating a pre-trained transformer model, the pre-trained transformer model trained on dynamic schema database data and data architecture information, to output a next data element based on an input data element " That is, the claim recites instantiating a pre-trained transformer model, the pre-trained transformer model trained on dynamic schema database data and data architecture information, to output a next data element based on an input data element. The above-noted limitation of claim 15, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 16,
Step 1 Analysis: Claim 16 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 16 is dependent on claims 12&15, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 16 recites “generating a predictive distribution associated with frequency of occurrence of respective data elements in response to an input of data elements to the pre-trained transformer model; and defining an encoding of associated short code words to data elements based on predicted frequency of occurrence " That is, the claim recites generating a predictive distribution associated with frequency of occurrence of respective data elements in response to an input of data elements to the pre-trained transformer model; and defining an encoding of associated short code words to data elements based on predicted frequency of occurrence. The above-noted limitation of claim 16, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 17,
Step 1 Analysis: Claim 17 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 17 is dependent on claims 12&15, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 17 recites “generating a predictive distribution associated with frequency of occurrence of respective output data elements associated with execution of the query in response to an input of data elements taken from a query under execution to the pre-trained transformer model; and defining an encoding of associated short code words to data elements based on predicted frequency of occurrence" That is, the claim recites generating a predictive distribution associated with frequency of occurrence of respective output data elements associated with execution of the query in response to an input of data elements taken from a query under execution to the pre-trained transformer model; and defining an encoding of associated short code words to data elements based on predicted frequency of occurrence. The above-noted limitation of claim 17, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 18,
Step 1 Analysis: Claim 18 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 18 is dependent on claims 12&15, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 18 recites “generating an output of new dynamic schema database data having a valid format and architecture consistent with the source database in response to an input of data elements taken from a source database including dynamic schema database data" That is, the claim recites generating an output of new dynamic schema database data having a valid format and architecture consistent with the source database in response to an input of data elements taken from a source database including dynamic schema database data. The above-noted limitation of claim 18, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Accordingly, this additional element, taken individually and in combination, does not result in the claim as a whole amounting to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 19,
Step 1 Analysis: Claim 19 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 19 is dependent on claims 12&15, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 19 recites “generating a cardinality estimate for queries without having to execute the queries on the source dataset in response to an input of data elements including dynamic schema database data" That is, the claim recites generating a cardinality estimate for queries without having to execute the queries on the source dataset in response to an input of data elements including dynamic schema database data. The above-noted limitation of claim 19, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 20,
Step 1 Analysis: Claim 20 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 20 is dependent on claims 12, 15 & 19, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 20 recites “generating predictive documents unconditionally; and evaluating generated probabilities of a next token following a runtime key input" That is, the claim recites generating predictive documents unconditionally; and evaluating generated probabilities of a next token following a runtime key input. The above-noted limitation of claim 20, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claims 1-11 are rejected under 35 U.S.C. 101 with the same rational of claims 12-20.
Claim Rejections - 35 USC § 103
4. 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.
5. 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.
6. 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.
7. 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.
8. Claims 1, 3-4, 7, 12, 14-15 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Spinaci et al (US 20260010680 A1, hereinafter “Spinaci”) in view of Cunningham (U.S. 20260044490 A1 hereinafter, “Cunningham”).
9. With respect to claim 1,
Spinaci discloses a system for integrating transformer architecture, the system comprising:
at least one processor operatively connected to a memory, the at least one processor configured to:
tokenize dynamic schema database data (Spinaci [0016], [0030], [0036] - [0047] e.g. [0036] The training system 102 may comprise a trainer 106 and the tokenizer 108. The trainer 106 and tokenizer 108 may be or include software that is executed at the training system 102 to implement tokenizing of training data 110 and training of the computerized model 120. …. [0037] In the example of FIG. 1, the tokenizer 108 uses training data 110 to generate tokenized records. The training data 110 may be any suitable quantity of structured data that is accessible to the training system 102. In some examples, the training data 110 includes tables that are part of the schema 124 implemented by the database management application 118. For example, the training system 102 may be in communication with the ERP computing system 104 to obtain structured data managed by the database management application 118 for use as training data 110. [0038] …. Each row of the example table is a record, where the record comprises a cell corresponding to each of the columns. …. [0039] A window 136 shows an example tokenizing operation performed by the tokenizer 108 on the record 152. During tokenization, the value at each cell is converted to a corresponding cell vector. …. A tokenized record 148 has a similar structure to the record 152 (e.g., the same number of cells), but the cell values of the record 152 are replaced with corresponding cell vectors. [0040] The tokenizer 108 is configured to tokenize different types of data differently. For example, the tokenizer 108 may generate a content vector for text data, such as the cell value TEXTB1 from the record 152, by providing the text data as input to a pre-trained model. The pre-trained model may be any suitable model that is configured to embed text data to generate a corresponding cell vector representing the text data. The pre-trained model may generate a content vector as output. The pretrained model may be or include any suitable text in betting model such as, for example, various text embedding models available from Hugging Face, Inc.), the tokenization including positional information (Spinaci [0019] e.g. [0019] Transformer models, used in many Large Language Models (LLM), can be highly effective at generating predictions of missing data in a large data set. Applying a transformer model to the structured data of a database management application, however, may present challenges. For example, structured data in a database is not ordered in the same way as natural language. In natural language, words are represented in a particular order, and the order of the words is relevant to the meaning of the phrase. Many transformer models exploit the ordered nature of natural language when generating predictions of missing data. In a database record, on the other hand, the order of cells in a record is not often relevant to the meaning of the cell values. For example, reordering the columns of the example TABLE 1 would not change the meaning of the data in any of the corresponding cells. Because many transformer models rely on positional encoding to extract information from input data, the unordered nature of structured data may compromise the effectiveness of the model.) associated with the source data;
train a transformer model on the tokenized dynamic schema database data and the positional information as an input;
the transformer model configured to generate predictive distributions (Spinaci [0023], [0041] – [0042], [0051], [0057] – [0058] e.g. [0051] The breakout window 134 shows an example operation of the trainer 106. For example, starting from the tokenized record 148, the trainer 106 may generate masked tokenized record 140. In this example, the trainer 106 masks the cell vectorVB1 and VN1. The trainer 106 executes the computerized model 120 using the masked tokenized record 140 as input. The result is a prediction 142 of the masked cell vectors. In this example, the output of the computerized model 120 includes a prediction vector PVB1 corresponding to the cell vector VB1 and a prediction vector PVN1 corresponding to the cell vector VN1. Trainer 106 may determine an error 144 based on the differences between the prediction of the masked cell vectors (e.g., PVB1, PVN1) and the actual values of the masked cell vectors indicated by the tokenized record (e.g., VB1, VN1). In some examples, this may include determining a cross entropy loss based on a probability distribution among all possible values of the respective vectors. Based on the error 144, modifications may be made to the computerized model 120.) corresponding to value and a valid format (Spinaci [0023], [0041] – [0042], [0057] – [0058], [0068], [0074] e.g. [0023] …. The pre-trained computerized model may receive the text data as input and generate a corresponding content vector as output. In some examples, a content vector corresponding to timestamp data may be determined by formatting the timestamp data. For example, timestamp data may be formatted to include numerical values for the day, month, and year indicated by the timestamp data. …. [0041] The tokenizer 108 may generate a content vector from timestamp data, such as DATEA 1 from the record 152, at least in part, by expressing the timestamp data in a vector format including a numerical representation of the day, the month, and the year of the date …. [0057] …. At operation 502, the training system 102 (e.g., the tokenizer 108 thereof) may format or encode the timestamp data with a numerical value for the day, month, and year of the associated date. At operation 504, the training system 102 (e.g., the tokenizer 108 thereof) may determine date metadata associated with the date. …. The holiday Boolean may have a value corresponding to true if the day is a holiday in the corresponding jurisdiction and a value corresponding to false if it is not. [0058] At operation 506, the training system 102 (e.g., the tokenizer 108 thereof) may generate a content vector using the day, month, and year format of the date and the date metadata. At operation 508, the training system 102 (e.g., the tokenizer 108 thereof) may generate a cell vector using the content vector and an indication of the column corresponding to the cell. …. [0068] … This may include converting the predicted cell vector or vectors to an appropriate data format such as a numerical value, a date, and/or text. At operation 812, the database management application 118 may generate a response to the query using the detokenized prediction or predictions generated at operation 810. [0074] …. In addition, the libraries 916 may include API libraries 936 such as media libraries (e.g., libraries to support presentation and manipulation of various media format such as MPEG4, H.264, MP3, AAC, AMR, JPG, PNG), graphics libraries (e.g., an OpenGL framework that may be used to render 2D and 3D in a graphic content on a display), database libraries (e.g., SQLite that may provide various relational database functions), web libraries (e.g., WebKit that may provide web browsing functionality), and the like. The libraries 916 may also include a wide variety of other libraries 938 to provide many other APls to the applications 920 and other software components/modules.) for dynamic schema database data; and
output value associated with the prediction of the valid format.
Although Spinaci substantially teaches the claimed invention, Spinaci does not explicitly indicate key value pairs.
Cunningham teaches the limitations by stating
tokenize dynamic schema database data (Cunningham [0035] – [0038], [0040], [0044], [0048], [0054], [0065] – [0066] e.g. tokenized … schema), the tokenization including positional (Cunningham [0055], [0065], [0095] e.g. position) information associated with the source data;
train a transformer model on the tokenized dynamic schema database data and the positional information as an input (Cunningham [0038], [0040], [0044] e.g. transformer … schema);
the transformer model configured to generate predictive distributions (Cunningham [0033], [0038], [0040], [0044], [0068], [0071], [0074] e.g. [0068] Still referring to FIG. 2, Computing device may be configured to generate a classifier using a Na'ive Bayes classification algorithm. Na'ive Bayes classification algorithm generates classifiers by assigning class labels to problem instances, represented as vectors of element values. …. A class containing the highest posterior probability is the outcome of prediction. Na'ive Bayes classification algorithm may include a gaussian model that follows a normal distribution. Na'ive Bayes classification algorithm may include a multinomial model that is used for discrete counts. Na'ive Bayes classification algorithm may include a Bernoulli model that may be utilized when vectors are binary. [0071] … and/or machine-learning model may select training examples representing each possible value on such a range and/or a representative sample of values on such a range. Selection of a representative sample may include selection of training examples in proportions matching a statistically determined and/or predicted distribution of such values according to relative frequency, such that, for instance, values encountered more frequently in a population of data so analyzed are represented by more training examples than values that are encountered less frequently. … Computing device, processor, and/or module may automatically generate a missing training example; this may be done by receiving and/or retrieving a missing input and/or output value and correlating the missing input and/or output value with a corresponding output and/or input value collocated in a data record with the retrieved value, provided by a user and/or other device, or the like.) corresponding to key value pairs and a valid format for dynamic schema database data (Cunningham [0031], [0035], [0060] e.g. [0031] Still referring to FIG. 1, apparatus 100 may include a database. The database may include a remote database. The database may be implemented, without limitation, as a relational database, a key-value retrieval database such as a NOSQL database, or any other format or structure for use as database that a person skilled in the art would recognize as suitable upon review of the entirety of this disclosure. The database may alternatively or additionally be implemented using a distributed data storage protocol and/or data structure, such as a distributed hash table or the like. The database may include a plurality of data entries and/or records as described above. …. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which data entries in database may store, retrieve, organize, and/or reflect data and/or records. [0035] …. As used in this disclosure, a "hash table" is a data structure that stores data in a way that allows for fast retrieval, insertion, and deletion of elements. The hash table may organize data into key-value pairs, where each key is unique and used to identify its corresponding value. A hash table may use a hash function to compute an index, or hash code, from the key, which determines where the key-value pair is stored within an array or list); and
output key value pairs associated with the prediction of the valid format.
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the invention, in view of the teachings of Spinaci and Cunningham, to overcome the drawback of inconsistent results due to variability in decision-making and lack of standardized rules (Cunningham [0003]).
10. With respect to claim 3,
Cunningham further discloses wherein the tokenization includes operations to preserve key and value information stored in a source document without reduction to sub-words (Cunningham [0060] e.g. [0060] This integration may ensure that user inputs are seamlessly translated into meaningful system outputs, with the data structure enabling rapid access, consistency, and scalability throughout the process. As used in this disclosure, a "hash table" is a data structure that stores data in a way that allows for fast retrieval, insertion, and deletion of elements. The hash table may organize data into key-value pairs, where each key is unique and used to identify its corresponding value. A hash table may use a hash function to compute an index, or hash code, from the key, which determines where the key-value pair is stored within an array or list).
11. With respect to claim 4,
Spinaci further discloses wherein at least one processor is configured to instantiate a pre-trained transformer model, the pre-trained (Spinaci [0023], [0040], [0044], [0055] e.g. pre-trained model) transformer model trained on dynamic schema database data and data architecture information, to output a next data element based on an input data element.
12. With respect to claim 7,
Cunningham further discloses generate an output of new dynamic schema (Cunningham abstract, [0099] e.g. modify the data schema) database data having a valid format and architecture consistent with the source database in response to an input of data elements taken from a source database including dynamic schema database data.
13. Claims 12, 14-15 and 18 are same as claims 1, 3-4 and 7 and are rejected for the same reasons as applied hereinabove.
14. Claims 2 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Spinaci in view of Cunningham, and further in view of YANG et al (CN 112199352 A hereinafter, “YANG”).
15. With respect to claim 2,
Although Spinaci and Cunningham combination substantially teaches the claimed invention, they do not explicitly indicate wherein the tokenization includes operations to maintain data architecture information associated with the source data.
YANG teaches the limitations by stating wherein the tokenization includes operations to maintain data architecture (YANG abstract e.g. obtaining data information from the data source, data architecture information and task relation information) information associated with the source data.
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the invention, in view of the teachings of Spinaci, Cunningham and YANG, to overcome the drawback of inconsistent results due to variability in decision-making and lack of standardized rules (Cunningham [0003]).
16. Claim 13 is same as claim 2 and is rejected for the same reasons as applied hereinabove.
17. Claims 5-6 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Spinaci in view of Cunningham, and further in view of Park et al (US 12537962 B2 hereinafter, “Park”).
18. With respect to claim 5,
Although Spinaci and Cunningham combination substantially teaches the claimed invention, they do not explicitly indicate
generate a predictive distribution associated with frequency of occurrence of respective data elements in response to an input of data elements to the pre-trained transformer model; and
define an encoding of associated short code words to data elements based on predicted frequency of occurrence.
Park teaches the limitations by stating
generate a predictive distribution associated with frequency of occurrence of respective data elements in response to an input of data elements to the pre-trained transformer model; and
define an encoding of associated short code words to data elements based on predicted frequency of occurrence (Park col. 21 line 47 – col. 22 line 22 e.g. short code word may be assigned to an index with a high frequency of occurrence and a long code word may be assigned to an index with a low frequency of occurrence).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the invention, in view of the teachings of Spinaci, Cunningham and Park, to overcome the drawback of inconsistent results due to variability in decision-making and lack of standardized rules (Cunningham [0003]).
19. With respect to claim 6,
Park further discloses
generate a predictive distribution associated with frequency of occurrence of respective output data elements associated with execution of the query in response to an input of data elements taken from a query under execution to the pre-trained transformer model; and
define an encoding of associated short code words to data elements based on predicted frequency of occurrence (Park col. 21 line 47 – col. 22 line 22 e.g. short code word may be assigned to an index with a high frequency of occurrence and a long code word may be assigned to an index with a low frequency of occurrence).
20. Claims 16-17 are same as claims 5-6 and are rejected for the same reasons as applied hereinabove.
21. Claims 8 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Spinaci in view of Cunningham, and further in view of Hofstetter et al (US 10824625 B1 hereinafter, “Hofstetter”).
22. With respect to claim 8,
Although Spinaci and Cunningham combination substantially teaches the claimed invention, they do not explicitly indicate generate a cardinality estimate for queries without having to execute the queries on the source dataset in response to an input of data elements including dynamic schema database data.
Hofstetter teaches the limitations by stating generate a cardinality estimate for queries without having to execute the queries on the source dataset in response to an input of data elements including dynamic schema database data (Hofstetter col. 1 lines 54-65 e.g. (5) When a query is received by a database engine, the query is parsed and translated into an abstract syntax tree. Semantic analysis turns the syntax tree into an operator tree. Building the operator tree combines the syntax tree with schema information, resolves table and column names, and resolves internal references within the query. During logical optimization, the database engine employs an array of optimization techniques, including join reordering, which leverage the more accurate cardinality estimates. Thus, the database engine described herein is able to better optimize complex database queries, and thereby improves query execution performance).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the invention, in view of the teachings of Spinaci, Cunningham and Hofstetter, to overcome the drawback of inconsistent results due to variability in decision-making and lack of standardized rules (Cunningham [0003]).
23. Claim 19 is same as claim 8 and is rejected for the same reasons as applied hereinabove.
24. Claims 9 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Spinaci in view of Cunningham and Hofstetter, and further in view of BEPLER et al (WO 2024173906 A1 hereinafter, “BEPLER”).
25. With respect to claim 9,
Although Spinaci, Cunningham and Hofstetter combination substantially teaches the claimed invention, they do not explicitly indicate
generate predictive documents unconditionally; and
evaluate generated probabilities of a next token following a runtime key input.
BEPLER teaches the limitations by stating
generate predictive documents unconditionally (BEPLER [0003] – [0008] e.g. prediction … unconditional language models); and
evaluate generated probabilities of a next token following a runtime key input (BEPLER [0039], [0055], [0083] e.g. The SoftMax layer in the module 410 takes these logits and generates next token probabilities. For example, a next predicted token is the argmax of the softmax output.).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the invention, in view of the teachings of Spinaci, Cunningham, Hofstetter and BEPLER, to overcome the drawback of inconsistent results due to variability in decision-making and lack of standardized rules (Cunningham [0003]).
26. Claim 20 is same as claim 9 and is rejected for the same reasons as applied hereinabove.
27. Claims 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Spinaci in view of Cunningham, Hofstetter and BEPLER, and further in view of FENG et al (CN 110807325 A hereinafter, “FNEG”).
28. With respect to claim 10,
Spinaci teaches wherein the at least one processor is configured to analyze the probabilities that match an input (Spinaci [0068], [0081] e.g. probability … given input … prediction).
Although Spinaci, Cunningham, Hofstetter and BEPLER combination substantially teaches the claimed invention, they do not explicitly indicate wherein the at least one processor is configured to analyze the probabilities that match a predicate input.
FENG teaches the limitations by stating wherein the at least one processor is configured to analyze the probabilities that match a predicate input (FENG page 9 e.g. the sample information, and negative sample positive sample predicate predicate input to the first text matching model, first prediction similarity for obtaining sample information and positive sample text matching model based on the first predicate; sample problem information and negative sample predicate of a second prediction similarity corresponding to sample question information and answer information belonging to the plurality of information types of the prediction probability, obtaining the first similarity with the error between the first prediction similarity, the second similarity and the error and answer information similarity between belonging to the second prediction error between prediction probability of each information type and corresponding sample probability, model parameter of the first text matching model so as to make the error convergence based on the adjusted model to obtain the first text matching).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the invention, in view of the teachings of Spinaci, Cunningham, Hofstetter, BEPLER and FDNG, to overcome the drawback of inconsistent results due to variability in decision-making and lack of standardized rules (Cunningham [0003]).
29. With respect to claim 11,
Spinaci further discloses wherein the pre-trained transformer model is configured to generate an output specific to previously generated tokens (Spinaci [0038], [0040] – [0044] e.g. historical … token).
Conclusion
The prior art made of record, listed on form PTO-892, and not relied upon, if any, is considered pertinent to applicant's disclosure.
30. The examiner requests, in response to this office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
31. When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the reference cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SyLing Yen whose telephone number is 571-270-1306. The examiner can normally be reached on Mon-Fri 8:30am - 5:00pm.
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/SYLING YEN/Primary Examiner, Art Unit 2166
August 6, 2026
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