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
2. This office action is in response to the original filing of 03/24/2023. Claim 1-20 are pending and have been considered below.
Claim Rejections - 35 USC § 101
3. 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.
I. Claim 17 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 17 recites a “computer readable storage medium” storing instructions…. The specification of the present application is silent regarding the “computer readable storage medium”). Thus, the broadest, reasonable interpretation of “computer readable storage medium” encompasses non-statutory subject matter (transmission media, i.e. signal or non-transitory media) that is unpatentable under 35 U.S.C. 101.
Claim 20 as a dependent claim is also rejected under the same rationale.
II. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter of abstract ideas.
Step 1:
Claims 1-20 are directed to a method/system/medium which is one of the statutory categories of invention.
Step 2A:
Prong 1:
Claims 1, 16, and 17 are directed to an abstract idea without significantly more. The claims recite the steps of:
“determining a training dataset for training a model, the training dataset comprising a plurality of example documents, each example document being associated with a first hierarchical level category label and a second hierarchical level category label, wherein first and second hierarchical levels are different hierarchical levels within a hierarchical structure of a common attribute, and the second hierarchical level category label is a subcategory of the first hierarchical level category label” [constitutes an evaluation concept that could practically be performed in the human mind]; and for each example document in the training dataset: “providing an example document to a numerical representation generation model to generate a numerical representation of the example document” [a human can calculate assign a number to a document utilizing an algorithm (i.e., model). Thus, these steps constitute a mathematical concept that could practically be performed in the human mind];
“providing the numerical representation of the example document to a first hierarchical level attribute predictor to generate a predicted first hierarchical level category” [a human can feed the number utilizing an algorithm (i.e., model). Thus, these steps constitute a mathematical concept that could practically be performed in the human mind];
“determining a predicted second hierarchical level category” [constitutes an evaluation concept that could practically be performed in the human mind];
“determining a first loss value based on the predicted first hierarchical level category and the first hierarchical level category label associated with the example document” [a human can calculate an error utilizing an algorithm (i.e., model). Thus, these steps constitute a mathematical concept that could practically be performed in the human mind];
“determining a second loss value based on the predicted second hierarchical level category and the second hierarchical level category label associated with the example document” [a human can calculate an error utilizing an algorithm (i.e., model). Thus, these steps constitute a mathematical concept that could practically be performed in the human mind];
“determining a combined loss value based on the first loss value and the second loss value” [a human can calculate an error utilizing an algorithm (i.e., model). Thus, these steps constitute a mathematical concept that could practically be performed in the human mind];
“adjusting one or more weights of the numerical representation generation model based on the combined loss value”; [constitutes an evaluation concept that could practically be performed in the human mind]; and
determining the numerical representation generation model and the first hierarchical level attribute predictor to be a trained transaction attribute prediction model; [a human can calculate a numerical representation utilizing an algorithm (i.e., model). Thus, these steps constitute a mathematical concept that could practically be performed in the human mind].
As such above-mentioned steps are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. A human can mentally identify a dataset, assign labels hierarchically, calculate values (i.e., numerical representation) for the labels using a pen and a paper based on an algorithm or model.
That is, nothing in the claim element precludes the step from practically being performed in a human mind or with 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).
Prong 2:
This judicial exception is not integrated into a practical application. Claims 16 and 17 further recite generic computer components (e.g., a “memory” and a “processor”, and a storage medium) to implement the steps of the invention. Said generic components are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component and considered to be insignificant extra solution activities. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. See MPEP 2106.04(d) and 2106.05(g).
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The generic components recited in claims 16 and 17 (e.g., a “memory” and a “processor”, and a storage medium) are at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. As discussed above with respect to integration of the abstract idea into a practical application. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Claim 2 recites “wherein the first hierarchical level is a lower level in the hierarchical structure than the second hierarchical level.” mere details that constitute evaluation and mathematical concepts that are mental processes. The claim does not add to more than abstract idea.
Claim 3 recites “wherein determining a predicted second hierarchical level category comprises: providing the numerical representation of the example document to a second hierarchical level attribute predictor to generate the predicted second hierarchical level category” mere details that constitute evaluation and mathematical concepts that constitute evaluation and mathematical concepts that could practically be performed in the human mind. The claim does not add to more than abstract idea.
Claim 4 recites “wherein determining a predicted second hierarchical level category comprises: querying a hierarchical chart of accounts, each account being associated with multiple hierarchical level categories, using the predicted first hierarchical level category; and determining the predicted second hierarchical level category as a parent category of the predicted first hierarchical level category” constitute evaluation and mathematical concepts that could practically be performed in the human mind. The claim does not add to more than abstract idea.
Claim 5 recites “wherein each example document of the training dataset is further associated with a third hierarchical level category label, wherein a third hierarchical level is different from the first and second hierarchical levels within the hierarchical structure of the common attribute, the method further comprising: for each example document in the training dataset: determining a predicted third hierarchical level category; and determining a third loss value based on the predicted third hierarchical level category and the third hierarchical level category label associated with the example document; wherein determining the combined loss value is further based on the third loss value” constitute evaluation and mathematical concepts that could practically be performed in the human mind. The claim does not add to more than abstract idea.
Claim 6 recites “wherein determining the predicted third hierarchical level category comprises: providing the numerical representation of the example document to a third hierarchical level attribute predictor to generate the predicted third hierarchical level category” constitute evaluation and mathematical concepts that could practically be performed in the human mind. The claim does not add to more than abstract idea.
Claim 7 recites “wherein determining the predicted third hierarchical level category comprises: querying a hierarchical chart of accounts, each account being associated with multiple hierarchical level categories, using the predicted first hierarchical level category; and determining the predicted third hierarchical level category as a grandparent category of the predicted first hierarchical level category” constitute evaluation and mathematical concepts that could practically be performed in the human mind. The claim does not add to more than abstract idea.
Claim 8 recites “wherein the first hierarchical level of the hierarchical structure of the common attribute is an account code” The additional element is recited so the claim does not provide a practical application and is not considered to be significant.
Claim 9 recites “wherein the second hierarchical level of the hierarchical structure of the common attribute is an account type”. The additional element is recited so the claim does not provide a practical application and is not considered to be significant.
Claim 10 recites “wherein the third hierarchical level of the hierarchical structure of the common attribute is an account class”. The additional element is recited so the claim does not provide a practical application and is not considered to be significant.
Claim 11 recites “wherein the second hierarchical level of the hierarchical structure of the common attribute is an account class” The additional element is recited so the claim does not provide a practical application and is not considered to be significant.
Claim 12 recites “wherein the first hierarchical level of the hierarchical structure of the common attribute is an account type”. The additional element is recited so the claim does not provide a practical application and is not considered to be significant.
Claim 13 recites “wherein the second hierarchical level of the hierarchical structure of the common attribute is an account class”. The additional element is recited so the claim does not provide a practical application and is not considered to be significant.
Claim 14 recites “wherein determining the combined loss value comprises: applying a first weighting to the first loss value and applying a second weighting to the second loss value”. The additional element constitutes an evaluation concept and is not considered to be significant.
Claim 15 recites “further comprising: deploying the trained transaction attribute prediction model on an accounting system” is considered to be a well-known and well-understood computing activity previously known to the industry that specified at high level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Claim 18 recites “wherein the hierarchical structure is a hierarchical classification structure of a chart of accounts” mere details of the abstract idea and is not considered to be significant.
Claim 19 recites “wherein the hierarchical structure is a hierarchical classification structure of a chart of accounts” mere details of the abstract idea and is not considered to be significant.
Claim 20 recites “wherein the hierarchical structure is a hierarchical classification structure of a chart of accounts” mere details of the abstract idea and is not considered to be significant.
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.
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.
5. Claims 1-7, and 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhong et al. (US 2021/0286989) in view of Young et al. (US 2021/0287301).
Claim 1. Zhong discloses a method comprising:
determining a training dataset for training a model, the training dataset comprising a plurality of example documents (training methodology 460 uses a neural network 300 processes (trains) data (sets) records (documents), each example document being associated with a first hierarchical level category label and a second hierarchical level category label) ([0037], (0054]), wherein first and second hierarchical levels are different hierarchical levels within a hierarchical structure of a common attribute (auxiliary tasks are broken up into format types (common attributes) which are ranked (hierarchically) based on complexity scale from 1 to 10) ([0037], [0040]); and
for each example document in the training dataset:
providing an example document to a numerical representation generation model to generate a numerical representation of the example document (layout recognition model 210 (numerical representation generation model) converts documents into numerical representations); (Fig. 4A, ([0037], [00046], [0056]);
providing the numerical representation of the example document to a first hierarchical level attribute predictor to generate a predicted first hierarchical level category (layout recognition model 510 sends numerical data to OCR sub model (first level hierarchical level predictor) delivers the numerical data to the appropriate (predicted) format type understanding model) ([0074]-[0075]);
determining a predicted second hierarchical level category (layout recognition model 510 sends numerical data to the list understanding sub-model (second category)) ([0074]-[0075]);
determining a first loss value based on the predicted first hierarchical level category and the first hierarchical level category label associated with the example document (first joint training stage optimizes the losses (values) of the individual OCR auxiliary tasks) ([0041], [0079]);
determining a second loss value based on the predicted second hierarchical level category and the second hierarchical level category label associated with the example document (second joint training stage optimizes losses of list understanding sub-model categories) ([0041], [0079]);
determining a combined loss value based on the first loss value and the second loss value (loss values from the first and second hierarchal category are combined to improve overall performance) ([0079]); and
adjusting one or more weights of the numerical representation generation model based on the combined loss value (training methodology 460 uses the first and second joint training stages to improve overall performance of layer recognition model) (Fig. 4A, [0041]), [0068], [0079]); and
determining the numerical representation generation model and the first hierarchical level attribute predictor to be a trained transaction attribute prediction model (since the model is trained to determine extracted features (attributes) for any unstructured business (transaction) data to optimize multitask trained model 436A) (Fig. 5,[0035]).
Zhong does not disclose the second hierarchical level category label is a subcategory of the first hierarchical level category label
However, Young discloses the second hierarchical level category label is a subcategory of the first hierarchical level category label (database has hierarchy table with parent-child hierarchies) ([0061]). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhong further in view of Young, in order to provide the advantages of a faster organization and processing speed by machine learning algorithms.
Claim 2. Zhong and Young disclose the method of claim 1, Zhong further discloses wherein the first hierarchical level is a lower level in the hierarchical structure than the second hierarchical level (the auxiliary tasks are ranked in complexity from lowest to highest A-H with A being the lowest complexity and H being the highest) ([0069]).
Claim 3. Zhong and Young disclose the method of claim 1, Zhong further discloses wherein determining a predicted second hierarchical level category comprises: providing the numerical representation of the example document to a second hierarchical level attribute predictor to generate the predicted second hierarchical level category (layout recognition model 510 passes data list understanding sub-model) ([0074]-[0075]).
Claim 4. Zhong and Young disclose the method of claim 1, Young further discloses wherein determining a predicted second hierarchical level category comprises: querying a hierarchical chart of accounts, each account being associated with multiple hierarchical level categories, using the predicted first hierarchical level category (querying a hierarchical chart of accounts, each account being associated with multiple hierarchical level categories (finding (querying) an account in a database that stores account info by types (hierarchies) in the ledgers) ([0031], [0061]); and determining the predicted second hierarchical level category as a parent category of the predicted first hierarchical level category (database has hierarchy table with parent-child hierarchies) ([0061]). One would have been motivated to do so in order to provide the advantages of a faster organization and processing speed by machine learning algorithms.
Claim 5. Zhong and Young disclose the method of claim 1, Zhong further discloses wherein each example document of the training dataset is further associated with a third hierarchical level category label, wherein a third hierarchical level is different from the first and second hierarchical levels within the hierarchical structure of the common attribute, the method further comprising: for each example document in the training dataset: determining a predicted third hierarchical level category (layout recognition model 510 sends numerical data to the list understanding sub-model (second category)) ([0074]-[0075]); and determining a third loss value based on the predicted third hierarchical level category and the third hierarchical level category label associated with the example document (providing multiple joint training stages optimize losses of list understanding sub-model categories) ([0041], [0079]); wherein determining the combined loss value is further based on the third loss value (loss values from the first and second hierarchal category are combined to improve overall performance) ([0079]).
Claim 6. Zhong and Young disclose the method of claim 5, Zhong further discloses wherein determining the predicted third hierarchical level category comprises: providing the numerical representation of the example document to a third hierarchical level attribute predictor to generate the predicted third hierarchical level category (layout recognition model 510 passes data list understanding sub-model) ([0074]-[0075]).
Claim 7. Zhong and Young disclose the method of claim 5, Young further discloses wherein determining the predicted third hierarchical level category comprises: querying a hierarchical chart of accounts, each account being associated with multiple hierarchical level categories, using the predicted first hierarchical level category(querying a hierarchical chart of accounts, each account being associated with multiple hierarchical level categories (finding (querying) an account in a database that stores account info by types (hierarchies) in the ledgers) ([0031], [0061]); and determining the predicted third hierarchical level category as a grandparent category of the predicted first hierarchical level category (database has hierarchy table with parent-child hierarchies) ([0061]). One would have been motivated to do so in order to provide the advantages of a faster organization and processing speed by machine learning algorithms.
Claim 14. Zhong and Young disclose the method of claim 1, Zhong further discloses wherein determining the combined loss value comprises: applying a first weighting to the first loss value and applying a second weighting to the second loss value (selection of the weighted sum of the losses of the auxiliary tasks) ([0041]).
Claim 15. Zhong and Young disclose the method of claim 1, Young further discloses comprising: deploying the trained transaction attribute prediction model on an accounting system ([0030]). One would have been motivated to do so in order to provide the advantages of a faster organization and processing speed by machine learning algorithms.
Claims 16 and 17 represent the system and medium of claim 1, respectively and are rejected along the same rationale.
Claim 18. Zhong and Young disclose the method of claim 1, Young further discloses wherein the hierarchical structure is a hierarchical classification structure of a chart of accounts (General Ledger Chart of Accounts…) ([0030]). One would have been motivated to do so in order to provide the advantages of a faster organization and processing speed by machine learning algorithms.
Claim 19. Zhong and Young disclose the system of claim 16, Young further discloses wherein the hierarchical structure is a hierarchical classification structure of a chart of accounts (General Ledger Chart of Accounts…) ([0030]). One would have been motivated to do so in order to provide the advantages of a faster organization and processing speed by machine learning algorithms.
Claim 20. Zhong and Young disclose the computer-readable storage medium of claim 17, Young further discloses wherein the hierarchical structure is a hierarchical classification structure of a chart of accounts (General Ledger Chart of Accounts…) ([0030]). One would have been motivated to do so in order to provide the advantages of a faster organization and processing speed by machine learning algorithms.
6. Claims 8-13 are rejected under 35 U.S.C. 103 as being unpatentable over Zhong et al. (US 2021/0286989) in view of Young et al. (US 2021/0287301) and further in view of Kumar et al. (US 2015/0205848).
Claim 8. Zhong and Young disclose the method of claim 1, but fail to explicitly disclose wherein the first hierarchical level of the hierarchical structure of the common attribute is an account code
However, Kumar discloses the first hierarchical level of the hierarchical structure of the common attribute is an account code (parent-child node color coded) ([0167]). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhong further in view of Kumar One would have been motivated to do so in order to identify trends, diagnose problems, and/or otherwise evaluate the inventory.
Claim 9. Zhong and Young disclose the method of claim 1, but fail to explicitly disclose wherein the second hierarchical level of the hierarchical structure of the common attribute is an account type.
However, Kumar discloses the second hierarchical level of the hierarchical structure of the common attribute is an account type ([0096], [0223]). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhong further in view of Kumar One would have been motivated to do so in order to identify trends, diagnose problems, and/or otherwise evaluate the inventory.
Claim 10. Zhong and Young disclose the method of claim 5, but fail to explicitly disclose wherein the third hierarchical level of the hierarchical structure of the common attribute is an account class.
However, Kumar discloses the third hierarchical level of the hierarchical structure of the common attribute is an account class ([0096], [0223]). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhong further in view of Kumar One would have been motivated to do so in order to identify trends, diagnose problems, and/or otherwise evaluate the inventory.
Claim 11. Zhong and Young disclose the method of claim 1, but fail to explicitly disclose wherein the second hierarchical level of the hierarchical structure of the common attribute is an account class.
However, Kumar discloses the second hierarchical level of the hierarchical structure of the common attribute is an account class ([0096], [0223]). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhong further in view of Kumar One would have been motivated to do so in order to identify trends, diagnose problems, and/or otherwise evaluate the inventory.
Claim 12. Zhong and Young disclose the method of claim 1, wherein the first hierarchical level of the hierarchical structure of the common attribute is an account type.
However, Kumar discloses the first hierarchical level of the hierarchical structure of the common attribute is an account type ([0096], [0223]). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhong further in view of Kumar One would have been motivated to do so in order to identify trends, diagnose problems, and/or otherwise evaluate the inventory.
Claim 13. Zhong and Young disclose the method of claim 12, wherein the second hierarchical level of the hierarchical structure of the common attribute is an account class.
However, Kumar discloses the second hierarchical level of the hierarchical structure of the common attribute is an account class ([0096], [0223]). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhong further in view of Kumar One would have been motivated to do so in order to identify trends, diagnose problems, and/or otherwise evaluate the inventory.
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure (See PTO-892).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Phenuel S. Salomon whose telephone number is (571) 270-1699. The examiner can normally be reached on Mon-Fri 7:00 A.M. to 4:00 P.M. (Alternate Friday Off) EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Usmaan Saeed can be reached on (571) 272-4046. The fax phone number for the organization where this application or proceeding is assigned is 571-273-3800.
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/PHENUEL S SALOMON/Primary Examiner, Art Unit 2146