DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 is directed to an abstract idea without significantly more.
Independent claims 1 and 8 recite “receive input text”, “identify one or a plurality of jurisdiction candidates in the input text from a predetermined taxonomy to generate a jurisdictions list”, “transform the jurisdictions list to disambiguate jurisdictions in the jurisdictions list”, and “generate and output the jurisdictions list as a jurisdiction prediction list”.
The limitation of receiving an input text, as drafted, is a process, under its 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 “processor” and “memory”, nothing in the claim precludes the step from practically being performed in the mind. For example, but for the elements listed above, “receive” in the context of this claim encompasses receiving text, which a human can do in the mind or with a pen and paper. Next, the limitation of identifying jurisdiction candidates in order to generate a jurisdiction list, as drafted, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the elements listed above, nothing in the claim precludes the step from practically being performed in the mind. For example, “identify” in the context of this claim encompasses categorizing text, which a human can do in the mind or with a pen and paper. Next, the limitation of transforming the jurisdiction list, as drafted, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the elements listed above, nothing in the claim precludes the step from practically being performed in the mind. For example, “transform” in the context of this claim encompasses altering a list, which a human can do in the mind or with a pen and paper. Lastly, the limitation of outputting the jurisdiction list, as drafted, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the elements listed above, nothing in the claim precludes the step from practically being performed in the mind. For example, “generate and output” in the context of this claim encompasses outputting text, which a human can do in the mind or with a pen and paper.
The judicial exception is not integrated into a practical application. In particular, the claims only recite the additional elements, using a processor and memory to perform the recited limitations. These elements in these steps are recited at a high-level of generality such that is amounts no more than mere instructions to apply the exception using generic computer component. 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 claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of using a processor and memory to perform the recited limitations amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claims are not patent eligible.
Dependent claims 2-7 and 9-14 are also rejected for the same reasons provided in independent claim 1 and 8 above. The dependent claim, including the further recited limitation, does not integrate the abstract idea into a practical application and the additional elements, taken individually and in combination do not contribute to an inventive concept. In other words, the dependent claims are directed to an abstract idea without significantly more.
Independent claim 15 recites “receive input text of the tax law article”, “identify one or a plurality of tax jurisdiction candidates in sentences of the tax law article from a predetermined tax jurisdictions taxonomy to generate the tax jurisdictions list”, “transform the tax jurisdictions list to disambiguate jurisdictions in the tax jurisdictions list”, and “generate and output the tax jurisdictions list as a jurisdiction prediction list”
The limitation of receiving a tax law article, as drafted, is a process, under its 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 “processor” and “memory”, nothing in the claim precludes the step from practically being performed in the mind. For example, but for the elements listed above, “receive” in the context of this claim encompasses receiving text, which a human can do in the mind or with a pen and paper. Next, the limitation of identifying tax jurisdiction candidates in order to generate a tax jurisdiction list, as drafted, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the elements listed above, nothing in the claim precludes the step from practically being performed in the mind. For example, “identify” in the context of this claim encompasses categorizing text, which a human can do in the mind or with a pen and paper. Next, the limitation of transforming the tax jurisdiction list, as drafted, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the elements listed above, nothing in the claim precludes the step from practically being performed in the mind. For example, “transform” in the context of this claim encompasses altering a list, which a human can do in the mind or with a pen and paper. Lastly, the limitation of outputting the tax jurisdiction list, as drafted, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the elements listed above, nothing in the claim precludes the step from practically being performed in the mind. For example, “generate and output” in the context of this claim encompasses outputting text, which a human can do in the mind or with a pen and paper.
The judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements, using a processor and memory to perform the recited limitations. These elements in these steps are recited at a high-level of generality such that is amounts no more than mere instructions to apply the exception using generic computer component. 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 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. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of using a processor and memory to perform the recited limitations amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Dependent claims 16-20 are also rejected for the same reasons provided in independent claim 15 above. The dependent claim, including the further recited limitation, does not integrate the abstract idea into a practical application and the additional elements, taken individually and in combination do not contribute to an inventive concept. In other words, the dependent claims are directed to an abstract idea without significantly more.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 5-11, 14-20 of U.S. Patent No. 12260177 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the issued patent are narrower in scope than that of the instant application.
Instant Application: 19/053,034
Issued Patent: US 12260177 B2
A computing system, comprising: a processor and memory of a computing device, the processor being configured to execute a program using portions of memory to: receive input text; identify one or a plurality of jurisdiction candidates in the input text from a predetermined taxonomy to generate a jurisdictions list; transform the jurisdictions list to disambiguate jurisdictions in the jurisdictions list; and generate and output the jurisdictions list as a jurisdiction prediction list.
1. A computing system, comprising: a processor and memory of a computing device, the processor being configured to execute a program using portions of memory to: receive input text; divide the input text into sentences; identify one or a plurality of jurisdiction candidates in the sentences from a predetermined taxonomy to generate a jurisdictions list; transform the jurisdictions list using a type recognition neural network to disambiguate jurisdictions in the jurisdictions list, the type recognition neural network being trained on a labeled ground truth dataset containing pairs of geographic names and tax jurisdiction types; and generate and output the jurisdictions list as a jurisdiction prediction list.
The computing system of claim 1, wherein labels are assigned to contiguous spans of tokens in the input text.
5. The computing system of claim 1, wherein labels are assigned to contiguous spans of tokens in the sentences using a named entity recognition neural network.
The computing system of claim 2, wherein the labels include labels for organizations and labels for geopolitical entities.
6. The computing system of claim 5, wherein the labels include labels for organizations and labels for geopolitical entities.
The computing system of claim 1, wherein geographic scope is estimated and connections between nearby place names are used to resolve jurisdictions with the same names in the jurisdictions list.
10. The computing system of claim 1, wherein geographic scope is estimated and connections between nearby place names are used to resolve jurisdictions with the same names in the jurisdictions list.
The computing system of claim 2, wherein the labels are filtered out in accordance with a rules-based filter algorithm.
7. The computing system of claim 5, wherein the labels are filtered out in accordance with a rules-based filter algorithm.
The computing system of claim 5, wherein the rules-based filter algorithm includes a rule that jurisdiction candidates in the labels not recognized as named entities be filtered out.
8. The computing system of claim 7, wherein the rules-based filter algorithm includes a rule that jurisdiction candidates in the labels not recognized as named entities by the named entity recognition neural network be filtered out.
The computing system of claim 5, wherein the rules-based filter algorithm includes a rule requiring that the labels be labels for organizations or labels for geopolitical entities.
9. The computing system of claim 7, wherein the rules-based filter algorithm includes a rule requiring that the labels be labels for organizations or labels for geopolitical entities.
A method comprising steps to: receive input text; identify one or a plurality of jurisdiction candidates in the input text from a predetermined taxonomy to generate a jurisdictions list; transform the jurisdictions list to disambiguate jurisdictions in the jurisdictions list; and generate and output the jurisdictions list as a jurisdiction prediction list.
11. A method comprising steps to: receive input text; divide the input text into sentences; identify one or a plurality of jurisdiction candidates in the sentences from a predetermined taxonomy to generate a jurisdictions list; transform the jurisdictions list using a type recognition neural network to disambiguate jurisdictions in the jurisdictions list, the type recognition neural network being trained on a labeled ground truth dataset containing pairs of geographic names and tax jurisdiction types; and generate and output the jurisdictions list as a jurisdiction prediction list.
The method of claim 8, wherein labels are assigned to contiguous spans of tokens in the input text.
14. The method of claim 11, wherein labels are assigned to contiguous spans of tokens in the sentences using a named entity recognition neural network.
The method of claim 9, wherein the labels include labels for organizations and labels for geopolitical entities.
15. The method of claim 14, wherein the labels include labels for organizations and labels for geopolitical entities.
The method of claim 9, wherein geographic scope is estimated and connections between nearby place names are used to resolve jurisdictions with the same names in the jurisdictions list.
19. The method of claim 11, wherein geographic scope is estimated and connections between nearby place names are used to resolve jurisdictions with the same names in the jurisdictions list.
The method of claim 9, wherein the labels are filtered out in accordance with a rules-based filter algorithm.
16. The method of claim 14, wherein the labels are filtered out in accordance with a rules-based filter algorithm.
The method of claim 12, wherein the rules-based filter algorithm includes a rule that jurisdiction candidates in the labels not recognized as named entities be filtered out.
17. The method of claim 16, wherein the rules-based filter algorithm includes a rule that jurisdiction candidates in the labels not recognized as named entities by the named entity recognition neural network be filtered out.
The method of claim 12, wherein the rules-based filter algorithm includes a rule requiring that the labels be labels for organizations or labels for geopolitical entities.
18. The method of claim 16, wherein the rules-based filter algorithm includes a rule requiring that the labels be labels for organizations or labels for geopolitical entities.
A computing system for generating a tax jurisdictions list for a tax law article, comprising: a processor and memory of a computing device, the processor being configured to execute a program using portions of memory to: receive input text of the tax law article; identify one or a plurality of tax jurisdiction candidates in sentences of the tax law article from a predetermined tax jurisdictions taxonomy to generate the tax jurisdictions list; transform the tax jurisdictions list to disambiguate jurisdictions in the tax jurisdictions list; and generate and output the tax jurisdictions list as a jurisdiction prediction list.
20. A computing system for generating a tax jurisdictions list for a tax law article, comprising: a processor and memory of a computing device, the processor being configured to execute a program using portions of memory to: receive input text of the tax law article; identify one or a plurality of tax jurisdiction candidates in sentences of the tax law article from a predetermined tax jurisdictions taxonomy to generate the tax jurisdictions list; transform the tax jurisdictions list using a transformer model to disambiguate jurisdictions in the tax jurisdictions list, the transformer model being trained on a labeled ground truth dataset containing pairs of geographic names and tax jurisdiction types; and generate and output the tax jurisdictions list as a jurisdiction prediction list.
The computing system of claim 15, wherein labels are assigned to contiguous spans of tokens in the input text.
5. The computing system of claim 1, wherein labels are assigned to contiguous spans of tokens in the sentences using a named entity recognition neural network.
The computing system of claim 16, wherein the labels include labels for organizations and labels for geopolitical entities.
6. The computing system of claim 5, wherein the labels include labels for organizations and labels for geopolitical entities.
The computing system of claim 15, wherein geographic scope is estimated and connections between nearby place names are used to resolve jurisdictions with the same names in the jurisdictions list.
10. The computing system of claim 1, wherein geographic scope is estimated and connections between nearby place names are used to resolve jurisdictions with the same names in the jurisdictions list.
The computing system of claim 15, wherein the labels are filtered out in accordance with a rules-based filter algorithm including a rule that jurisdiction candidates in the labels not recognized as named entities be filtered out.
8. The computing system of claim 7, wherein the rules-based filter algorithm includes a rule that jurisdiction candidates in the labels not recognized as named entities by the named entity recognition neural network be filtered out.
The computing system of claim 15, wherein the labels are filtered out in accordance with a rules-based filter algorithm including a rule requiring that the labels be labels for organizations or labels for geopolitical entities.
9. The computing system of claim 7, wherein the rules-based filter algorithm includes a rule requiring that the labels be labels for organizations or labels for geopolitical entities.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 8, and 15 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Nistor et al. US 11861667 B1 (hereinafter Nistor).
Regarding independent claims 1, 8, and 15 Nistor teaches a computing system for generating a tax jurisdictions list for a tax law article, comprising:
a processor and memory of a computing device, the processor being configured to execute a program using portions of memory to ([Column 3, line 51-52] “a system comprising at least one processor and a memory coupled to the at least one processor”):
receive input text of the tax law article (FIG. 9, 905;);
identify one or a plurality of tax jurisdiction candidates in sentences of the tax law article from a predetermined tax jurisdictions taxonomy to generate the tax jurisdictions list ([Column 6, line 36-39] “the service may be determining or generating information regarding one or more estimations of taxes associated with a transaction of customer entity 119 in one or more tax jurisdictions”; FIG. 9, 920, 925; examiner interprets classification codes as the taxonomy);
transform the tax jurisdictions list to disambiguate jurisdictions in the tax jurisdictions list (FIG. 9, 955; [Column 20, line 46-49] “the processor-based system determines an estimated total taxation amount due for the proposed transaction based on the individual estimates for each of the items that are the subject of that transaction”, examiner interprets the various items to form a list); and
generate and output the tax jurisdictions list as a jurisdiction prediction list (FIG. 9, 960;).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 2-4, 9-11, and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Nistor in view of Hsiao et al. US 20180285773 A1 (hereinafter Hsiao).
Regarding claims 2, 9, and 16, Nistor teaches all of the limitations of claims 1, 8, and 15, upon which claims 2, 9, and 16 depend.
Nistor fails to teach wherein labels are assigned to contiguous spans of tokens in the input text.
However, Hsiao teaches wherein labels are assigned to contiguous spans of tokens in the input text (FIG. 3, 304; [0051] “the machine-learning model is a logistic regression model that predicts the label based, at least in part, on features derived from a descriptive string for the transaction”)
Nistor in view of Hsiao are considered to be analogous to the claimed invention because both are the same field of classifying business transactions. 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 the technique of determining an estimated amount of taxes due in association with a proposed transaction based on a risk tolerance value specified by a party to the transaction of Nistor with the technique of labeling spans of tokens taught by Hsiao in order to improve a composite machine-learning system that determines labels for transactions (see Hsiao [0001]).
Regarding claims 3, 10, and 17, Nistor in view of Hsiao teaches all of the limitations of claims 2, 9, and 16, upon which claims 3, 10, and 17 depend.
Additionally, Hsiao teaches wherein the labels include labels for organizations and labels for geopolitical entities ([0038] “the labeling scheme uses categories defined the tax law of a jurisdiction that has authority to tax the user. For example, if the United States is the jurisdiction, the labeling scheme can include the “schedule C” categories for deductible expenses defined by the Internal Revenue Service (IRS)”).
Regarding claims 4, 11, and 18, Nistor teaches all of the limitations of claims 1, 8, and 15, upon which claims 4, 11, and 18 depend.
Nistor fails to teach wherein geographic scope is estimated and connections between nearby place names are used to resolve jurisdictions with the same names in the jurisdictions list.
However, Hsiao teaches wherein geographic scope is estimated and connections between nearby place names are used to resolve jurisdictions with the same names in the jurisdictions list ([0045-0047] “The relabeling history 220 also includes a similar record for each previous transaction the user relabeled. In other words, each record in the relabeling history 220 includes a respective transaction (one or more descriptive strings and other transaction information, in some embodiments) and a respective corrected label… the application 116 compares the cardinality of the set (i.e., the number of transaction records in the set) to a threshold number. If the cardinality meets the threshold number, the application 116 verifies whether a predefined percentage (e.g., 100%) of the transaction records in the set include the same corrected label”).
Nistor in view of Hsiao are considered to be analogous to the claimed invention because both are the same field of classifying business transactions. 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 the technique of determining an estimated amount of taxes due in association with a proposed transaction based on a risk tolerance value specified by a party to the transaction of Nistor with the technique of resolving jurisdictions taught by Hsiao in order to improve a composite machine-learning system that determines labels for transactions (see Hsiao [0001]).
Claims 5-7, 12-14, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Nistor in view of Hsiao as shown above for claim 2, in further view of Bianco et al US 20110029420 A1 (hereinafter Bianco).
Regarding claims 5 and 12, Nistor in view of Hsiao teaches all of the limitations of claims 2 and 9, upon which claims 5 and 12 depend.
Nistor in view of Hsiao fails to teach wherein the labels are filtered out in accordance with a rules-based filter algorithm.
However, Bianco teaches wherein the labels are filtered out in accordance with a rules-based filter algorithm ([0044] “Any of the client or server devices described may have tangible computer readable media with logic, code, or instructions for performing any actions described herein or running any algorithm”; [0118] A category field 528b may enable a user to filter through contacts based on a contact category).
Nistor in view of Hsiao in view of Bianco are considered to be analogous to the claimed invention because all are the same field of classifying business transactions. 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 the technique of classification techniques of Nistor in view of Hsiao with the technique of labeling with a rule-based filter taught by Bianco in order to improve systems and methods for a manager toolkit (see Bianco [0001]).
Regarding claims 6, 13, and 19, Nistor in view of Hsiao in view of Bianco teaches all of the limitations of claims 5, 12, and 15, upon which claims 6, 13, and 19 depend.
Additionally, Bianco teaches wherein the rules-based filter algorithm includes a rule that jurisdiction candidates in the labels not recognized as named entities be filtered out ([0120] “a mandatory category field may filter out contacts that do not meet the criteria of the category, while an optional category field may rank the contacts in an order based on the criteria of the optional category”).
Regarding claims 7, 14, and 20, Nistor in view of Hsiao in view of Bianco teaches all of the limitations of claims 5, 12, and 15, upon which claims 7, 14, and 20 depend.
Additionally, Bianco teaches wherein the rules-based filter algorithm includes a rule requiring that the labels be labels for organizations or labels for geopolitical entities ([0170] “Some focus filters may include locations, skills, or user searches, or any other types of categories relating to contacts”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
William et al. (US 20040019541 A1) teaches a system for determining taxes that is configurable for local jurisdictions. The system includes a tax knowledge base that provides the facility to store data pertaining to taxes in local jurisdictions, and a tax rule base that provides the facility to store rules for applying taxes in local jurisdictions. The system also includes a tax determination manager that determines the tax for a transaction using the tax knowledge base and, optionally, the tax rule base.
Dang et al. (US 11915123 B2) teaches a system, program product, and method for employing deep learning techniques to fuse data across modalities. A multi-modal data set is received, including a first data set having a first modality and a second data set having a second modality, with the second modality being different from the first modality. The first and second data sets are processed, including encoding the first data set into one or more first vectors, and encoding the second data set into one or more second vectors. The processed multi-modal data set is analyzed, and the encoded features from the first and second modalities are iteratively and asynchronously fused. The fused modalities include combined vectors from the first and second data sets representing correlated temporal behavior. The fused vectors are then returned as output data.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZEESHAN SHAIKH whose telephone number is (703)756-1730. The examiner can normally be reached Monday-Friday 7:30AM-5:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Richemond Dorvil can be reached at (571) 272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ZEESHAN MAHMOOD SHAIKH/Examiner, Art Unit 2658
/RICHEMOND DORVIL/Supervisory Patent Examiner, Art Unit 2658
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