DETAILED ACTION
Status of Claims
Claims 1 – 20, which are currently pending, are fully considered below.
Claims 2 - 20 are added.
No claims are canceled.
Claim 1 is amended.
Priority
The instant application is a CON of U.S. Patent Application 18/210391, which is U.S. Patent 12,306,855.
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 .
Double Patenting
A rejection based on double patenting of the “same invention” type finds its support in the language of 35 U.S.C. 101 which states that “whoever invents or discovers any new and useful process... may obtain a patent therefor...” (Emphasis added). Thus, the term “same invention,” in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957).
A statutory type (35 U.S.C. 101) double patenting rejection can be overcome by canceling or amending the claims that are directed to the same invention so they are no longer coextensive in scope. The filing of a terminal disclaimer cannot overcome a double patenting rejection based upon 35 U.S.C. 101.
Claims 1 – 8, 10 – 12, 15, and 19 are rejected under 35 U.S.C. 101 as claiming the same invention as that of claims 1 – 6, 9, 11 – 13. And 16 - 18 of prior U.S. Patent No. 12,306,855. This is a statutory double patenting rejection.
Instant 19/204,047
U.S. Patent 12,306,855
1. A method comprising:
accessing classifiable data from a first organizational unit of a plurality of organizational units of an input data structure;
generating a character encoding of the classifiable data;
providing the character encoding to a classifier configured to
perform a character-level classification on the character encoding;
determining at least one label for the first organizational unit based on the character- level classification, wherein the classifier comprises a convolutional neural network (CNN).
3. The method of claim 1, wherein the classifiable data is broken into a plurality of characters used for the character encoding.
1. A computer-implemented method, comprising:
accessing classifiable data from a first organizational unit of a plurality of organizational units of an input data structure;
generating a character encoding of the classifiable data;
providing the character encoding to a classifier configured to:
perform a character-level classification on the character encoding; and
determine at least one label for the first organizational unit based on the character-level classification, wherein the classifiable data is broken into a plurality of characters used for the character encoding.
3. The computer-implemented method of claim 21, wherein the classifier comprises a convolutional neural network (CNN).
2. The method of claim 1, wherein the organizational units are rows or columns in a table of the input data structure.
2. The computer-implemented method of claim 1, wherein the organizational units are rows or columns in a table of the input data structure.
4. The method of claim 1, wherein the CNN is configured to perform convolutions around the plurality of organizational units.
4. The computer-implemented method of claim 3, wherein the CNN is configured to perform convolutions around the plurality of organizational units.
5. The method of claim 1, wherein the CNN is configured as a conditional random field (CRF).
5. The computer-implemented method of claim 3, wherein the CNN is configured as a conditional random field (CRF).
6. The method of claim 1, wherein accessing the classifiable data includes breaking the plurality of organizational units into chunks of a predetermined size.
6. The computer-implemented method of claim 1, wherein accessing the classifiable data comprises breaking the plurality of organizational units into chunks of a predetermined size.
7. An apparatus comprising: a processing circuit; and a memory coupled to the processing circuit, the memory including executable instructions, which when executed by the processing circuit, causes the processing circuit to:
access classifiable data from a first organizational unit of a plurality of organizational units of an input data structure; generate a character encoding of the classifiable data;
provide the character encoding to a classifier configured to: filter at least a portion of the classifiable data; and perform a character-level classification on the character encoding.
8. The apparatus of claim 7, wherein the processing circuit is further caused to determine at least one label for the first organizational unit based on the character-level classification.
9. An apparatus comprising: at least one processor; and a memory coupled to the at least one processor, the memory comprising instructions that, when executed by the at least one processor, cause the at least one processor to:
access classifiable data from a first organizational unit of a plurality of organizational units of an input data structure; generate a character encoding of the classifiable data;
provide the character encoding to a classifier configured to: perform a character-level classification on the character encoding; and determine at least one label for the first organizational unit based on the character-level classification, wherein the classifiable data is broken into a plurality of characters used for the character encoding.
10. The apparatus of claim 7, wherein the classifier comprises a convolutional neural network (CNN).
11. The apparatus of claim 9, wherein the classifier comprises a convolutional neural network (CNN).
11. The apparatus of claim 10, wherein the CNN is configured to perform convolutions around the plurality of organizational units.
12. The apparatus of claim 11, wherein the CNN is configured to perform convolutions around the plurality of organizational units.
12. The apparatus of claim 10, wherein the CNN is configured as a conditional random field (CRF).
13. The apparatus of claim 11, wherein the CNN is configured as a conditional random field (CRF).
15. A non-transitory computer-readable storage medium having executable instructions stored thereon, which when executed by a processing circuit, cause the processing circuit to:
receive classifiable data from a first organizational unit of a plurality of organizational units of an input data structure; sample the classifiable data to generate a character encoding of the classifiable data; provide the character encoding to a classifier configured to perform a character-level classification on the character encoding; determine at least one label for the first organizational unit based on the character-level classification, wherein the classifier comprises a convolutional neural network (CNN).
16. A non-transitory computer-readable medium storing instructions configured to cause one or more processors of a computing device to:
access classifiable data from a first organizational unit of a plurality of organizational units of an input data structure; generate a character encoding of the classifiable data; provide the character encoding to a classifier configured to: perform a character-level classification on the character encoding; and determine at least one label for the first organizational unit based on the character-level classification, wherein the classifiable data is broken into a plurality of characters used for the character encoding.
17. The non-transitory computer-readable medium of claim 16, wherein the classifier comprises a convolutional neural network (CNN).
19. The non-transitory computer-readable storage medium of claim 15, wherein the CNN is configured as a conditional random field (CRF).
18. The non-transitory computer-readable medium of claim 17, wherein the CNN is configured as a conditional random field (CRF).
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 the judicial exception of an abstract idea without significantly more.
Claim 1 recites:
accessing classifiable data from a first organizational unit of a plurality of organizational units of an input data structure;
a character encoding of the classifiable data;
providing the character encoding to a classifier configured to perform a character-level classification on the character encoding;
determining at least one label for the first organizational unit based on the character-level classification, wherein the classifier comprises a convolutional neural network (CNN).
Step 2A Prong One
Claim 1 limitations of “generating a character encoding…” and “determining at least one label” recite abstract ideas as mathematical concepts or mental processes. “Generating a character encoding” recites a mathematical concept although it is not expressed in mathematical symbols. “Determining at least one label” may be done as a mental process, in the human mind.
MATHEMATICAL CONCEPTS
The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. MPEP 2106.04(a)(2)(I). The Supreme Court has identified a number of concepts falling within this grouping as abstract ideas including: a procedure for converting binary-coded decimal numerals into pure binary form, Gottschalk v. Benson, 409 U.S. 63, 65, 175 USPQ2d 673, 674 (1972); a mathematical formula for calculating an alarm limit, Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ2d 193, 195 (1978); the Arrhenius equation, Diamond v. Diehr, 450 U.S. 175, 191, 209 USPQ 1, 15 (1981); and a mathematical formula for hedging, Bilski v. Kappos, 561 U.S. 593, 611, 95 USPQ 2d 1001, 1004 (2010).
The Court’s rationale for identifying these "mathematical concepts" as judicial exceptions is that a ‘‘mathematical formula as such is not accorded the protection of our patent laws,’’ Diehr, 450 U.S. at 191, 209 USPQ at 15 (citing Benson, 409 U.S. 63, 175 USPQ 673), and thus ‘‘the discovery of [a mathematical formula] cannot support a patent unless there is some other inventive concept in its application.’’ Flook, 437 U.S. at 594, 198 USPQ at 199. In the past, the Supreme Court sometimes described mathematical concepts as laws of nature, and at other times described these concepts as judicial exceptions without specifying a particular type of exception. See, e.g., Benson, 409 U.S. at 65, 175 USPQ2d at 674; Flook, 437 U.S. at 589, 198 USPQ2d at 197; Mackay Radio & Telegraph Co. v. Radio Corp. of Am., 306 U.S. 86, 94, 40 USPQ 199, 202 (1939) (‘‘[A] scientific truth, or the mathematical expression of it, is not patentable invention[.]’’). More recent opinions of the Supreme Court, however, have affirmatively characterized mathematical relationships and formulas as abstract ideas. See, e.g., Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 218, 110 USPQ2d 1976, 1981 (2014) (describing Flook as holding "that a mathematical formula for computing ‘alarm limits’ in a catalytic conversion process was also a patent-ineligible abstract idea."); Bilski v. Kappos, 561 U.S. 593, 611-12, 95 USPQ2d 1001, 1010 (2010) (noting that the claimed "concept of hedging, described in claim 1 and reduced to a mathematical formula in claim 4, is an unpatentable abstract idea,").
It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a ‘‘process of organizing information through mathematical correlations’’ are directed to an abstract idea); and Bancorp Servs., LLC v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1280, 103 USPQ2d 1425, 1434 (Fed. Cir. 2012) (identifying the concept of ‘‘managing a stable value protected life insurance policy by performing calculations and manipulating the results’’ as an abstract idea).
MENTAL PROCESSES MPEP 2106.04(a)(2)(III).
The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012) ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same).
Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.
The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016) (holding that claims to a mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper").
Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016) (holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer").
Because both product and process claims may recite a "mental process", the phrase "mental processes" should be understood as referring to the type of abstract idea, and not to the statutory category of the claim. The courts have identified numerous product claims as reciting mental process-type abstract ideas, for instance the product claims to computer systems and computer-readable media in Versata Dev. Group. v. SAP Am., Inc., 793 F.3d 1306, 115 USPQ2d 1681 (Fed. Cir. 2015).
Step 2A Prong 2
Claim 1, as a whole, fails to integrate the recited judicial exception into a practical application of the exception.
The limitation of “accessing classifiable data from a first organizational unit of a plurality of organizational units of an input data structure” appears to be mere data gathering and “providing the character encoding to a classifier configured to perform a character-level classification on the character encoding” appears to be mere instructions to apply this on a computer as a generic CNN.
The mere recitation of the “classification” and “determining” using a CNN is merely applying this on a generic computer component and/or linking to the field of use. As stated in the spec in [0008] and [0042] “various types of classifiers may be used” of which a CNN is just one generic form of such.
Integration of a Judicial Exception Into A Practical Application 2106.04(d)
The Supreme Court has long distinguished between principles themselves (which are not patent eligible) and the integration of those principles into practical applications (which are patent eligible). See, e.g., Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 80, 84, 101 USPQ2d 1961, 1968-69, 1970 (2012) (noting that the Court in Diamond v. Diehr found ‘‘the overall process patent eligible because of the way the additional steps of the process integrated the equation into the process as a whole,’’ but the Court in Gottschalk v. Benson ‘‘held that simply implementing a mathematical principle on a physical machine, namely a computer, was not a patentable application of that principle’’). Similarly, in a growing body of decisions, the Federal Circuit has distinguished between claims that are ‘‘directed to’’ a judicial exception (which require further analysis to determine their eligibility) and those that are not (which are therefore patent eligible), e.g., claims that improve the functioning of a computer or other technology or technological field. See Diamond v. Diehr, 450 U.S. 175, 209 USPQ 1 (1981); Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972). See, e.g., MPEP § 2106.06(b) (summarizing Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 118 USPQ2d 1684 (Fed. Cir. 2016), McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 120 USPQ2d 1091 (Fed. Cir. 2016), and other cases that were eligible as improvements to technology or computer functionality instead of being directed to abstract ideas).
The Supreme Court and Federal Circuit have identified a number of considerations as relevant to the evaluation of whether the claimed additional elements demonstrate that a claim is directed to patent-eligible subject matter. The list of considerations here is not intended to be exclusive or limiting. Additional elements can often be analyzed based on more than one type of consideration and the type of consideration is of no import to the eligibility analysis. Additional discussion of these considerations, and how they were applied in particular judicial decisions, is provided in MPEP § 2106.05(a) through (c) and MPEP § 2106.05(e) through (h).
Limitations the courts have found indicative that an additional element (or combination of elements) may have integrated the exception into a practical application include:
An improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2); [AltContent: rect]
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
The courts have also identified limitations that did not integrate a judicial exception into a practical application:
Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitation of “accessing classifiable data from a first organizational unit of a plurality of organizational units of an input data structure” appears to be mere data gathering and “providing the character encoding to a classifier configured to perform a character-level classification on the character encoding” appears to be mere instructions to apply this on a computer as a generic CNN.
The mere recitation of the “classification” and “determining” using a CNN is merely applying this on a generic computer component and/or linking to the field of use. As stated in the spec in [0008] and [0042] “various types of classifiers may be used” of which a CNN is just one generic form of such.
THE SEARCH FOR AN INVENTIVE CONCEPT MPEP 2106.05(I)
The second part of the Alice/Mayo test is often referred to as a search for an inventive concept. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 217, 110 USPQ2d 1976, 1981 (2014) (citing Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 71-72, 101 USPQ2d 1961, 1966 (2012)).
An inventive concept "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself." Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016). See also Alice Corp., 573 U.S. at 21-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 78, 101 USPQ2d at 1968 (after determining that a claim is directed to a judicial exception, "we then ask, ‘[w]hat else is there in the claims before us?") (emphasis added)); RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract"). Instead, an "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception itself. Alice Corp., 573 U.S. at 27-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966).
Claim 2 recites:
wherein the organizational units are rows or columns in a table of the input data structure.
Step 2A Prong One
Claim 2’s limitation “wherein the organizational units are rows or columns in a table of the input data structure” recites an abstract idea as a mental process. “Wherein the organizational units are rows or columns in a table of the input data structure” may be an observation as a mental process, in the human mind.
Step 2A Prong 2
Claim 2, as a whole, fails to integrate the recited judicial exception into a practical application of the exception. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Step 2B
Claim 2 does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 3 recites:
wherein the classifiable data is broken into a plurality of characters used for the character encoding.
Step 2A Prong One
Claim 3’s limitation “wherein the classifiable data is broken into a plurality of characters used for the character encoding” recites an abstract idea as a mental process. “Wherein the classifiable data is broken into a plurality of characters used for the character encoding” may be an observation as a mental process, in the human mind.
Step 2A Prong 2
Claim 3, as a whole, fails to integrate the recited judicial exception into a practical application of the exception. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Step 2B
Claim 3 does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 4 recites:
wherein the CNN is configured to perform convolutions around the plurality of organizational units.
Step 2A Prong One
Claim 4’s limitation “wherein the CNN is configured to perform convolutions around the plurality of organizational units” recites an abstract idea as a mental process. “wherein the CNN is configured to perform convolutions around the plurality of organizational units” may be an observation as a mental process, in the human mind.
Step 2A Prong 2
Claim 4, as a whole, fails to integrate the recited judicial exception into a practical application of the exception. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Step 2B
Claim 4 does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 5 recites:
wherein the CNN is configured as a conditional random field (CRF).
The limitation of “wherein the CNN is configured as a conditional random field (CRF)” appears to be mere instructions to apply this on a computer as a generic CNN.
The mere recitation wherein the CNN is configured as a conditional random field is merely applying this on a generic computer component and/or linking to the field of use. As stated in the spec in [0008] and [0042] “various types of classifiers may be used” of which a CNN is just one generic form of such.
Therefore, the claim does not recite limitations which amount to significantly more than the abstract idea.
Claim 6 recites:
wherein accessing the classifiable data includes breaking the plurality of organizational units into chunks of a predetermined size.
Step 2A Prong One
Claim 6 limitation “wherein accessing the classifiable data includes breaking the plurality of organizational units into chunks of a predetermined size” recite mathematical concepts although they are not expressed in mathematical symbols.
Step 2A Prong 2
Claim 6, as a whole, fails to integrate the recited judicial exception into a practical application of the exception. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Step 2B
Claim 6 does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 7 recites:
access classifiable data from a first organizational unit of a plurality of organizational units of an input data structure;
generate a character encoding of the classifiable data;
provide the character encoding to a classifier configured to: filter at least a portion of the classifiable data; and
perform a character-level classification on the character encoding.
Step 2A Prong One
Claim 7 limitations of “generate a character encoding…” “provide the character encoding to a classifier configured to filter at least a portion of the classifiable data,” and “perform a character level classification on the character encoding” recite abstract ideas as mathematical concepts or mental processes. “Generating a character encoding” and “provide the character encoding to a classifier configured to filter at least a portion of the classifiable data” recite mathematical concepts although they are not expressed in mathematical symbols. “Perform a character level classification on the character encoding” may be done as a mental process, in the human mind.
Step 2A Prong 2
Claim 7, as a whole, fails to integrate the recited judicial exception into a practical application of the exception.
The limitation of “access classifiable data from a first organizational unit of a plurality of organizational units of an input data structure” appears to be mere data gathering and “Perform a character level classification on the character encoding” appears to be mere instructions to apply this on a computer as a generic CNN.
The mere recitation of the “classification” and “accessing” using a CNN is merely applying this on a generic computer component and/or linking to the field of use. As stated in the spec in [0008] and [0042] “various types of classifiers may be used” of which a CNN is just one generic form of such.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitation of “access classifiable data from a first organizational unit of a plurality of organizational units of an input data structure” appears to be mere data gathering and “provide the character encoding to a classifier configured to: filter at least a portion of the classifiable data” appears to be mere instructions to apply this on a computer as a generic CNN.
The mere recitation of the “classification” and “determining” using a CNN is merely applying this on a generic computer component and/or linking to the field of use. As stated in the spec in [0008] and [0042] “various types of classifiers may be used” of which a CNN is just one generic form of such.
Claim 8 recites:
wherein the processing circuit is further caused to determine at least one label for the first organizational unit based on the character-level classification.
Step 2A Prong One
Claim 8 limitation of “wherein the processing circuit is further caused to determine at least one label for the first organizational unit based on the character-level classification” may be done as a mental process, in the human mind.
Step 2A Prong 2
Claim 8, as a whole, fails to integrate the recited judicial exception into a practical application of the exception. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Step 2B
Claim 8 does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 9 recites:
wherein the classifier comprises a convolutional block comprising a filter or input lens to be applied to at the classifiable data.
Step 2A Prong One
Claim 9’s limitation “wherein the CNN is configured to perform convolutions around the plurality of organizational units” recites an abstract idea as a mental process. “wherein the CNN is configured to perform convolutions around the plurality of organizational units” may be an observation as a mental process, in the human mind.
Step 2A Prong 2
Claim 9, as a whole, fails to integrate the recited judicial exception into a practical application of the exception. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Step 2B
Claim 9 does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 10 recites:
wherein the classifier comprises a convolutional neural network (CNN).
The limitation of “wherein the CNN is configured as a conditional random field (CRF)” appears to be mere instructions to apply this on a computer as a generic CNN.
The mere recitation wherein the CNN is configured as a conditional random field is merely applying this on a generic computer component and/or linking to the field of use. As stated in the spec in [0008] and [0042] “various types of classifiers may be used” of which a CNN is just one generic form of such.
Therefore, the claim does not recite limitations which amount to significantly more than the abstract idea.
Claim 11 recites:
wherein the CNN is configured to perform convolutions around the plurality of organizational units.
Step 2A Prong One
Claim 11’s limitation “wherein the CNN is configured to perform convolutions around the plurality of organizational units” recites an abstract idea as a mental process. “wherein the CNN is configured to perform convolutions around the plurality of organizational units” may be an observation as a mental process, in the human mind.
Step 2A Prong 2
Claim 11, as a whole, fails to integrate the recited judicial exception into a practical application of the exception. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Step 2B
Claim 11 does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 12 recites:
wherein the CNN is configured as a conditional random field (CRF).
The limitation of “wherein the CNN is configured as a conditional random field (CRF)” appears to be mere instructions to apply this on a computer as a generic CNN.
The mere recitation wherein the CNN is configured as a conditional random field is merely applying this on a generic computer component and/or linking to the field of use. As stated in the spec in [0008] and [0042] “various types of classifiers may be used” of which a CNN is just one generic form of such.
Therefore, the claim does not recite limitations which amount to significantly more than the abstract idea.
Claim 13 recites:
wherein filtering at least a portion of the classifiable data includes the processing circuit being caused to redact or mask the portion of the classifiable data.
Step 2A Prong One
Claim 13’s limitation “wherein filtering at least a portion of the classifiable data includes the processing circuit being caused to redact or mask the portion of the classifiable data” may be an observation as a mental process, in the human mind.
Step 2A Prong 2
Claim 13, as a whole, fails to integrate the recited judicial exception into a practical application of the exception. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Step 2B
Claim 13 does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 14 recites:
wherein the organizational units are rows or columns in a table of the input data structure.
Step 2A Prong One
Claim 14’s limitation “wherein the organizational units are rows or columns in a table of the input data structure” recites an abstract idea as a mental process. “Wherein the organizational units are rows or columns in a table of the input data structure” may be an observation as a mental process, in the human mind.
Step 2A Prong 2
Claim 14, as a whole, fails to integrate the recited judicial exception into a practical application of the exception. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Step 2B
Claim 14 does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 15 recites:
receive classifiable data from a first organizational unit of a plurality of organizational units of an input data structure;
sample the classifiable data to generate a character encoding of the classifiable data;
provide the character encoding to a classifier configured to perform a character-level classification on the character encoding;
determine at least one label for the first organizational unit based on the character-level classification, wherein the classifier comprises a convolutional neural network (CNN).
Step 2A Prong One
Claim 15 limitations of “sample the classifiable data to generate a character encoding…” and “determining at least one label…” recite abstract ideas as mathematical concepts or mental processes. “Sample the classifiable data to generate a character encoding” recites a mathematical concept although it is not expressed in mathematical symbols. “Determining at least one label” may be done as a mental process, in the human mind.
Step 2A Prong 2
Claim 15, as a whole, fails to integrate the recited judicial exception into a practical application of the exception.
The limitation of “retrieve classifiable data from a first organizational unit of a plurality of organizational units of an input data structure” appears to be mere data gathering and “provide the character encoding…” appears to be mere instructions to apply this on a computer as a generic CNN.
The mere recitation of the “classification” and “retrieving” using a CNN is merely applying this on a generic computer component and/or linking to the field of use. As stated in the spec in [0008] and [0042] “various types of classifiers may be used” of which a CNN is just one generic form of such.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitation of “retrieve classifiable data from a first organizational unit of a plurality of organizational units of an input data structure” appears to be mere data gathering and “provide the character encoding…” appears to be mere instructions to apply this on a computer as a generic CNN.
The mere recitation of the “classification” and “retrieving” using a CNN is merely applying this on a generic computer component and/or linking to the field of use. As stated in the spec in [0008] and [0042] “various types of classifiers may be used” of which a CNN is just one generic form of such.
Claim 16 recites:
wherein the organizational units are rows or columns in a table of the input data structure.
Step 2A Prong One
Claim 16’s limitation “wherein the organizational units are rows or columns in a table of the input data structure” recites an abstract idea as a mental process. “Wherein the organizational units are rows or columns in a table of the input data structure” may be an observation as a mental process, in the human mind.
Step 2A Prong 2
Claim 16, as a whole, fails to integrate the recited judicial exception into a practical application of the exception. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Step 2B
Claim 16 does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 17 recites:
wherein the classifiable data is broken into a plurality of characters used for the character encoding.
Step 2A Prong One
Claim 17’s limitation “wherein the classifiable data is broken into a plurality of characters used for the character encoding” recites an abstract idea as a mental process. “Wherein the classifiable data is broken into a plurality of characters used for the character encoding” may be an observation as a mental process, in the human mind.
Step 2A Prong 2
Claim 17, as a whole, fails to integrate the recited judicial exception into a practical application of the exception. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Step 2B
Claim 17 does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 18 recites:
wherein the CNN is configured to perform convolutions around the plurality of organizational units.
Step 2A Prong One
Claim 18’s limitation “wherein the CNN is configured to perform convolutions around the plurality of organizational units” recites an abstract idea as a mental process. “wherein the CNN is configured to perform convolutions around the plurality of organizational units” may be an observation as a mental process, in the human mind.
Step 2A Prong 2
Claim 18, as a whole, fails to integrate the recited judicial exception into a practical application of the exception. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Step 2B
Claim 18 does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 19 recites:
wherein the CNN is configured as a conditional random field (CRF).
The limitation of “wherein the CNN is configured as a conditional random field (CRF)” appears to be mere instructions to apply this on a computer as a generic CNN.
The mere recitation wherein the CNN is configured as a conditional random field is merely applying this on a generic computer component and/or linking to the field of use. As stated in the spec in [0008] and [0042] “various types of classifiers may be used” of which a CNN is just one generic form of such.
Therefore, the claim does not recite limitations which amount to significantly more than the abstract idea.
Claim 20 recites:
wherein accessing the classifiable data includes breaking the plurality of organizational units into chunks of a predetermined size.
Step 2A Prong One
Claim 20 limitation “wherein accessing the classifiable data includes breaking the plurality of organizational units into chunks of a predetermined size” recite mathematical concepts although they are not expressed in mathematical symbols.
Step 2A Prong 2
Claim 20, as a whole, fails to integrate the recited judicial exception into a practical application of the exception. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Step 2B
Claim 20 does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
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, 3, 7, 8, 10, 15, and 17 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Mark Daniel McClusky et al. (U.S. Patent 11,392,651).
With respect to claim 1, McClusky teaches:
accessing classifiable data from a first organizational unit of a plurality of organizational units of an input data structure (see column 2, lines 19 - 30, where data is input and accessed from an organizational unit of a data storage (data in an organizational unit which is a database), also see column 8, lines 53 – 67, where the data is able to be classified);
generating a character encoding of the classifiable data (see column 8, lines 53 – 67, where a CNN is used to encode the word or character n-gram vectors and perform classification);
providing the character encoding to a classifier configured to perform a character-level classification on the character encoding (see column 8, lines 53 – 67, where a CNN is used to encode the word or character n-gram vectors and perform classification);
determining at least one label for the first organizational unit based on the character-level classification (see column 10, lines 47 – 59, where a label may be assigned based on the classification),
wherein the classifier comprises a convolutional neural network (CNN) (see column 8, lines 53 – 67, where a CNN is used to encode the word or character n-gram vectors and perform classification).
With respect to claim 3, McClusky teaches:
wherein the classifiable data is broken into a plurality of characters used for the character encoding (see column 8, lines 53 – 67, where a CNN is used to encode the word or character n-gram vectors at the character level and perform classification).
With respect to claim 7, McClusky teaches:
access classifiable data from a first organizational unit of a plurality of organizational units of an input data structure (see column, where data is input as an organizational unit of a data storage device, also see column 8, lines 53 – 67, where a CNN is used to encode the word or character n-gram data vectors and perform classification on the data);
generate a character encoding of the classifiable data (see column 8, lines 53 – 67, where a CNN is used to encode the word or character n-gram vectors and perform classification);
provide the character encoding to a classifier configured to: filter at least a portion of the classifiable data (see column 8, lines 53 – 67, where a CNN is used to encode the word or character n-gram vectors and perform classification); and
perform a character-level classification on the character encoding (see column 8, lines 53 – 67, where a CNN is used to encode the word or character n-gram vectors and perform classification).
With respect to claim 8, McClusky teaches:
wherein the processing circuit is further caused to determine at least one label for the first organizational unit based on the character-level classification (see column 10, lines 47 – 59, where a label may be assigned based on the classification).
With respect to claim 10, McClusky teaches:
wherein the classifier comprises a convolutional neural network (CNN) (see column 8, lines 53 – 67, where a CNN is used to encode the word or character n-gram vectors and perform classification).
With respect to claim 15, McClusky teaches:
receive classifiable data from a first organizational unit of a plurality of organizational units of an input data structure (see column, where data is input as an organizational unit of a data storage device, also see column 8, lines 53 – 67, where a CNN is used to encode the word or character n-gram data vectors and perform classification on the data);
sample the classifiable data to generate a character encoding of the classifiable data (see column 8, lines 53 – 67, where a CNN is used to encode the word or character n-gram vectors and perform classification);
provide the character encoding to a classifier configured to perform a character-level classification on the character encoding (see column 8, lines 53 – 67, where a CNN is used to encode the word or character n-gram vectors and perform classification);
determine at least one label for the first organizational unit based on the character-level classification, wherein the classifier comprises a convolutional neural network (CNN) (see column 10, lines 47 – 59, where a label may be assigned based on the classification).
With respect to claim 17, McClusky teaches:
wherein the classifiable data is broken into a plurality of characters used for the character encoding (see column 8, lines 53 – 67, where a CNN is used to encode the word or character n-gram vectors at the character level and perform classification).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Christian Reisswig teaches data driven structure extraction from text documents (U.S. Patent 12,204,860).
Reza Ghaeini et al. disclose semi supervised classification with stacked autoencoder (U.S. Patent 11,544,529).
Peng Ji et al. disclose classifying images in overlapping groups of images using convolutional neural networks (U.S. Patent Publication 20210158094).
Mark Daniel McClusky et al. teach user interface for identifying unmet technical needs and or technical problems (U.S. Patent 11,762,916).
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXANDRIA Y BROMELL whose telephone number is (571)270-3034. The examiner can normally be reached M-F 8-4.
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/ALEXANDRIA Y BROMELL/ Primary Examiner, Art Unit 2156 May 1, 2026
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