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 Objections
Claims 6 and 16 are objected to because of the following informalities: The claims recite “report data corresponds a risk” in line 1. This appears to be a typographical error (for example, --report data corresponds to a risk--). Appropriate correction is required.
Claim 20 is objected to because of the following informalities: Claim 20 depends from Claim 1 and is identical to Claim 10 (which also depends from Claim 1). Based on the claim set, it appears that this may be a typographical error, and that Claim 20 is intended to depend from Claim 11. Appropriate correction is required.
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.
Step 1:
The claims are directed to a process (method as introduced in Claim 1) and/or system (Claim 11), thus Claims 1-20 fall within one of the four statutory categories. See MPEP 2106.03.
Step 2A, Prong 1:
The claimed invention recites an abstract idea according to MPEP §2106.04. The independent claims which recite the following claim limitations as an abstract idea, are underlined below.
Claims 1 and 11 recite (as represented by the language of Claim 1):
receiving, using a communication device, an algorithm1 data from a user device, wherein the user device is associated with a user, wherein the user is associated with an organization;
analyzing, using a processing device, the algorithm data, wherein the algorithm data comprises each of an algorithmic data corresponding to an algorithm and an organization data corresponding to the organization, wherein the algorithm is utilized by the organization;
generating, using the processing device, an algorithm report data based on the analyzing, wherein the algorithm report data corresponds to a report of the algorithm; and
transmitting, using the communication device, the algorithm report data to the user device.
1 The algorithm data, as claimed, is merely data from/for any type of algorithm (set of rules, instructions, etc.). There is no indication that creation or use of the algorithm data (or the algorithm with which it is associated) would be performed with any computer-based implementation (including, but not limited to, machine learning or artificial intelligence processing).
The underlined claim limitations as emphasized above, as drafted, recite a process that, under its broadest reasonable interpretation, covers concepts performed in the human mind (including an observation, evaluation, judgment, opinion) in the form of analyzing the performance of (or use of or other characteristics of) an algorithm and reporting the results. Other than reciting a computer implementation, nothing in the claim elements precludes the step from encompassing the performance of concepts performed in the human mind which represents the abstract idea of mental processes. But for the recitation of generic implementation of computer system components, the claimed invention merely recites a process for analyzing an algorithm and reporting the results which could be performed in the human mind or by using pen and paper. For example, a user could review an algorithm (based on criteria such as the algorithm’s performance, how much the algorithm is used, the algorithm’s impact on the organization, or any other relevant criteria/characteristics) and then provide a report of their findings to the user and/or organization associated with the analyzed algorithm.
Step 2A, Prong 2:
This judicial exception is not integrated into a practical application. In particular, the claims recite additional elements such as:
a system, including:
a communication device (for receiving and transmitting data);
a user device (for receiving and transmitting data); and
a processing device (for analyzing data and generating reports).
In particular, the additional elements cited above beyond the abstract idea are recited at a high-level of generality and simply equivalent to a generic recitation and basic functionality that amount to no more than mere instructions to apply the judicial exception using generic computer technology components.
Accordingly, since the specification describes the additional elements in general terms, without describing the particulars, the additional elements may be broadly but reasonably construed as generic computing components being used to perform the judicial exception (see specification at page 14, line 13, “…embodiments of the disclosure may be practiced within a general-purpose computer or in any other circuits or systems…”; page 6, line 10, “Examples of the one or more client devices and/or the server computer may include, a desktop computer, a laptop computer, a tablet computer, a personal digital assistant, a portable electronic device, a wearable computer, a smart phone…”2). These claimed additional elements merely recite the words “apply it" (or an equivalent) with the judicial exception, or merely include 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).
Thus, the additional claim elements are not indicative of integration into a practical application, because the claims do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e)). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea and the claims are directed to an abstract idea.
2 It is also noted that throughout the specification, there are many places were various computer/system
components are described in a generic or general-purpose manner and/or using generic or general-purpose
examples.
Step 2B:
The claims do not include additional elements, individually or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept at Step 2B. Thus, the claim is not patent eligible.
Dependent Claims:
Claims 2-10 and 12-20 recite further elements related to the analysis and reporting steps of the parent claims. These activities fail to differentiate the claims from the related activities in the parent claims and fail to provide any material to render the claimed invention to be significantly more than the identified abstract ideas, as outlined below.
Claims 2 and 12 recite “wherein the algorithm is utilized to perform a task associated with a transaction, wherein the analyzing of the algorithm data comprises identifying a transaction data, wherein the transaction data corresponds to the transaction, wherein the algorithmic data comprises the transaction data”, which further specifies types of data to be analyzed, but does not lead toward eligibility. The types of data to be analyzed is part of the abstract idea and does not integrate the abstract idea into a practical application or provide an inventive concept.
Claims 3 and 13 recite “wherein the analyzing of the algorithm data further comprises identifying an algorithm output data, wherein the algorithm output data corresponds to an output delivered by the algorithm, wherein the algorithmic data further comprises an algorithm output data”, which further specifies types of data to be analyzed, but does not lead toward eligibility. The types of data to be analyzed is part of the abstract idea and does not integrate the abstract idea into a practical application or provide an inventive concept.
Claims 4 and 14 recite “wherein the task corresponds to a decision-making process, wherein the algorithm output data corresponds to a decision”, which further specifies types of data to be analyzed, but does not lead toward eligibility. The types of data to be analyzed is part of the abstract idea and does not integrate the abstract idea into a practical application or provide an inventive concept.
Claims 5 and 15 recite “identifying, using the processing device, a regulatory data based on the analyzing, wherein the regulatory data corresponds to a regulatory requirement, wherein the algorithm is associated with the regulatory requirement, wherein the generating of the algorithm report data is further based on the regulatory data”, which further specifies additional types of data to be used in the analysis, but does not lead toward eligibility. The additional types of data to be used in the analysis is part of the abstract idea. Specifying that it is identified using the processing device does not integrate the abstract idea into a practical application or provide an inventive concept.
Claims 6 and 16 recite “wherein the algorithm report data corresponds a risk associated with the algorithm, wherein the algorithm report data further represents a department associated with the risk, wherein the organization comprises a plurality of departments, wherein the organization data represents the plurality of departments”, which further specifies types of data to be analyzed, but does not lead toward eligibility. The types of data to be analyzed is part of the abstract idea and does not integrate the abstract idea into a practical application or provide an inventive concept.
Claims 7 and 17 recite “wherein the algorithm report data comprises an insight for a decision-making process, wherein the organization is associated with the decision-making process”, which further specifies types of data to be analyzed, but does not lead toward eligibility. The types of data to be analyzed is part of the abstract idea and does not integrate the abstract idea into a practical application or provide an inventive concept.
Claims 8 and 18 recite “receiving, using the communication device, a new regulatory data from the user device, wherein the new regulatory data corresponds to a new regulatory requirement, wherein the generating of the algorithm report data is further based on the new regulatory data”, which further specifies additional types of data to be used in the analysis, but does not lead toward eligibility. The additional types of data to be used in the analysis is part of the abstract idea. Specifying that it is received using the communication device does not integrate the abstract idea into a practical application or provide an inventive concept.
Claims 9 and 19 recite “wherein the report represents a level of compliance of the algorithm with the new regulatory requirement”, which further specifies types of data in the report, but does not lead toward eligibility. The types of in the report is part of the abstract idea and does not integrate the abstract idea into a practical application or provide an inventive concept.
Claims 10 and 20 recite “wherein the generating is further based on a machine learning model”. The machine learning model is recited at a high-level of generality and simply equivalent to a generic recitation and basic functionality that amount to no more than mere instructions to apply the judicial exception using generic computer technology components. The machine learning model is merely recited as a generic tool for generating a report.
The claims do not provide any new additional limitations or meaningful limits beyond abstract idea that are not addressed above in the independent claims therefore, they do not integrate the abstract idea into a practical application nor do they provide significantly more to the abstract idea. Thus, after considering all claim elements, both individually and as a whole, it has been determined that the claims do not integrate the judicial exception into a practical application or provide an inventive concept. Therefore, Claims 2-10 and 12-20 are ineligible.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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.
Claim(s) 1-5, 7-9, 11-15, and 17-19 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Hahn-Carlson et al. (Pub. No. US 2005/0274792 A1).
In regards to Claims 1 and 11, Hahn-Carlson discloses:
receiving, using a communication device, an algorithm data from a user device, wherein the user device is associated with a user, wherein the user is associated with an organization; ([0049], expense-tracking processor uses rules and transaction data to associate tracked expense data with a tracking parameter, this process represents an algorithm; [0050], transaction data (algorithm data used in the expense-tracking algorithm) is received by a transaction party (transaction parties include organizations, such as a business entity, see [0053]); [0033], algorithm data (transaction related information) can be provided by any involved entity (for example, a seller, i.e. business entity) via user-interface tools (which would represent user devices associated with the entity); [0039], the nodes (user-interface tools) are used by (associated with) users to send data, the nodes/user-interfaces and the expense-tracking processor are both capable of sending and receiving data and therefore represent communication devices)
analyzing, using a processing device, the algorithm data, wherein the algorithm data comprises each of an algorithmic data corresponding to an algorithm and an organization data corresponding to the organization, wherein the algorithm is utilized by the organization; ([0048]; [0051]; [0053]; [0054], the auditing processor (analyzing, using a processing device) analyses the algorithm data from the expense-tracking algorithm, the algorithm data analyzed includes algorithmic data (the transaction/expense data used in the algorithm) and organization data (the audits are performed on data for specific entities/organizations, additionally, the auditor requesting data for a specific entity and/or being granted access to a specific entities data further indicates the use of organization data corresponding to an organizations (see also, [0055], profiles used to identify and track results for each entity); [0050], the algorithm (expense-tracking) is utilized by the organization (business entity))
generating, using the processing device, an algorithm report data based on the analyzing, wherein the algorithm report data corresponds to a report of the algorithm; ([0056], the processing device (auditing processor), generates an algorithm report data based on the analysis of the expense-tracking algorithm (the information generated by the auditor (output) is used further to generate a report regarding the analysis of the expense-tracking algorithm, indicating that the auditor output corresponds to a report and represents “algorithm report data”)
transmitting, using the communication device, the algorithm report data to the user device ([0056], a report (that is generated with algorithm report data) is sent to the business entity (which communicate using the user devices (user-interface tools) described above))
In regards to Claims 2 and 12, Hahn-Carlson discloses:
wherein the algorithm is utilized to perform a task associated with a transaction, wherein the analyzing of the algorithm data comprises identifying a transaction data, wherein the transaction data corresponds to the transaction, wherein the algorithmic data comprises the transaction data ([0050], the algorithm (expense-tracking) is used to perform a task associated with a transaction (“…processes transaction expense data on an active transaction-by-transaction basis, utilizing incoming transaction documents to automatically classify, associate or otherwise process transaction expense data on behalf of a user …”); [0051], the analysis performed on the algorithm (expense-tracking) includes analyzing the expense data that was classified by the algorithm (identifies expense data that is part of transaction data corresponding to a transaction, the transaction data being algorithmic data corresponding to use of algorithm))
In regards to Claims 3 and 13, Hahn-Carlson discloses:
wherein the analyzing of the algorithm data further comprises identifying an algorithm output data, wherein the algorithm output data corresponds to an output delivered by the algorithm, wherein the algorithmic data further comprises an algorithm output data ([0051], the output delivered by the algorithm (expense-tracking) includes expense classifications, the analyzing of the algorithm by the auditing processor audits the outputs and identifies classifications in conjunction with expense data for compliance, performance, etc. (the delivered output data being algorithmic data corresponding to use of algorithm))
In regards to Claims 4 and 14, Hahn-Carlson discloses:
wherein the task corresponds to a decision-making process, wherein the algorithm output data corresponds to a decision ([0050]; etc., the algorithm (expense-tracking) is used to perform a task associated with a transaction, such as classifying expense data related to the transaction using rules (see [0049]), which represents a decision-making process which makes decisions (classifications for expenses))
In regards to Claims 5 and 15, Hahn-Carlson discloses:
identifying, using the processing device, a regulatory data based on the analyzing, wherein the regulatory data corresponds to a regulatory requirement, wherein the algorithm is associated with the regulatory requirement, wherein the generating of the algorithm report data is further based on the regulatory data ([0051]; [0055]; [0094], the auditing/analyzing includes identifying regulatory rules/requirements (regulatory data) that apply to the algorithm being analyzed (different entities can be subject to different regulatory requirements, such as different business types), the auditor output (“algorithm report data”, as described above) is generated based on regulatory-type compliance rules used to determine if the expense data is properly classified)
In regards to Claims 7 and 17, Hahn-Carlson discloses:
wherein the algorithm report data comprises an insight for a decision-making process, wherein the organization is associated with the decision-making process ([0051]; [0056], the auditor is performed to audit the transaction/expanse data and classifications to make a determination regarding whether the expense data is properly classified in regards to regulation/compliance rules, therefore this output would include whether or not classification are correct or in error, one of ordinary skill in the art would recognize that this data provides an “insight” into the decision making process (for example, it would provide insights regarding any classification rules that are causing errors or non-compliance))
In regards to Claims 8 and 18, Hahn-Carlson discloses
identifying, using the communication device, a new regulatory data from the user device, wherein the new regulatory data corresponds to a new regulatory requirement, wherein the generating of the algorithm report data is further based on the new regulatory data ([0051], “These compliance rules 142 are updated to reflect regulatory changes, and can be tailored to each particular company audited.”, these updated requirements would be used in the same manner as previous requirements for generating the auditor output (“algorithm report data”, see also [0055]; [0094]); [0090], “…compliance rules 504 provided by users (e.g., transaction parties…”, transaction parties include business entities (organizations))
In regards to Claims 9 and 19, Hahn-Carlson discloses:
wherein the report represents a level of compliance of the algorithm with the new regulatory requirement ([0051]; [0055] ; [0090]; [0094], these updated requirements would be used in the same manner as previous requirements for generating the auditor output (“algorithm report data”), therefore any reports generated based off of the compliance determination using the auditor that has updated regulatory compliance requirements would include a level of compliance of the algorithm with the new regulatory requirement that have already been updated; [0051]; [0056], the auditor is performed to audit the transaction/expanse data and classifications to make a determination regarding whether the expense data is properly classified in regards to regulation/compliance rules, therefore this output would include whether or not classifications are correct (compliant) or in error (non-complaint), one of ordinary skill in the art, under broadest reasonable interpretation, would recognize that this data provides a “level of compliance“, the levels including at least “compliant” or “non-compliant”)
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 6, 10, 16, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hahn-Carlson in view of Drangmeister et al. (US 2020/0394722 A1).
In regards to Claims 6 and 16, Hahn-Carlson discloses the above system/method for generating algorithm report data based on analyzing an algorithm. Hahn-Carlson does not explicitly disclose, but Drangmeister teaches:
wherein the algorithm report data corresponds [to] a risk associated with the algorithm, wherein the algorithm report data further represents a department associated with the risk, wherein the organization comprises a plurality of departments, wherein the organization data represents the plurality of departments (algorithms, machine learning models, etc. that are used to classify transaction data (including expense data, see [0086], “ Information for which codes are assigned include, but are not limited to, (a) accounting categories such as asset, liability, equity, income, expense,…”); [0088], a materiality factor is assigned to each transaction, materiality factors can include ”…likelihood that any given transaction coding is incorrect based on statistical models and historical transaction data for a particular client, for all clients with similar business models, and for all ARASP clients… whether incorrectly recording the transaction would subject the client to relatively higher level of financial or legal risk.”; [0090], examples of coding/classification errors; [0091], “Transactions with coding data or meta data that indicate a high prevalence of errors would be subjected to additional verification in step…”, this can include using materiality to determine scores or flag items for review (output of the algorithm review process representing “algorithm report data”); [0088], the determined risk can be associated with a department, such as risk of misleading investors (representing an external department), “…materiality enquires whether sophisticated investors would be misled if the transactional amount was omitted or misclassified. If sophisticated investors would be misled or would have likely made a different financial decision because of their reliance on a particular transaction, the transactional amount is considered to be material.”, the organization has multiple departments, including external (investors), and internal (sales clerks, accountants and bookkeepers, CFOs, CEO’s, line managers, etc. represent different departments within an organization, see [0089], [0091]))
It would have been obvious to one of ordinary skill in the art, before to the effective filing date of the claimed invention, to have further modified the system of Hahn-Carlson so as to have included wherein the algorithm report data corresponds to a risk associated with the algorithm, wherein the algorithm report data further represents a department associated with the risk, wherein the organization comprises a plurality of departments, wherein the organization data represents the plurality of departments, as taught by Drangmeister.
Hahn-Carlson discloses a “base” method/system in which financial transaction information coding is audited to verify that the data is correctly coded to comply with regulations, as shown above. Drangmeister also teaches a comparable method/system in which financial transaction information coding is audited to verify that the data is correctly coded, as shown above. Drangmeister also teaches an embodiment in which the review of the coding can additionally make determinations regarding potential risks associated with one or more departments based on coding errors, as shown above. One of ordinary skill in the art would have recognized the adaptation of wherein the algorithm report data corresponds to a risk associated with the algorithm, wherein the algorithm report data further represents a department associated with the risk, wherein the organization comprises a plurality of departments, wherein the organization data represents the plurality of departments to Hahn-Carlson could be performed with the technical expertise demonstrated in the applied references. (See KSR [127 S Ct. at 1739] "The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results."). One of ordinary skill in the art would understand how to add the additional steps of determining risks associated with improperly coded transaction data in Drangmeister to the process for determining if codes are misclassified in Hahn-Carlson.
In regards to Claims 10 and 20, Hahn-Carlson discloses the above system/method for generating algorithm report data. Hahn-Carlson does not explicitly disclose, but Drangmeister teaches:
wherein the generating is further based on a machine learning model ([0091], machine learning is used in the materiality scores, that are related to the determination of risk, as described above, to be provided to users for review (algorithm report data), “..in combination with the AI/ML module 704 disclosed herein assist auditors and management (of the client) with assessing materiality, qualitative areas of interest, transaction accuracy and identified omissions by calculating a statistical “batting score’ or “Materiality Adjusted Coding Coefficient” …”)
It would have been obvious to one of ordinary skill in the art, before to the effective filing date of the claimed invention, to have further modified the system of Hahn-Carlson so as to have included wherein the generating is further based on a machine learning model, as taught by Drangmeister.
Hahn-Carlson discloses a “base” method/system in which financial transaction information coding is audited to verify that the data is correctly coded to comply with regulations, as shown above. Drangmeister also teaches a comparable method/system in which financial transaction information coding is audited to verify that the data is correctly coded, as shown above. Drangmeister also teaches an embodiment in which the output data of the algorithm analysis is generating based on a machine learning model, as shown above. One of ordinary skill in the art would have recognized the adaptation of wherein the generating is further based on a machine learning model to Hahn-Carlson could be performed with the technical expertise demonstrated in the applied references. (See KSR [127 S Ct. at 1739] "The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.")
Additional Prior Art Identified but not Relied Upon
Kendrick et al. (Pub. No. US 2015/0294312 A1). Discloses receiving algorithm data from a user device associated with a user/organization, analyzing the algorithm data, generating algorithm report data, and providing report data (see at least [0010]; [0032]; [0039]; [0040]; [0096]; [0105]; [0109]; [0124]; [0131]-[0133]; Claim 2).
Ramesh et al. (Pub. No. US 2021/0304204 A1). Discloses classifying transaction that includes flagging potential risk and reviewing the results of the machine learning model (algorithm) (see at least [0019]; [0025]).
Katz et al. (Pub. No. US 2010/0004981 A1). Discloses risk determination related to a department (branch) for financial data (see at least Abstract; [0013]; [0014]; [0018]; [0037]; [0040]; Claim 12).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAUN D SENSENIG whose telephone number is (571)270-5393. The examiner can normally be reached M-F: 10:00am-4:00pm.
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/S.D.S/
Examiner, Art Unit 3629
February 7, 2026
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626