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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103 is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Priority
Acknowledgment is made of applicant's claim for domestic priority based on US non-provisional application 17/694331 and 18/102579 filed on 03/14/2022 and 01/27/2023.
Nonstatutory Double Patent Rejections
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. See 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); and 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) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent is shown to be commonly owned with this application. See 37 CFR 1.130(b).
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/forms/. The filing date of the application will determine what form 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/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 are rejected under the judicially created doctrine of obviousness-type double patenting as being unpatentable over the claims of US 12293160 B2.
With respect to the independent claims, the limitations of claims 1+4+5 and 14 of US 12293160 B2 fully encompasses the limitations of claims 1 and 14 in the instant application.
Similarly, the limitations of claim 13 in view of claims 4-5 of US 12293160 B2 fully encompasses the limitations of claim 13 in the instant application.
With respect to the dependent claims, the limitations of claim 1 (“data object type”) of US 12293160 B2 fully encompasses the limitations of claims 2 and 15 in the instant application.
The limitations of claim 1 (“first data object score”) of US 12293160 B2 fully encompasses the limitations of claims 3 and 16 in the instant application.
The limitations of claim 12 (“wherein the determining the recency weight for each data entry is based on the time entry and the data entry vector representation that is based on both the text content and a data object type”) of US 12293160 B2 fully encompasses the limitations of claims 4 and 17 in the instant application.
The limitations of claim 9 (“wherein the first data object includes a title plant and each data entry of the first set of data entries includes a document label for a title document”) of US 12293160 B2 fully encompasses the limitations of claims 5 and 18 in the instant application.
The limitations of claim 1 (“first data object including a first set of data entries”) of US 12293160 B2 fully encompasses the limitations of claim 6 in the instant application.
The limitations of claim 1 (“each data entry of the first set of data entries includes text content associated with a time entry”) of US 12293160 B2 fully encompasses the limitations of claim 7 in the instant application.
The limitations of claim 1 (“vectorizing the selected text strings for each data entry to generate a data entry vector representation for each data entry”; i.e., tokens are text strings) of US 12293160 B2 fully encompasses the limitations of claim 8 in the instant application.
The limitations of claim 3 of US 12293160 B2 fully encompasses the limitations of claim 9 in the instant application.
The limitations of claim 12 (“wherein the determining the recency weight for each data entry is based on the time entry”) of US 12293160 B2 fully encompasses the limitations of claim 10 in the instant application.
The limitations of claims 4-5 of US 12293160 B2 fully encompasses the limitations of claims 11 and 19 in the instant application.
The limitations of claim 1 (“storing, by the computer system, the first data object in a database in response to determining that the first data object score satisfied the data object score condition”) of US 12293160 B2 fully encompasses the limitations of claims 12 and 20 in the instant application.
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 USC 101 as directing toward non-statutory subject matter.
Claim 1 recites a non-transitory computer-readable medium (i.e., manufacture). Claim 20 recites a method (i.e., a process). Reply to Decision on Appeal of June 8, 2021
To distinguish ineligible claims that merely recite a judicial exception from eligible claims that require an implementation of judicial exception, the Supreme Court uses a two-step framework: Step One (Step 2A), determine whether the claims at issue are directed to one of those patent-ineligible concepts; and Step Two (Step 2B), if so, ask “what else is there in the claims?” to determine whether the additional elements transform the nature of the claim into a patent eligible application. Alice Corp. Pty. Ltd. v. CLS Bank Int’l., 134 S. Ct. 2347, 2355 (2014).
Step One (Step 2A) is a two prong test that requires the determination of whether the claims at issue are directed to an enumerated patent ineligible concept. See MPEP 2106.04.
Step 2A Prong (1) requires the determination of the specific limitations in the claim under examination (individually or in combination) that the examiner believes recites an abstract idea and determining whether the identified limitations falls within the subject matter groupings of abstract ideas enumerated. See MPEP 2106.04(a).
The enumerated patent ineligible concepts comprising:
(a) Mathematical Concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
(b) Certain methods of organizing human activity – fundamental economic principles / practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules / instructions) and
(c) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion). See MPEP 2106.04(a).
If the claim recites an enumerated patent ineligible concept, then Prong (2) of Step One (Step 2A) requires the determination of whether the claim integrates the patent ineligible concept into a practical application. Individually and in combination, identifying whether there are any additional elements recited in the claim beyond the judicial exceptions and evaluating those additional elements to determine whether they integrate the exception into a practical application, using one or more of the considerations laid out by the Supreme Court and the Federal Circuit. See MPEP 2106.04(d).
Under Step 2B, if the claim does not integrate the ineligible concept into a practical application and therefore directed to a judicial exception, evaluate whether the claim provides an inventive concept by determining whether there are additional elements, individually and in ordered combination, amount to significantly more than the exception itself. See MPEP 2106.04.
Step 2A Prong (1)
The “directed to” inquiry does not ask whether the claims involve a patent ineligible concept but, considered in light of the specification, whether the claim as a whole is directed to excluded subject matter or directed to an improvement to computer functionality. Enfish L.L.C. v. Microsoft Corp., 822 F.3d 1327, 1335 (Fed. Cir. 2016).
Therefore, Prong (1) of Step 2A requires identifying specific limitations in the claims that recites (“describes” or “set forth”) an abstract idea and determine whether the identified limitations falls within the subject matter groupings of abstract ideas enumerated. See MPEP 2106.04 (“Thus, it is sufficient for this analysis for the examiner to identify that the claimed concept (the specific claim limitation(s) that the examiner believes may recite an exception) aligns with at least one judicial exception”).
Under Prong (1), Claim 1 recites a computing platform comprising:
at least one processor;
at least one non-transitory computer-readable medium; and
program instructions stored on the at least one non-transitory computer-readable medium that, when executed by the at least one processor, cause the computing platform to:
(1) obtain, from one or more data sources, a set of data entries that each comprises (i) respective text content that is at least partially unstructured and (ii) a respective time value indicating a time associated with the respective text content;
(2) process each respective data entry in the set of data entries by:
(2)(a) utilizing a vectorization technique to produce a respective vector representation of the respective text content of the respective data entry; and
(2)(b) utilizing a recency-weight function to produce a respective recency weight based on a recency of the respective time value of the respective data entry;
(3) input the respective vector representations and the respective recency weights that are produced for the set of data entries into a trained machine learning model and thereby cause the trained machine learning model to generate and output a prediction score based at least on the respective vector representations and the respective recency weights;
(4) evaluate whether the prediction score satisfies one or more conditions;
(5) based on the evaluation, make a determination of whether to proceed under either a high-risk path or a low-risk path; and
(6) perform one or more automated actions in accordance with the determination of whether to proceed under either the high-risk path or the low-risk path.
Claim 13 recites a non-transitory computer-readable medium, wherein the at least one non-transitory computer-readable medium is provisioned with program instructions that, when executed by at least one processor, cause a computing platform to implement (1)-(6).
Claim 14 recites a corresponding method corresponding to (1)-(6).
With respect to (1), individually and considered in light of the specification US 2025/0348688 A1 at ¶42: “at block 402, the data object management controller 305 may obtain a data object that includes a set of data entries. Each data entry of the set of data entries may include text content associated with a respective time entry. As such, the data object may be considered to include a time series of unstructured or partially un-structured entries, like natural language entries in an index of historical records”.
Therefore, (1) corresponds to collecting information, including when limited to particular content (e.g., natural language entries in an index of historical records) that does not change its character as information, are within the realm of abstract idea. Electric Power Grp., L.L.C. v. Alstom SA, 830 F.3d 1350, 1353 (Fed. Cir. 2016) (“we have treated collecting information, including when limited to particular content (which does not change its character as information), as within the realm of abstract idea”).
With respect to (2)(a), individually and considered in light of the specification US 2025/0348688 A1 at ¶51: “The data object management controller 305 may apply the vectorization algorithm to the set of data entries to obtain a data entry vector representation for each data entry” and at ¶52: “For example, the vectorization algorithm may include a Doc2Vec algorithm, a Sentence2Vec algorithm, a Word2Vec algorithm, a FastText algorithm, and/or any other tokenization/vectorization algorithm that would be apparent to one of skill in the art in possession of the present disclosure that may be used to generate a data entry vector representation of the data entries by first converting a portion of the text content (e.g., words, sentences) to vectors using the dictionary and averaging or aggregating those vectors representations to obtain a data entry vector representation for that data entry in the set of data entries”.
Similarly, with respect to (2)(b), individually and considered in light of the specification US 2025/0348688 A1 at ¶55: “the data object management controller 305 may determine the time that is associated with each data entry of the set of data entries. Based on the time associated with the data entry, a recency weight may be applied to the data entry vector representation. For example, for each data entry, a delta in the time of the data object or current date and the time associated with the data object may be determined. That delta may be applied to an exponential decay to transform the recency to a value between “0” and “1” where “1” indicates more recent and “0” indicates less recent. A constant from this transformation may be chosen by a standard machine learning optimization technique. For example, the equation:
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31
220
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”.
Further, with respect to (3), individually and considered in light of the specification US 2025/0348688 A1 at ¶56: “The method 500 may proceed to block 508 where a data object score is generated. In an embodiment, at block 508, the data object management controller 305 may generate a data object score. The data object score may be an aggregation of the data entry scores associated with the set of data entries. However, in various embodiments, the data object score may be generated by a machine learning model provided by the data object management controller 305. The machine learning model (e.g., a gradient boosting machine (GBM) model, a tree-based model, or other machine learning models) may include a mechanism for scoring the data entry scores from block 506, using parameters/weights learned during model training. The machine learning model instantiates a way to convert an n input vector (recency weighted data entry vectors also referred to as data entry scores) into a single predictive score…”.
Therefore, (2)-(3) correspond to analyzing information by mathematical algorithms that are essentially mental processes within the abstract idea category. Electric Power Grp., 830 F.3d at 1354 (“we have treated analyzing information by steps people go through in their minds, or by mathematical algorithms, without more, as essentially mental processes within the abstract-idea category”).
With respect to (4), individually and considered in light of the specification US 2025/0348688 A1 at ¶58: “Referring to FIG. 4, the method 400 may proceed from block 404 to block 406 where it is determined that the data object score satisfies a data object score condition. In an embodiment, at block 406, the data object management controller 305 may determine that the data object score satisfies a data object score condition. For example, the data object score may fall within a first range, a second range, a third range, or any other range of data object scores. The data object score condition may be associated with a condition-specific action”.
Similarly, with respect to (5)-(6), individually and considered in light of the specification US 2025/0348688 A1 at ¶60: “The method 400 may then proceed to block 408 where a condition-specific action associated with the data object score condition is performed. In an embodiment, at block 408, the data object management controller 305 may run instructions that are associated with the data object score condition that the data object score satisfies. For example, the condition-specific action associated with the data object score condition may include storing the data object in a database. In another example, the condition-specific action associated with the data object score condition may include generating a non-risk notification and providing that non-risk notification to an administrator or user via the user computing device 102/200. In yet another example, the condition-specific action associated with the data object score condition may include generating a risk notification and providing that risk notification (e.g., risk of a heart attack, risk of involuntary lien, risk of a computer component failure) to an administrator or user via the user computing device 102/200… Continuing with the example in FIG. 7, the action associated with the data objects 702 and 704 having a data object score that falls in the high-risk range that there is an involuntary lien on a property, the condition-specific action may include taking the “High Risk Path,” which may include having a manual underwriting procedure completed by an underwriter to determine whether there are any involuntary liens on the property. If the data object score fell in the low-risk range, the condition-specific action may include taking the “Low Risk Path,” which may include issuing a notification to automatically insure the parcel of property”.
Therefore, (4)-(5) correspond to analyzing information by steps people go through in their minds are essentially mental processes within the abstract idea category. Electric Power Grp., 830 F.3d at 1354 (“we have treated analyzing information by steps people go through in their minds, or by mathematical algorithms, without more, as essentially mental processes within the abstract-idea category”).
Finally, performing an automated action in accordance with the determinations of (5) amounted to “apply it”, with a computer.
In ordered combination, steps (1)-(6) amounted to collecting data, analyzing data by mathematical algorithm, and making evaluations / determinations of said mathematically analyzed data that are essentially mental processes. See also MPEP 2106.04(a)(2)IIIA (“a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind”).
Thus, claims 1, 13, and 14 described patent ineligible subject matter enumerated under category (c) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
Step 2A Prong (2).
Under Prong (2) of Step 2A, the goal is to determine whether the claim is directed to the recited exception by evaluating whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. See MPEP 2106.04II(A).
In particular, evaluating integration into a practical application requires identifying whether there are any additional elements recited in the claim beyond the judicial exception and evaluating those additional elements, individually and in combination, to determine whether they integrate the exception into a practical application, using one or more of the considerations laid out by the Supreme Court and the Federal Circuit (“CAFC”). See MPEP 2106.04(d).
The Supreme Court and the CAFC distinguished between (1) computer-functionality improvements from the (2) uses of existing computers as tools in aid of processes focused on abstract ideas. Electric Power Grp., L.L.C. v. Alstom SA, 830 F.3d 1350, 1354 (Fed. Cir. 2016) (“…we relied on the distinction made in Alice between, on one hand, computer-functionality improvement and, on the other, uses of existing computers as tools in aid of processes focused on “abstract ideas”…”).
In one example, the CAFC applied Alice inquiry to ask whether the focus of the claims is on the specific asserted improvement in computer capabilities (i.e., the self-referential table for a computer database) or instead, on a process that qualifies as an abstract idea for which computers are invoked merely as a tool. Enfish L.L.C. v. Microsoft Corp., 822 F.3d 1327, 1335-36 (Fed. Cir. 2016).
In Enfish, the claims were specifically directed to a self-referential table for a computer database. Id. at 1337. In particular, the claim language required a four step algorithm specifically directed to a self-referential table for a computer database that improves upon prior art information search and retrieval systems by employing a flexible, self-referential table to store data. Id. at 1336-37. CAFC determined that the plain focus of the claims was on an improvement to computer functionality itself (i.e., the self-referential table for a computer database), not on economic or other tasks for which a computer is used in its ordinary capacity. Id at 1335-36.
Therefore, the focus of the claims is on a specific asserted improvement in computer capabilities (i.e., the self-referential table for a computer database), not on economic or other tasks for which a computer is used in its ordinary capacity. Id. at 1336. See also MPEP 2106.04(d)I (“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)”).
On the other hand, with respect to a claim for detecting and automatically analyzing events on an interconnected electric power grid in real time over a wide area, the CAFC held that such claim clearly focused on a combination of abstract ideas comprising collecting information limited to particular content and analyzing information by mental steps or by mathematical algorithms. Electric Power Grp., 830 F.3d 1350 at 1354.
Specifically, the claims specified what information in the power-grid field it is desirable to gather, analyze, and display in “real time” but they did not include any requirement for performing the claimed functions of gathering, analyzing, and displaying in real time by use of anything but entirely conventional, generic technology such that the claims failed to state an inventive concept. Id. at 1356.
Further, a process for gathering and analyzing information of a specified content, then displaying the results is not a particular assertedly inventive technology for performing those functions. Id. Even though the claims required “displaying concurrent visualization” of two or more types of information that corresponded to time-synchronized display, the displays were anything but readily available. Id at 1355.
In other words, it is a case where selecting information for collection, analysis, and display by content or source that did nothing significant to differentiate a process from ordinary mental processes. Id. at 1355. The claims did not require an arguably inventive set of components or methods, did not invoke any assertedly inventive programming, and merely required the selection and manipulation of information to provide a “humanly comprehensible” amount of information useful for users that did not transform an otherwise abstract processes of information collection and analysis. Id.
Finally, the Supreme Court held that mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention. Alice, 134 S. Ct. at 2358. For example, in Alice, the Supreme Court held that data processing systems with data storage unit and transmission units were purely functional and generic and such recitation of hardware failed to offer any meaningful limitation beyond generally linking the use of a method to a particular technological environment. Id. at 2360. See MPEP 2106.04(d)I (“Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP 2106.05(h)”).
Individually, claims 1, 13, and 14 required a computer platform, one or more processors, memory, and non-transitory machine readable medium for performing the aforementioned analysis by mathematical algorithms in order to make prediction and evaluation that are essentially human mental processes.
The computer system, the processor, the memory, and the non-transitory machine readable medium required by the claims are akin to the recitation of purely functional and generic hardware (i.e., data processing system and data storage unit) in Alice that failed to offer any meaningful limitation beyond generally linking the use of a method to a particular technological environment.
As an ordered combination, steps (1)-(6) correspond to mathematical calculation of a prediction score (specification US 2025/0348688 A1 at ¶56: “The machine learning model instantiates a way to convert an n input vector (recency weighted data entry vectors also referred to as data entry scores) into a single predictive score”) to make evaluation and determination of high risk or low risk for display / notification.
Further, the claims do not use computer components (processors and non-transitory computer readable storage medium) to solve a technological problem or to improve an existing technological process. Instead of being directed toward a specific asserted improvement in computer capabilities such as the four step algorithm corresponding to a self-referential table for improving database search and retrieval in Enfish, the computer components and trained machine learning model (i.e., computer) were generic machinery invoked as tools to make determinations that are essentially mental processes.
Performing automated actions like sending a notification (dependent claims 11 and 19) are akin to the claims of Electric Power Grp. that specified what information in the power-grid field it is desirable to gather, analyze (i.e., calculate in the instant application), and display in “real time” but they do not include any requirement for performing the claimed functions of gathering, analyzing, and displaying in real time by use of anything but entirely conventional, generic technology (i.e., with processor and memory).
Even if the prediction yields desirable information regarding predication of whether a patient is likely to have a disease (specification US 2025/0348688 A1 at ¶60: “ the condition-specific action associated with the data object score condition may include generating a risk notification and providing that risk notification (e.g., risk of a heart attack, risk of involuntary lien, risk of a computer component failure) to an administrator or user via the user computing device 102/200”), the ordered combination amounts to collection and analysis of information similar to Electric Power Grp.
Therefore, as an ordered combination, claims 1, 13, and 14 do not integral abstract mental processes into a practical application and the claims are instead directed toward patent ineligible analysis by mathematical algorithm and mental predictions / evaluation.
Step 2B Inventive Concept.
The Guideline stated that if the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception, and requires further analysis under Step 2B where it may still be eligible if it amounts to an “inventive concept”. See MPEP 2106.04IIA and MPEP 2106.05.
Further, an inventive concept can be found in the non-conventional and non-generic arrangement of known conventional pieces. BASCOM Global Internet Servs. v. AT&T Mobility, 827, F3d 1341, 1350 (Fed. Cir. 2016).
In BASCOM, the CAFC held that filtering content is an abstract idea because it is a longstanding, well-known method of organizing human behavior similar to concepts previously found to be abstract. BASCOM, 827 F.3d at 1348. However, the CAFC determined that the claims did not merely recite filtering content along with the requirement to perform it on the internet or on a set of generic computer components, nor did the claims preempt all ways of filtering content on the internet. Id. at 1350.
Rather, the inventive concept described and claimed was the installation of a filtering tool at a specific location, remote from the end-users, with customizable filtering features specific to each end user that gives the filtering tool both the benefits of a filter on a local computer and the benefits of a filter on an internet service provider “ISP” server. Id. By taking a prior art filter solution (one size fits all filter at internet service provider “ISP” server) and making it more dynamic and efficient (providing individualized filtering at the ISP server), the claimed invention improves the performance of the computer system itself. Id. at 1351.
On the other hand, implementation via computers does not offer a meaningful limitation beyond generally linking the use of an abstract idea to a particular technological environment. Alice, 134 S. Ct. at 2360 (“Nearly every computer will include a “communications controller” and “data storage unit” capable of performing the basic calculation, storage, and transmission functions required by the method claims”). Intellectual Ventures I L.L.C. v. Capital One Bank, 792 F.3d 1363, 1370-71 (Fed. Cir. 2015) (“Steps that do nothing more than spell out what it means to “apply it on a computer” cannot confer patent-eligibility).
Similarly, limiting an abstract idea to one field of use do not convert otherwise ineligible concept into an inventive concept. Intellectual Ventures I L.L.C. v. Erie Indem. Co., 850 F.3d 1315, 1328 (Fed. Cir. 2017). Neither does adding computer functionality to increase the speed or efficiency of the process confer patent eligibility on an otherwise abstract idea. Intellectual Ventures I, 792 F.3d at 1367 (citing Bancorp Servs., LLC v. Sun Life Insurance Co. of Can., 687 F.3d 1266, 1278 (Fed. Cir. 2012) (“The fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter”)).
For example, in Intellectual Ventures I, the claims generally relates to customizing web page content as a function of navigation history and information known about the user via an interactive interface or a selectively tailored medium by which a web site user communicates with a web site information provider. Intellectual Ventures I., 792, F.3d at 1369. The CAFC held that the claim relates to an abstract concept of customizing information based on information known about the user and navigation history. Id.
Further, the claim provided no inventive concept to support patent eligibility because the interactive interface simply describes a generic web server with attendant software, tasked with providing web pages to and communicating with the user’s computer. Id. at 1370. Such required use of a software brain tasked with tailoring information and providing it to the user provides no additional limitation beyond applying an abstract idea, restricted to the internet, on a generic computer. Id. at 1371.
In the instant application, the individual recitation of computer, one or more processors, non-transitory machine readable storage medium in claims 1, 13, and 14 merely invoke generic machinery rather than focus on any particular technological device. Rather, the method and the apparatus for making analysis by mathematical algorithm in order to make essentially mental predictions did not offer a meaningful limitation beyond generally linking the use of an abstract idea to a conventional computer environment.
As an ordered combination, unlike BASCOM that described an unconventional combination to provide both the benefits of a filter on a conventional local computer and the benefits of a filter on the conventional ISP server, the utilization of generic processors and computer readable non-transitory storage media merely perform its conventional established function to use a computer as a tool for implementing analysis by mathematical algorithm in order to make essentially mental predictions.
Therefore, Claims 1, 11, 13, 14, and 19 do not supply an inventive concept.
Dependent claims 2-10, 12, 15-18, and 20 failed to integrate the abstract idea into a practical application or provide an inventive concept for the same reasons stated above because the dependent claims merely further defined the abstract steps of (1)-(6).
For the above reasons, Claims 1-20 are patent ineligible.
Claim Rejections - 35 USC § 103
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 103 that form the basis for the rejections under this section made 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 1-7 and 10-20 are rejected under 35 USC 103(a) as being unpatentable over Heinze et al. (US 2008/0256329 A1) in view of Cai et al. (US 2019/0349321 A1).
Regarding Claims 1, 13 and 14, Heinze discloses a computing platform (Figs. 1A-1B) comprising:
at least one processor (¶25, CPU 152);
at least one non-transitory computer-readable medium and program instructions stored on / provisioned within the at least one non-transitory computer-readable medium that, when executed by the at least one processor (¶25, storage device 162 with PROM, EPROM, and a hard drive; ¶26, program instructions written in C/C++/ASP/JAVA stored in PROM, EPROM, Flash), cause the computing platform to:
obtain, from one or more data sources (¶18, source data being analyzed), a set of data entries that each comprises (i) respective text content that is at least partially unstructured (¶21, natural language processing of free-text like physician notes input text data per ¶2) and (ii) a respective time value indicating a time associated with the respective text content (¶5, time or date information associated with source vector);
process each respective data entry in the set of data entries by:
utilizing a vectorization technique to produce a respective vector representation of the respective text content of the respective data entry (¶18, source vectors are vectors created from source data being analyzed by some means specific to the style or form of the data; ¶21, a parser creates source vectors from one or more source documents); and
utilizing a recency-weight function to produce a respective recency weight based on a recency of the respective time value of the respective data entry (¶41, determine a corresponding magnitude (i.e., weight per ¶18) based on date and time information presented in a source vector’s source text or the source text of the entire document from which the source vector is derived);
input the respective vector representations and the respective recency weights that are produced for the set of data entries into a trained machine learning model (¶32, vector processing application 112 implementing automated learning algorithms to perform vector comparison of source data vectors to target vectors; ¶36, determine whether a term that defines a target vector dimension is also present in the source vector under comparison to obtain a similarity measure by determining whether a test for a source vector feature (j) evaluates to true) and thereby cause the trained machine learning model to generate and output a prediction score (¶¶28-30, vector comparison between the source vector and the target vector according to equation (1) requires vector processing application 112 to determine x21 + x22 and y21 + y22+ y23 by aggregating the magnitudes (“weights”) of features in the source vector and the target vectors respectively to obtain the similarity measure) based at least on the respective vector representations and the respective recency weights (¶41, in a test for date and time, select magnitudes / weights for a target vector dimension based on the date and time information of the source vector to apply a high weight if date and time information defines and incident that occurred within a particular time span);
evaluate whether the prediction score satisfies one or more conditions (¶53, once a magnitude (i.e., weight) has been assigned to each dimension in both the source and target vectors, compare the source and target vectors where a perfect match yields a score of 1 and a comparison with no like terms between the source and target vectors yields a score of “0”); and
based on the evaluation, make a determination to perform one or more automated actions to proceed (¶¶28-30 in view of ¶2, quantify vector difference comparison by measuring the angle between two vectors (i.e., source data vector and target vectors) where source data vectors correspond to parsed items from input text data (e.g., physician notes) and target vectors are known vectors in a knowledge base (per ¶18) that represent descriptions of diagnoses and medical procedures to assign appropriate medical codes to the input text data).
Heinze does not disclose based on the evaluation, make a determination of whether to proceed under either a high-risk path or a low-risk path.
Cai teaches a machine learning computing platform (¶51, agent platform with natural language processor using machine learning prescriptive models to process IT incidents to generate a prescriptive solution) to output a prediction score based on vector representations of text content of a set of data entries (¶52, natural language processing in vector representations; ¶54, execute cosine similarity on new incident title / description against all historical incidents; ¶56, calculate similarity / distance between incident tickets to output a type of confidence score)
based on the evaluation, make a determination of whether to proceed under either a high-risk path or a low-risk path (¶175, using machine learning processes to identify hidden relationships or patterns connecting different data points, trigger execution on future similar scenarios to generate alerts for virtual agent, and use operational risk models 124 to predict operational risk events that could cause impact from a financial, reputational, operational or regulatory perspective; ¶179 and ¶183, combine machine learning prescriptive models 126 and operational risk model 124 with natural language processor 120 on processed text fields of IT incident tokens to generate an operational risk prediction using the operation risk model; ¶184, link summaries to predictive models 126 and operational risk models 124 to identify risk events and provide summaries of those events; i.e., predict if a prescriptive solution has a risk from a financial, reputational, operational or regulatory perspective with no risk being “low-risk path” and financial, reputational, operational or regulatory risk being “high-risk path”); and
perform one or more automated actions in accordance with the determination of whether to proceed under either the high-risk path or the low-risk path (¶184, display condensed summaries linked to predictive models 126 and operational risk models 124 to identify risk events and provide summaries of those events).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to make a determination of whether to proceed under either a high-risk path or a low-risk path based on the evaluation and perform one or more automated actions accordingly in order to measure similarity between new incident against all historical incidents, provide a confidence score to users (Cai, ¶54 and ¶56; compare Heinz, ¶18 and ¶36, obtain a similarity measure between source vectors created from source data being analyzed to target vectors in a targeted set of knowledge), and predict operational risk events that could cause impact from a financial, reputational, operational or regulatory perspective (Cai, ¶175).
Regarding Claims 2 and 15, Heinze discloses wherein each respective data entry in the set of data entries is associated with a respective data-entry type (¶37, morphological characteristics of the term under consideration; ¶38, syntactic characteristics of the terms under consideration; ¶41, date and time information presented in a source vector’s source text).
Regarding Claims 3 and 16, Heinze discloses wherein the prediction score that is generated and output by the trained machine learning model is based further on the respective data-entry type associated with each of the set of data entries (¶¶28-30, vector comparison between the source vector and the target vector according to equation (1) requires vector processing application 112 to determine x21 + x22 and y21 + y22+ y23 by aggregating the magnitudes (“weights”) of features (i.e., morphological characteristics, syntactic characteristics, time and date information) in the source vector and the target vectors respectively to obtain the similarity measure).
Regarding Claims 4 and 17, Heinze discloses wherein, while processing each respective data entry in the set of data entries, the recency-weight function that is utilized for each respective data entry is selected based on the respective data-entry type of the respective data entry (¶41, select magnitude (i.e., weight) for a target vector dimension for vector comparison based on date and time information presented in the source vector’s source text).
Regarding Claims 5 and 18, Heinze discloses wherein each respective data entry in the set of data entries is obtained from a respective data source of the one or more data sources (¶18, source vectors are created from source data being analyzed; ¶21, a parser creates source vectors from one or more source documents), and wherein the prediction score that is generated and output by the trained machine learning model is based further on the respective data source of each of the set of data entries (¶¶28-30, vector comparison between the source vector and the target vector according to equation (1) requires vector processing application 112 to determine x21 + x22 and y21 + y22+ y23 by aggregating the magnitudes (“weights”) of features in the source vector and the target vectors respectively to obtain the similarity measure).
Regarding Claim 6, Heinze discloses wherein the set of data entries comprises a set of data entries contained within a given data object (¶32, source data vectors included in the source data 142, which can be stored in the source data storage 140).
Regarding Claim 7, Heinze discloses wherein the set of data entries comprises an index of electronic documents (¶50, a source vector is received from the source data 142 stored in the source data storage 140) and associated dates that is returned by a database search (¶32, the source data analysis unit 130 communicates with the source data storage 140 through the communication link 118 to access the requested data (e.g., source data vectors) and forwards the accessed data to the vector comparison system 134 and/or the source vector feature detection system 138; ¶41, date and time information presented in a source vector’s source text).
Regarding Claim 10, Heinze discloses wherein the recency-weight function comprises a function that assigns a higher weight to a more-recent time value and a lower weight to a less-recent time value (¶41, a higher weight is applied if the date and time information in or associated with the source vector defines an incident / encounter that occurred within a particular time span; i.e., if the incident / encounter occurred outside the particular time span, the weight would’ve been lower).
Regarding Claims 11 and 19, Heinze does not disclose wherein the one or more automated actions that are performed in accordance with the determination of whether to proceed under either the high-risk path or the low-risk path comprise: issuing a notification that corresponds to the determination of whether to proceed under either the high-risk path or the low-risk path.
Cai discloses wherein the one or more automated actions that are performed in accordance with the determination of whether to proceed under either the high-risk path or the low-risk path comprise: issuing a notification that corresponds to the determination of whether to proceed under either the high-risk path or the low-risk path (¶184, display condensed summaries and link the summaries to predictive models 126 and operational risk models 124 to identify risk events and provide summaries of those events, the risk model 124 predict operational risk events from a financial, reputational, operational or regulatory perspective).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to issue a notification / summary that corresponds to the determination of whether to proceed under either the high-risk path or the low-risk path (Cai, ¶184, identify risk events) in order to display condensed summaries of a large amount of data linked to predictive models and operational risk models that identify risk events and provide summaries of those events (Cai, ¶184; i.e., a summary showing a financial risk, reputational risk, operational risk or regulatory risk corresponds to high risk path where a summary showing no such risk correspond to a low risk path).
Regarding Claims 12 and 20, Heinze does not disclose wherein the one or more automated actions that are performed in accordance with the determination of whether to proceed under either the high-risk path or the low-risk path comprise: storing the set of data entries in a given database.
Cai discloses wherein the one or more automated actions that are performed in accordance with the determination of whether to proceed under either the high-risk path or the low-risk path comprise: storing the set of data entries in a given database (¶51, update a knowledge base for the natural language processor 120 using machine learning, prescriptive models 126, and processed text fields of IT incident tokens).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to store the set of data entries in a given database (e.g., Heinze, ¶18 and ¶23, semantic data storage 120 storing target vector data 124 as knowledge base for the labeled target vectors) in order to implement automatic expansion of the knowledge base / database to provide a self-learning architecture (Cai, ¶53).
Claims 8-9 are rejected under 35 USC 103(a) as being unpatentable over Heinze et al. (US 2008/0256329 A1) and Cai et al. (US 2019/0349321 A1) as applied to claim 1, in view of Subramanian et al. (US 2019/0354887 A1).
Regarding Claim 8, Heinze does not disclose wherein the vectorization technique comprises an embedding-based vectorization technique that operates on tokens produced by a tokenization technique.
Subramanian discloses system for word vector embedding (Fig. 3) comprising a word embedding analyzer determining a word embedding by preprocessing a document using a tokenization technique to produce tokens (¶64 and Fig. 3, preprocessing 300) and to perform embedding based vectorization technique to operate on the produced tokens (¶64 and Fig. 3, process 312, using a trained word embedding model to infer a vector).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to implement an embedding-based vectorization technique that operates on tokens produced by a tokenization technique in order to infer word embedding relationship based on the principle that words which are similar in context appear closer in word embedding space (Subramanian, ¶64; compare Heinze, ¶36, determine whether a term that defines a target vector dimension is also present in the source vector under comparison to obtain a similarity measure).
Regarding Claim 9, Heinze does not disclose wherein processing each respective data entry in the set of data entries further involves: prior to converting the respective text content of the respective data entry into the respective vector representation using the vectorization technique, cleansing the respective text content of the respective data entry by removing any stop word that is identified within the respective text content of the respective data entry.
Cai discloses prior to perform text similarity processing of vector representations of respective data entries (¶52 and Fig. 9), cleansing the respective text content of the respective data entry by removing any stop word that is identified within the respective text content of the respective data entry (¶54, Fig. 8, remove stop words from title and description; ¶145, parsing data set to remove low relevance words).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to implement a parser of one or more source documents (Heinze, ¶21, source vectors were created by a parser from one or more source documents) to cleanse the respective text content of the respective data entry by removing any stop word that is identified within the respective text content of the respective data entry when converting the respective text content of the respective data entry into the respective vector representation using the vectorization technique in order to remove low relevance words to generate a simplified data set (Cai, ¶145, parsing the data set and removing low relevance words from the sentences to generate a simplified data set; Fig. 8, receive title and description, and remove stop words).
The combination does not teach stop word removal was performed prior to converting the respective text content of the respective data entry into the respective vector representation using the vectorization technique.
Subramanian discloses prior to converting the respective text content of the respective data entry into the respective vector representation using the vectorization technique, cleansing the respective text content of the respective data entry by removing any stop word that is identified within the respective text content of the respective data entry (¶64 and Fig. 3, stop word removal is performed at preprocessing 300 and vectorization is performed at step 312).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to implement a parser of one or more source documents (Heinze, ¶21, source vectors were created by a parser from one or more source documents) to cleanse the respective text content of the respective data entry by removing any stop word that is identified within the respective text content of the respective data entry prior to converting the respective text content of the respective data entry into the respective vector representation using the vectorization technique in order to perform preprocessing (Subramanian, Fig. 3, preprocessing 300) to remove low relevance words to generate a simplified data set (Cai, ¶145, parsing the data set and removing low relevance words from the sentences to generate a simplified data set; Fig. 8, receive title and description, and remove stop words).
Conclusion
Prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 10878505 B1 discloses a data analytics platform for forecasting future states of commodities and assets by processing textual and numerical data sources to extract sentiment from textual data in a natural language processing engine, evaluate numerical data in a time-series analysis, and generate an initial forecast for commodity or asset being analyzed (Abstract). In particular, a time-series modeling engine 170 quantifies observations in the structured data sources 120 to permit inferences to be drawn from past performance reflected in the historical pricing data 121, and to extrapolate data points that permit an application to temporally similar simulations of future outcomes (Col 15, Rows 48-53). Further, a commodity-specific neural networks 190 also incorporate a time delay, or feedback loop 198, which is calculated to account for temporal dependencies that help to explain sentiment expressed in the representative text vector based on the taxonomy members in relation to price movements over time, and to further improve the results of the commodity forecasting algorithm 180 (Col 19, Rows 5-11) where weights can be initialized and change (i.e. decay) over time, as a system learns what weights should be, and how they should be adjusted (Col 19, Row 67 – Col 20, Row 10)
US 11533217 B2 discloses system for predicting system or network failures by implementing early warning signal time frame estimation generating vector representation of symptoms that represent a qualifying network function failure prediction scenario (Abstract and Col 13, Rows 18-21). In particular, using vector representation of symptom events and time sliced collections of the events, TF-IDF weighted document vectors for each time slice can be calculated and use Approximate nearest neighbor algorithm to find and group all similar network function failure prediction situation instances (Col 13, Rows 20-27).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to examiner Richard Z. Zhu whose telephone number is 571-270-1587 or examiner’s supervisor Hai Phan whose telephone number is 571-272-6338. Examiner Richard Zhu can normally be reached on M-Th, 0730:1700.
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/RICHARD Z ZHU/Primary Examiner, Art Unit 2654 9/21/2026
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