DETAILED ACTION
This communication is a Final Rejection Office Action in response to the 4/2/2026 submission filed in Application 18/927,601.
Claims 7, 14 are cancelled. Claims 21-22 are new. Claims 1-6, 8-13, 15-22 are now presented.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments files 4/2/2026 with respect to claim(s) the prior art have been considered but are moot because they do not applied to the new grounds of rejection that was necessitated by amendment..
Applicant's remaining arguments have been fully considered but they are not persuasive.
Regarding the rejection under 101, the Applicant argues “Assignee respectfully cites to paragraph [0010] of the Specification which states that the processes "provide a technical improvement for generating a data structure comprising non- standardized records by efficiently determining a corrected format of the data and organizing the data accordingly." Such a technical improvement of generating the data structure is furthered by the identification of null or missing values in the data structure and subsequently searching for and replacing the null values with information retrieved from external data sources. As an example, techniques described in the claims format a series of unstructured data into a structured data set that results in at least one null category (e.g., a missing or incomplete data point), retrieving data for the null category from a remote device (e.g., an external data source), and replacing the null category with the data retrieved (e.g., to complete the data set). Thus, the completeness and any subsequent analysis of the structured data set is enhanced since the techniques of the claims provide for a way to identify and resolve incomplete data sets.”
The Examiner respectfully disagrees. The claims do not describe how the formatting schema is applied or how the schema results in a null category. Under the broadest reasonable interpretation, a human can apply a formatting schema to the set of non-standardized records to obtain standardized records, wherein the formatting schema results in a null category of a particular standardized record. As such, this amounts to an abstract idea.
Regarding the rejection under 101, the Applicant further argues “Claims directed to an improvement to computer functionality are not directed to an abstract idea. See Enfish LLC v. Microsoft Corp., F.3d 1327, 1336 (Fed. Cir. 2016). In Enfish, the Federal Circuit found claims directed to a self-referential table were not directed to an abstract idea under step one of the Mayo test. See 822 F.3d 1327, 1339 (Fed. Cir. 2016). In particular, the court reasoned that the self-referential table recited in the claims improved the way a computer stores and retrieves data in memory. See Id. In Finjan Inc. v. Blue Coat Systems, Inc., the Federal Circuit found claims directed to linking a security profile to a downloadable were patent eligible because the claims recited a new kind of file that enables a computer security system to perform new functions. See 879 F.3d 1299, 1304-1305 (Fed. Cir. 2018). Thus, under Enfish and Finjan claims are not directed to a patent ineligible concept when the claimed elements result in an improvement to functionality of a computer for performing a task (e.g., storing/retrieving data and scanning/file associating, etc.). Similarly, finding automatic ways to complete or resolve incomplete data sets as in the current claims is a problem arising from a computer and, therefore, patent eligible.”
The Examiner respectfully disagrees. In Finjan the Federal Circuit found “The “behavior-based” approach to virus scanning was pioneered by Finjan and is disclosed in the ’844 patent’s specification. In contrast to traditional “code-matching” systems, which simply look for the presence of known viruses, “behavior-based” scans can analyze a downloadable’s code and determine whether it performs potentially dangerous or unwanted operations—such as renaming or deleting files. Because security profiles communicate the granular information about potentially suspicious code made available by behavior-based scans, they can be used to protect against previously unknown viruses as well as “obfuscated code”—known viruses that have been cosmetically modified to avoid detection by code-matching virus scans.” Further, the court found “the method of claim 1 employs a new kind of file that enables a computer security system to do things it could not do before. The security profile approach allows access to be tailored for different users and ensures that threats are identified before a file reaches a user’s computer. The fact that the security profile “identifies suspicious code” allows the system to accumulate and utilize newly available, behavior-based information about potential threats. The asserted claims are therefore directed to a non-abstract improvement in computer functionality, rather than the abstract idea of computer security writ large.”
In the instant case, there is no new approach that improves the technology that was pioneered by the Applicant. The instant claims require organizing data in a schema. This is directed to a process that can be performed mentally. Limitations that are classified as abstract cannot also be an improvement to the technology.
Further, the Examiner respectfully disagrees that the claims are similar to the clams in Enfish. Page 11 of the Enfish decision the Federal Circuit states: "the first step in the Alice inquiry in this case asks 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. As noted infra, in Bilski and Alice and virtually all of the computer-related § 101 cases we have issued in light of those Supreme Court decisions, it was clear that the claims were of the latter type-requiring that the analysis proceed to the second step of the Alice inquiry, which asks if nevertheless there is some inventive concept in the application of the abstract idea. See Alice, 134 S. Ct. at 2355, 2357-59. In this case, however, the plain focus of the claims is on an improvement to computer functionality itself, not on economic or other tasks for which a computer is used in its ordinary capacity. Accordingly, we find that the claims at issue in this appeal are not directed to an abstract idea within the meaning of Alice. Rather, they are directed to a specific improvement to the way computers operate, embodied in the self-referential table." 7. As such the Federal Circuit found that the claims at issue in Enfish were not directed toward an Abstract idea, but directed toward an improvement to computer functionality itself. In the instant case, the Examiner has identified Abstract ideas to be present in the claims. Further, the claims do not recite a technical improvement because limitations that fall into the abstract idea groupings cannot also be directed to a technical improvement. Further as explained in the rejection, the additional elements beyond the abstract idea do not result in an inventive concept of integrate the abstract idea into a practical application.
Regarding the rejection under 101, the Applicant further argues “Additionally, in the December 5, 2025 Memorandum to the Patent Examining Corps, the Office amended the MPEP § 2106.04(d) to read, in part, "first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize that the claimed invention as providing an improvement in the functioning of a computer, or an improvement to other technology or a technical field." As described above, the Specification clearly states that the technique described in the claims improves generating data structures by applying a formatting schema to unstructured data and organizing the data accordingly. The Memo goes on to clarify that one enumerative improvement in Ex Parte Desjardins includes, among others, "the enablement of reduced complexity in the system." As described above, the current claims are directed to identifying and replacing null or missing data categories with value retrieved automatically form external data sources, thereby reducing the complexity of a computational system that would otherwise require manual intervention and/or additional prompts or inputs to organize the data due to missing data points.”
In Ex Parte Desjardins the Appeals Review Panel found that “In Step 2A Prong Two, the ARP then determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. Importantly, the ARP evaluated the claims as a whole in discerning at least the limitation “adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task” reflected the improvement disclosed in the specification”. In the instant case there is no such technical improvement to machine learning processes or to formatting data schemas. As such the claims are not similar to those recited in Desjardins.
Regarding the rejection under 101, the Applicant further argues “The Examiner further takes the position that the claims are not integrated into a practical application. See Office Action at page 6. Assignee respectfully disagrees and submits that a claim integrates a judicial exception if the claimed invention "improves the functioning of a computer or improves another technology or technical field." See October 2019 Update of Patent Eligibility Guidance at 2106.04(d)(1). More specifically, Assignee contends the claims integrate the abstract idea into a practical application of automatically completing incomplete data sets with compatible information retrieved from external data sources. Automatically searching for and replacing missing data in a data set is not something that can be performed as a mental process, especially when the data used to replace the missing data comes from data sources not local to or immediately accessible to an organizer of the data. In other words, a person would not be able to perform the claimed processes automatically at least because the human mind does not perform data organization or supplementation actions automatically.
The Examiner respectfully disagrees. A human (or a human with the aid of a pen and paper) can search for and replace missing data in a data set. The fact that the process is automated amounts to merely relying on a computer to perform a function that a human could otherwise perform. This is not sufficient to integrate the abstract idea into a practical 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 1-6, 8-13, 15-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
When considering subject matter eligibility under 35 U.S.C. 101, in step 1 it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, in step 2A prong 1 it must then be determined whether the claim is recite a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). If the claim recites a judicial exception, under step 2A prong 2 it must additionally be determined whether the recites additional elements that integrate the judicial exception into a practical application. If a claim does not integrate the Abstract idea into a practical application, under step 2B it must then be determined if the claim provides an inventive concept.
In the instant case Claims 1-7 are directed toward a computer program product to apply a formatting schema to the set of non-standardized records to obtain standardized record. Claims 8-14 are directed toward a system to apply a formatting schema to the set of non-standardized records to obtain standardized record. Claims 15-20 are directed toward a method to apply a formatting schema to the set of non-standardized records to obtain standardized record. As such, each of the Claims is directed to one of the four statutory categories of invention.
MPEP 2106.04 II. A. explains that in step 2A prong 1 Examiners are to determine whether a claim recites a judicial exception. MPEP 2106.04(a) explains that:
To facilitate examination, the Office has set forth an approach to identifying abstract ideas that distills the relevant case law into enumerated groupings of abstract ideas. The enumerated groupings are firmly rooted in Supreme Court precedent as well as Federal Circuit decisions interpreting that precedent, as is explained in MPEP § 2106.04(a)(2). This approach represents a shift from the former case-comparison approach that required examiners to rely on individual judicial cases when determining whether a claim recites an abstract idea. By grouping the abstract ideas, the examiners’ focus has been shifted from relying on individual cases to generally applying the wide body of case law spanning all technologies and claim types.
The enumerated groupings of abstract ideas are defined as:
1) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);
2) Certain methods of organizing human activity – fundamental economic principles or 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 or instructions) (see MPEP § 2106.04(a)(2), subsection II); and
3) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
As per step 2A prong 1 of the eligibility analysis, claim 1 is directed to the abstract idea of apply a formatting schema to the set of non-standardized records to obtain standardized records which falls into the abstract idea categories of certain methods of organizing human activity and mental processes.
The elements of Claim 1 that represent the Abstract idea include:
apply a formatting schema to the set of non-standardized records to obtain standardized records;
wherein the formatting schema results in a null category of a particular standardized record of the standardized records;
replace the null category in the particular standardized record with the data retrieved
associating the standardized records with a mapping functionality in accordance with data within the non-standardized records associated with a location.
MPEP 2106.04(a)(2) II. states:
The phrase "methods of organizing human activity" is used to describe concepts relating to:
fundamental economic principles or 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, and business relations); and
managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions).
The Supreme Court has identified a number of concepts falling within the "certain methods of organizing human activity" grouping as abstract ideas. In particular, in Alice, the Court concluded that the use of a third party to mediate settlement risk is a ‘‘fundamental economic practice’’ and thus an abstract idea. 573 U.S. at 219–20, 110 USPQ2d at 1982. In addition, the Court in Alice described the concept of risk hedging identified as an abstract idea in Bilski as ‘‘a method of organizing human activity’’. Id. Previously, in Bilski, the Court concluded that hedging is a ‘‘fundamental economic practice’’ and therefore an abstract idea. 561 U.S. at 611–612, 95 USPQ2d at 1010.
In the instant case, the limitations of claims 4-5 that are directed to searching for candidates amounts to method of organizing human activity.
MPEP 2106.04(a)(2) states:
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
In the instant case, the limitations of
apply a formatting schema to the set of non-standardized records to obtain standardized records;
wherein the formatting schema results in a null category of a particular standardized record of the standardized records;
replace the null category in the particular standardized record with the data retrieved
associating the standardized records with a mapping functionality in accordance with data within the non-standardized records associated with a location.
cover the performance of the limitations in the mind but for the recitation of generic computer components. The claims do not define how the schema results the null category or how the data is retrieved and replaced. Under the broadest reasonable interpretation, a human can replace a null category with retrieved data.
Under step 2A prong 2 the examiner must then determine if the recited abstract idea is integrated into a practical application. MPEP 2106.04 states:
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);
• 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).
In the instant case, this judicial exception is not integrated into a practical application. In particular, Claim 1 recites the additional elements of:
A non-transitory computer readable medium comprising computer readable code executable by one or more processors to:
obtain a set of non-standardized records each associated with a candidate, wherein the set of non-standardized records are obtained from a plurality of sources;
retrieve, from a remote device, data for the null category in response to a query requesting the standardized records
and store the standardized records in a data structure,
However, the computer elements (the non-transitory computer readable medium comprising computer readable code executable by one or more processors) are recited at a high level of generality and given the broadest reasonable interpretation are simply generic computers performing generic computer functions. Generic computers performing generic computer functions, alone, do not amount to significantly more than the abstract idea and mere instructions to implement an abstract idea on a computer.
Further, the obtaining records are obtained from a plurality of sources and retrieving information from a remote device under the broadest reasonable interpretation, amounts to insignificant pre-solution and post-solution activity which the MPEP says is insignificant extra solution activity (see MPEP 2106.05(g).
Further, the storing of information under the broadest reasonable interpretation, amounts to insignificant post-solution and post-solution activity which the MPEP says is insignificant extra solution activity (see MPEP 2106.05(g).
Viewing the use of generic computers in combination with the data gathering and storage does not add anything further than looking at the limitations individually. When viewed either individually, or as an ordered combination, the additional elements do not integrate the abstract ide into a practical application.
In step 2B, the Examiner must determine whether the claim adds a specific limitation other than what is well-understood, routine, conventional activity in the field - see MPEP 2106.05(d).
Nothing in the claim indicates that the receiving or transmitting of information is anything other than conventional. See MPEP 2106.05(d) that states “Receiving or transmitting data over a network, e.g., using the Internet to gather data is conventional when claimed in a merely generic manner (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Further, nothing in the claim indicates that the storing of data on a data structure is anything other than conventional. See MPEP 2106.05(d) that states “Storing and retrieving information in memory is conventional when claimed generically (see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93)
Further, similar to the analysis with respect to step 2A prong 2 recitations of claim limitations that recite the use of a generic computer to carry out an abstract idea cannot provide an inventive concept under step 2B of the eligibility analysis.
Viewing the use of generic computers in combination with the data gathering and storage does not add anything further than looking at the limitations individually. When viewed either individually, or as an ordered combination, the additional limitations do not amount to an inventive concept.
Further, Claims 2-7 further limit the abstract idea of an analysis that can be performed mentally but fail to remedy the deficiencies of the parent claim as they do not impose any limitations that amount to significantly more than the abstract idea itself.
Further, Claim 2 recites the use of a model trained to format the non-standardized records into a format associated with the data structure. However, the use of the trained model is indicative of adding the words “apply it” (or an equivalent) with the judicial exception. MPEP 2106.05(f) states:
When determining whether a claim simply recites a judicial exception with the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners may consider the following:
(1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.
By way of example, in Intellectual Ventures I v. Capital One Fin. Corp., 850 F.3d 1332, 121 USPQ2d 1940 (Fed. Cir. 2017), the steps in the claims described "the creation of a dynamic document based upon ‘management record types’ and ‘primary record types.’" 850 F.3d at 1339-40; 121 USPQ2d at 1945-46. The claims were found to be directed to the abstract idea of "collecting, displaying, and manipulating data." 850 F.3d at 1340; 121 USPQ2d at 1946. In addition to the abstract idea, the claims also recited the additional element of modifying the underlying XML document in response to modifications made in the dynamic document. 850 F.3d at 1342; 121 USPQ2d at 1947-48. Although the claims purported to modify the underlying XML document in response to modifications made in the dynamic document, nothing in the claims indicated what specific steps were undertaken other than merely using the abstract idea in the context of XML documents. The court thus held the claims ineligible, because the additional limitations provided only a result-oriented solution and lacked details as to how the computer performed the modifications, which was equivalent to the words "apply it". 850 F.3d at 1341-42; 121 USPQ2d at 1947-48 (citing Electric Power Group., 830 F.3d at 1356, 1356, USPQ2d at 1743-44 (cautioning against claims "so result focused, so functional, as to effectively cover any solution to an identified problem")).
In the instant case, the additional elements of the broadly a model trained to format the non-standardized records into a format associated with the data structure attempt to cover any solution to the identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, which does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it”. For example, the claims do not state how the model is trained or how it formats the non-standardized records into a format associated with the data structure. As such, the broadly recited model does not integrate a judicial exception into a practical application or provide significantly more.
Further, similar to the analysis with respect to step 2A prong 2 recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished cannot provide an inventive concept under step 2B of the eligibility analysis.
Further, Claim 4-6 amount to searching for candidates and displaying the results. The searching of candidates amounts to abstract mental processes and abstract methods of organizing human activity. The display of the results amounts to well-known and insignificant post solution activity.
Accordingly, the Examiner concludes that there are no meaningful limitations in claims 1-7 that transform the judicial exception into a patent eligible application such that the claim amounts to significantly more than the judicial exception itself.
The analysis above applies to all statutory categories of invention. As such, the presentment of claim 1 otherwise styled as a method or system, for example, would be subject to the same analysis. Therefore, Claims 8-20 are rejected for the similar rational that applied to claims 1-7.
Claim Rejections - 35 USC § 103
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) 1, 2, 3, 8, 9, 10, 15, 16, 17, 21, 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sendra US 2024/0078497 A1 in view or Singh US 2024/0394526 A1 in view of Litzow US 2008/0154694 A1.
As per Claim 1 Sendra teaches A non-transitory computer readable medium comprising computer readable code executable by one or more processors to:
obtain a set of non-standardized records each associated with a candidate, wherein the set of non-standardized records are obtained from a plurality of sources; Sendra para. 118 -119 teach at 805, a set of experience data of a user is received as unstructured data. For example, the set of experience data is related to educational and work experience of the user, and includes data associated with one or more of technical skills, professional skills, competencies, formal education, work and volunteer experience, badges, certifications, credentials, and patents. This data can be received in various forms including paper or as an electronic document.
At 810, the unstructured experience data is converted into structured experience data of the user.
apply a formatting schema to the set of non-standardized records to obtain standardized records; and Sendra para. 118 -119 teach At 805, a set of experience data of a user is received as unstructured data. For example, the set of experience data is related to educational and work experience of the user, and includes data associated with one or more of technical skills, professional skills, competencies, formal education, work and volunteer experience, badges, certifications, credentials, and patents. This data can be received in various forms including paper or as an electronic document. At 810, the unstructured experience data is converted into structured experience data of the user. Further, para. 59 teaches information about “What I Know” can include data related to an individual's skills and expertise (such as, skills, experience, education, certification, languages, publications, and awards). Information about “What I Know” can be received from one or more data sources. For example, skills, competencies, and capabilities of an employee can be obtained and inferred from employee input (such as entries in a job application form), human resources information system (HRIS) sources (such as, employee activities, achievement records, projects, learning and development, team activities, and goal achievement), and peer or manager feedback. A combination of various inputs can be used for What I Know, and may vary between organizations, as well as between different users in the same organizations. It should also be noted that both structured and unstructured data may comprise What I Know data. As noted earlier, if the user is directly answering a prompt such as, “What is your highest degree?”, the response can be stored as structured data as it can be associated with an appropriate label such as, “HighestDegree.” However, in other cases, the data may be received in unstructured form, such as an electronic image of a document, where the data will need to be extracted and stored in a structured format as through a context analyzer.
store the standardized records in a data structure, Sendra Abstract teaches he structured experience data, the structured personality data, and the structured motivational and preferences data are combined into a user profile, which is stored in a database.
Sendra does not explicitly disclose wherein the data structure associates the standardized records with a mapping functionality in accordance with data within the non-standardized records associated with a location. Singh para. 64 teaches however, in accordance with various embodiments of the present disclosure, a predictive machine learning model may be trained to predict an association of a data field of unstructured data to one of a plurality of candidate data tables. The plurality of candidate data tables may comprise data tables of which the data field may be potentially mapped to and standardized according to a common data model. Accordingly, the predictive machine learning model may be trained with disambiguation embeddings based on the common data model and logical data type characteristics associated with the common data model. Feedback data associated with prediction output generated by the disclosed predictive machine learning model may be used to re-train weights relevant to the feedback data. This technique will lead to higher accuracy of performing data standardization operations. In doing so, the techniques described herein improve efficiency and speed of training predictive machine learning models, thus reducing the number of computational operations needed and/or the amount of training data entries needed to train predictive machine learning models. Accordingly, the techniques described herein improve the computational efficiency, storage-wise efficiency, and/or speed of training predictive machine learning models. Both Sendra and Singh are directed to standardizing unstructured data. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Sendra to include wherein the data structure associates the standardized records with a mapping functionality in accordance with data within the non-standardized records associated with a location as taught by Singh to provide more accurate data standardization operations (see para. 64).
Sendra in view of Singh does not teach wherein the formatting schema results in a null category of a particular standardized record of the standardized records; retrieve, from a remote device, data for the null category in response to a query requesting the standardized records replace, automatically by the one or more processors, the null category in the particular standardized record with the data retrieved
However, Litzow para. 91 teaches over time and as the system functions, the recording of various transactions and optionally, the customer's own response to questions posed, refines this record. Optionally, the invention is the data within the system may augment the Customer Database, with data from questionnaires posed to the customer on a regular or occasional basis. After enrollment at step 113, the DPS examines the information garnered against its own standards for operable completeness. In the event that the information is either incomplete or contains apparently inconsistent information 114, the DPS will request and receive supplemental information from a third party, such as, for example, credit reporting agencies 115. In light of the additional information garnered, the information is again compiled and tested for completeness. If it is still incomplete after all available supplemental third party information has been received, the enrolling customer may be contacted for clarification. Both Sendra in view of Singh and Litzow are directed to normalizing data. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Sendra to include wherein the formatting schema results in a null category of a particular standardized record of the standardized records; retrieve, from a remote device, data for the null category in response to a query requesting the standardized records replace, automatically by the one or more processors, the null category in the particular standardized record with the data retrieved as taught by Litzow to compile the most complete data and thereby produce the most accurate results (as suggested by para. 91).
As per Claim 2 Sendra does not ex0licity disclose the non-transitory computer readable medium of claim 1, wherein the computer readable code to apply the formatting schema to the set of non-standardized records comprises computer readable code to: apply the non-standardized records to a model trained to format the non-standardized records into a format associated with the data structure. However, Singh para. 25 teaches In accordance with various embodiments of the present disclosure, a predictive machine learning model may be trained to predict an association of a data field of unstructured data to one of a plurality of candidate data tables. The plurality of candidate data tables may comprise data tables of which the data field may be potentially mapped to and standardized according to a common data model. Accordingly, the disclosed predictive machine learning model may be trained with disambiguation embeddings based on the common data model and logical data type characteristics associated with the common data model. Feedback data associated with prediction output generated by the disclosed predictive machine learning model may be used to re-train weights relevant to the feedback data. This technique will lead to higher accuracy of performing data standardization operations. In doing so, the techniques described herein improve efficiency and speed of training predictive machine learning models, thus reducing the number of computational operations needed and/or the amount of training data entries needed to train predictive machine learning models. Accordingly, the techniques described herein improve the computational efficiency, storage-wise efficiency, and/or speed of training predictive machine learning models. Both Sendra and Singh are directed to standardizing unstructured data. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Sendra to include wherein the computer readable code to apply the formatting schema to the set of non-standardized records comprises computer readable code to: apply the non-standardized records to a model trained to format the non-standardized records into a format associated with the data structure as taught by Singh to provide more accurate data standardization operations (see para. 64).
As per Claim 3 Sendra teaches the non-transitory computer readable medium of claim 1, wherein the non-standardized records comprise at least classification of data from a set of classifications of data comprising a name, an associated institution, a degree name, and a skillset. Sendra para. 59 teaches Information about “What I Know” can include data related to an individual's skills and expertise (such as, skills, experience, education, certification, languages, publications, and awards). Information about “What I Know” can be received from one or more data sources. For example, skills, competencies, and capabilities of an employee can be obtained and inferred from employee input (such as entries in a job application form), human resources information system (HRIS) sources (such as, employee activities, achievement records, projects, learning and development, team activities, and goal achievement), and peer or manager feedback. A combination of various inputs can be used for What I Know, and may vary between organizations, as well as between different users in the same organizations. It should also be noted that both structured and unstructured data may comprise What I Know data. As noted earlier, if the user is directly answering a prompt such as, “What is your highest degree?”, the response can be stored as structured data as it can be associated with an appropriate label such as, “HighestDegree.” However, in other cases, the data may be received in unstructured form, such as an electronic image of a document, where the data will need to be extracted and stored in a structured format as through a context analyzer.
As per Claim 21 Sendra teaches The non-transitory computer readable medium of claim 1, wherein the remote device retrieves the data from at least one of a record from an affiliated institution, a social network, or an internet-based database. However, Litzow para. 91 teaches request and receive supplemental information from a third party, such as, for example, credit reporting agencies 115. In light of the additional information garnered, the information is again compiled and tested for completeness. If it is still incomplete after all available supplemental third party information has been received, the enrolling customer may be contacted for clarification. Both Sendra in view of Singh and Litzow are directed to normalizing data. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Sendra to include wherein the remote device retrieves the data from at least one of a record from an affiliated institution, a social network, or an internet-based database as taught by Litzow to compile the most complete data and thereby produce the most accurate results (as suggested by para. 91).
Claims 8, 9, 10, 22 recite limitations similar to those recited in claims 1, 2, 3, 21 and are rejected for similar reasons. Further, Sendra teaches A system comprising: one or more processors; and one or more computer readable media comprising computer readable code executable by the one or more processors to perform the recited steps.(see Sendra para. 4)
Claims 15-17 recite limitations similar to those recited in claims 1, 2, 3 and are rejected for similar reasons. Further, Sendra teaches A method comprising performing the recited steps (see Sendra para. 4)
Claim(s) 4, 6, 11, 13, 18, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sendra US 2024/0078497 A1 in view of Singh US 2024/0394526 A1 in view of Litzow US 2008/0154694 A1 in further view of Lamont US 2020/0225653 A1.
As per Claim 4 Sendra does not explicitly disclose the non-transitory computer readable medium of claim 1, further comprising computer readable code to: provide a user interface comprising one or more user input components configured to receive user input comprising a search query; and in response to receiving the search query via the user interface, provide a results interface comprising: a listing of relevant candidates based on the standardized records associated with the relevant candidates; and a graphical map indicating the location for each of the relevant candidates in accordance with the standardized records associated with the relevant candidates. However, Lamont para. 88 teaches the user can see the technicians 1804 located in his or her area that specialize in the type of service 1812 selected. If there is not a technician that specializes in that particular area of service then the location can be expanded out to other areas. The map 1822 can show the technicians within the user's approximate location 1802, or the summary panel 1808 can toggle the technicians 1818 to only show the technicians that specialize in such as, for example, air condition & heating, water heater, plumbing, insulation, painting, electrical, roofing, pest control, or the like. The summary panel 1808 can allow the user to toggle to show the technicians that are available in the are based upon the urgency of the service 1816. For example, if the technician does not have any availability for an urgent service request then that technician will not be shown on the map, or the technician can be shown as busy until a certain day and/or time. The user can toggle between the type of request 1912 to show which technicians are available for immediate service calls, or available to schedule a quote, or future service call. The summary panel 1808 can provide the technicians based upon ratings, time, service type, location, and/or urgency. The user interface can include a service location identifier 1820 that can identify the location of the computing device or the buildings location that the user has specified. The service location identifier 1820 can allow the user to select his or her preference between an address or the computing device's location, or in some embodiments the user can search 1814 for a particular technician or building location to request services to another location from specified technician. Both Sendra and Lamont are directed to matching candidates to jobs. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Sendra to include provide a user interface comprising one or more user input components configured to receive user input comprising a search query; and in response to receiving the search query via the user interface, provide a results interface comprising: a listing of relevant candidates based on the standardized records associated with the relevant candidates; and a graphical map indicating the location for each of the relevant candidates in accordance with the standardized records associated with the relevant candidates as taught by Lamont to provide an easier way to identify qualified candidates based on location (as suggested by para. 88)
As per Claim 6 Sendra does not explicitly disclose the non-transitory computer readable medium of claim 4, wherein the graphical map comprises one or more selectable components which, when selected, cause the results interface to update in accordance with the selected component. However, Lamont para. 87 teaches referring to FIGS. 18-19, which illustrate example user interfaces that are displayed to a user to enable the steward to request an on-demand service thought the use of a connector, according to an embodiment. When the user initiates and operates the technician on-demand service application on his or her computing device the user can interact with features on the home page of the user interface on both the mobile application or on the website. The user interface can show for example, the user's region 1802, different service options, technicians in the region, the type of technician in the area, and/or the urgency of the service. The user interface can have a summary panel 1808 wherein the summary panel can select the user region 1810 or approximate location 1802, the type of services needed 1812, the urgency of the service 1816, and technicians in the region or next to the user's region or location 1818. The summary panel 1808 can be provided concurrently with the multistate selection feature (as shown in FIG. 19), or all by itself. The summary panel 1818 can all the user to show where they are located geographically 1810 on a map 1822 and toggle their location on and off. The summary panel can provide the type of services 1812 the user may need on his or her building, or can schedule for future services wherein the type of services can provide a multistate selection feature (as shown in FIG. 19) with for example, the type of services 1910, what type 1912, and how soon 1914. For example, the user can choose between, but not limited to, air condition & heating, water heater, plumbing, insulation, painting, electrical, roofing, pest control, or the like. Once the user chooses one of those options then the user will be shown another multistate selection feature 1912 which will allow the user to choose from, but not limited to, just a price quote, emergency services, second opinion, routine service, repair, replacement, install, or the like. The user will then be able to choose how soon 1914 they need the service for example, right away, today, tomorrow, choose a day and time. Both Sendra and Lamont are directed to matching candidates to jobs. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Sendra to include wherein the graphical map comprises one or more selectable components which, when selected, cause the results interface to update in accordance with the selected component as taught by Lamont to provide an easier way to identify qualified candidates based on location (as suggested by para. 88).
Claim 11, 13 recites limitations similar to those recited in claims 4, 6 and is rejected for similar reasons. Further, Sendra teaches A system comprising: one or more processors; and one or more computer readable media comprising computer readable code executable by the one or more processors to perform the recited steps.(see Sendra para. 4)
Claim 18, 20 recites limitations similar to those recited in claims 4, 6 and is rejected for similar reasons. Further, Sendra teaches A method comprising performing the recited steps (see Sendra para. 4)
Claim(s) 5, 12, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sendra US 2024/0078497 A1 in view of Singh US 2024/0394526 A1 in view of Litzow US 2008/0154694 A1 in view of Lamont US 2020/0225653 A1 as applied to claim 4 and in further view of Bheemavarap US 2018/0268373 A1.
As per Claim 5 Sendra does not explicitly disclose the non-transitory computer readable medium of claim 4, further comprising computer readable code to: determine a requested skillset from the search query; and apply the requested skillset and the non-standardized records to a model trained to identify additional relevant candidates for which standardized records comprise alternative skillsets closely aligned to the requested skillset. However, Bheemavarap [0074] Embodiments of the present invention draw comparisons between existing or past employees and a new incoming candidate on multiple dimensions. A job-specific attributes database comprising job-specific attributes profiles for different jobs, roles and positions is generated using historic organization and industry data and workforce sciences input. Current and/or past employees of the company or organization are analyzed to identify their employee-specific attributes (such as professional skills and personality traits) that match the job-specific attributes profile for their respective jobs. The job-specific attributes profile is generated using organization and industry data and workforce sciences input and thus provides the key attributes for a specific job, role or position. Therefore, by identifying the attributes of employees based on the job-specific attributes profile, the company or organization is provided with the best overview of the relevant skills and traits for each employee. The employees are then organized and mapped to the job-specific attributes database depending on their job relevant attributes. The candidate-specific attributes of a job candidate are identified by analyzing information such as the candidate's profile/resume, cover letter, assessments, communication, other accessible contributions like blogs, social media content, papers etc., interview performance, and response to interview questions. The job candidate is then matched with one or more employees with similar attributes. The system maps the candidate to clusters/groups of similar employees based on similar skills and traits and provides information on whether the candidate would be the right fit for a certain job, role, position, team, organization etc. The system may also draw comparisons between candidates and employees based on automatically drawn insights from historic organization and industry data that pertain to various aspects such as professional skills, academic and other performances, personality insights, past employment data, and job performance. In other embodiments, the system further enables stakeholders to search for candidates who are similar to a specific reference employee or have a desired set of attributes. Both Sendra and Bheemavarap are directed to identifying job candidates. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Sendra to include determine a requested skillset from the search query; and apply the requested skillset and the non-standardized records to a model trained to identify additional relevant candidates for which standardized records comprise alternative skillsets closely aligned to the requested skillset as taught by Bheemavarap to better select the best candidate for a job (see para. 64).
Claim 12 recites limitations similar to those recited in claims 5 and is rejected for similar reasons. Further, Sendra teaches A system comprising: one or more processors; and one or more computer readable media comprising computer readable code executable by the one or more processors to perform the recited steps.(see Sendra para. 4)
Claim 19 recites limitations similar to those recited in claims 5 and is rejected for similar reasons. Further, Sendra teaches A method comprising performing the recited steps (see Sendra para. 4)
Claim(s) 7, 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sendra US 2024/0078497 A1 in view of Singh US 2024/0394526 A1 in view of Litzow US 2008/0154694 A1 as applied to claim 1 and in further view of Tuchman US 9,183,203 B1.
As per Claim 7 Sendra does not explicitly disclose the non-transitory computer readable medium of claim 1, wherein the computer readable code to apply the formatting schema to the set of non-standardized records comprises computer readable code to: identify, for a particular standardized record, a null category; and request, from a remote device, data for the null category. However, Tuchman column 20 line 58-column b21 line 9 teaches FIG. 15 shows an implementation of logic flow for similarity determination in one embodiment of GDMA operation. Like the similarity determination shown in an alternative implementation in FIG. 12, the similarity determination implementation shown in FIG. 15 allows the GDMA to compare two or more entities along one or more bases for comparison. One or more term specifications may be received 1501, identifying the two or more entities to be compared. For example, received terms may comprise identifiers of two or more people, companies, organizations, documents, products, places, times, and/or the like. A determination may be made as to whether or not the entity comparison is to be data type specific 1505. If so, one or more data type specifications may be received 1510 and the type vectors corresponding to those specifications may be extracted from term tensors corresponding to each of the entities to be compared 1515. A determination may be made as to whether one or both of the term tensors are missing a type vector for the entered data type 1520. If so, an error handling procedure may be undertaken, such as setting type values to default and/or null values, providing an error message to the user, requesting reentry of data type specifications, requesting reentry of term specifications, and/or the like 1525. If no types are missing, the GDMA may evaluate an inner product for extracted data type vectors 1530 in order to determine the entity similarities, and may provide determined inner products and/or values derived therefrom for display 1555. Both Sendra and Tuchman are directed to gathering structured and unstructured data. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Sendra to include identify, for a particular standardized record, a null category; and request, from a remote device, data for the null category as taught by Tuchman to gather more complete information.
Claim 14 recites limitations similar to those recited in claims 7 and is rejected for similar reasons. Further, Sendra teaches A system comprising: one or more processors; and one or more computer readable media comprising computer readable code executable by the one or more processors to perform the recited steps.(see Sendra para. 4)
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/DEIRDRE D HATCHER/Primary Examiner, Art Unit 3625
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