DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In view of the Pre-Brief Appeal Conference decision mailed on 02/09/2026, PROSECUTION IS HEREBY REOPENED. A new ground of rejection is set forth below.
To avoid abandonment of the application, appellant must exercise one of the following two options:
(1) file a reply under 37 CFR 1.111 (if this Office action is non-final) or a reply under 37 CFR 1.113 (if this Office action is final); or,
(2) initiate a new appeal by filing a notice of appeal under 37 CFR 41.31 followed by an appeal brief under 37 CFR 41.37. The previously paid notice of appeal fee and appeal brief fee can be applied to the new appeal. If, however, the appeal fees set forth in 37 CFR 41.20 have been increased since they were previously paid, then appellant must pay the difference between the increased fees and the amount previously paid.
A Supervisory Patent Examiner (SPE) has approved of reopening prosecution by signing below:
/FAHD A OBEID/Supervisory Patent Examiner, Art Unit 3627
Claims 1, 3 and 6-17 remain pending and are examined herein below.
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, 3 and 6-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Whether a Claim is to a Statutory Category
In the instant case, claims 1, 3 and 6-15 recite a system/machine, claim 16 recites a non-transitory computer readable storage medium storing instructions configured to be executed by a system/ process and claim 17 recites a method/ process that are performing a series of functions. Therefore, these claims fall within the four statutory categories of invention of a machine and a process. Step 1 is satisfied.
Step2A – Prong 1: Does the Claim Recite a Judicial Exception
Exemplary claim 1 (and similarly claims 16 and 17) recites the following abstract concepts that are found to include an enumerated “abstract idea”:
A system for determining whether data within an input electronic document constitutes vouching evidence for an enterprise resource planning (ERP) item, the system comprising one or more processors configured to cause the system to:
receive data representing an ERP item;
generate hypothesis data based on the received data representing an ERP item and one or more reference electronic documents, wherein the hypothesis data comprises a predicted information content and a predicted location of the content in an electronic document not used to generate hypothesis data;
receive the input electronic document;
extract ERP information from the input electronic document, wherein extracting the ERP information comprises generating first data representing information content of the ERP information and second data representing a document location for the ERP information;
determine one or more spatial relationships between a plurality of entities included in the extracted ERP information:
generate a graph data structure representing the one or more spatial relationships between the plurality of entities;
apply a first set of one or more models to the hypothesis data and to extracted ERP information in order to generate first output data indicating whether the extracted ERP information constitutes vouching evidence for the ERP item;
apply a second set of one or more models to the extracted ERP information in order to generate second output data indicating whether the extracted ERP information constitutes vouching evidence for the ERP item,
wherein applying at least one of the first set of one or more models to generate the first output data and the second set of one or more models to generate the second output data is based on the graph structure representing the spatial relationships between the plurality of entities included in the extracted ERP information, and
wherein at least one of the first output data and the second output data comprises a binary indication as to whether the extracted ERP information constitutes vouching evidence for the ERP item and a location within the electronic document corresponding to the determination as to whether the extracted ERP information constitutes vouching evidence for the ERP item; and
determine whether the extracted ERP information constitutes vouching evidence for the ERP item, based on the first output data and the second output data.
[Emphasis added to show the bolded abstract idea being executed by unbolded additional elements that do not meaningfully limit the abstract idea]
This system claim is grouped within the "mental processes” grouping of abstract ideas in prong one of step 2A of the Alice/Mayo test because the claims involve a series of steps for observation, evaluation and judgement/ opinion to determine whether the extracted ERP information constitutes vouching evidence, which is a process that is encompassed by the abstract idea of mental processes. The steps of receiving (observation), generating (evaluation), extracting (observation), determining (judgement/ opinion), interlinking (observation) and applying (evaluation) in the context of this claim encompass an auditor user manually receiving data and applying multiple rules [models] to determine if the received data qualifies as evidence for an audit of Enterprise Resource Planning items/ information. The examiner has reviewed each abstract idea from each step individually and in combination with each other limitation, and still finds that the claim 1 (and similarly claims 16 and 17) recites abstract idea. See e.g., MPEP 2106.04(a)(2)(III)(D). As the outputs of the claimed process are generated by applying a first and second set of one or more models with no particular model clearly disclosed in the specification of the instant application (other than noting that said models are AI models or machine learning models), claim 1 (and similarly claims 16 and 17) recites abstract idea. See e.g., July 2024 Subject Matter Eligibility Example 47 claim 2. Accordingly, the claims recite an abstract idea.
Step2A – Prong 2: Does the Claim Recite Additional Elements that Integrate the Judicial Exception into a Practical Application
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A of the Alice/Mayo test, the additional elements of the claims, such as processors and models merely use a computer as a tool to perform an abstract idea and/or generally link the use of a judicial exception to a particular technological environment. Specifically, the processors and models perform the steps or functions of observation, evaluation and judgement/ opinion to determine whether the extracted ERP information constitutes vouching evidence. The use of a processor/computer as a tool to implement the abstract idea and/or generally linking the use of the abstract idea to a particular technological environment does not integrate the abstract idea into a practical application because it requires no more than a computer (or technical elements disclosed at a high level of generality such as processors and models) performing functions of receiving, generating, extracting, determining, interlinking and applying that correspond to acts required to carry out the abstract idea (MPEP 2106.05(f) and (h)). Accordingly, the additional elements, alone and in ordered combination, do not impose any meaningful limits on practicing the abstract idea, and the claims are directed to an abstract idea.
Step2B: Does the Claim Amount to Significantly More
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element analysis of Step 2A Prong 2 is equally applied to Step 2B. “Another consideration when determining whether a claim recites significantly more than a judicial exception is whether the additional element(s) are well-understood, routine, conventional activities previously known to the industry. This consideration is only evaluated in Step 2B of the eligibility analysis.” MPEP 2106.05(d). The courts have recognized the following computer functions as well‐understood, routine, and conventional (“WURC”) functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. Exemplary claim 1 recites the following limitations that the courts have found to be WURC:
Claim 1 includes limitations relating to transmitting data over a network (as claimed: receive data representing an ERP item, receive the input electronic document which is understood to be network communication based on ¶ [0140] of the specification of the instant application). See MPEP 2106.05(d)(II) where courts found to be WURC – i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, 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); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
Claim 1 includes limitations relating to performing repetitive calculations (as claimed: generate hypothesis data based on the received data, determine one or more spatial relationships between a plurality of entities, generate a graph data structure representing the one or more spatial relationships, apply a first set of one or more models to the hypothesis data, apply a second set of one or more models to the extracted ERP information, at least one of the first output data and the second output data comprises a binary indication, determine whether the extracted ERP information constitutes vouching evidence for the ERP item). See MPEP 2106.05(d)(II) where courts found to be WURC – ii. Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.");
Claim 1 includes limitations relating to electronically scanning or extracting data from a physical document (as claimed: extract ERP information from the input electronic document). See MPEP 2106.05(d)(II) where courts found to be WURC – v. Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank, 776 F.3d 1343, 1348, 113 USPQ2d 1354, 1358 (Fed. Cir. 2014) (optical character recognition);
Claim 1 includes limitations relating to storing and retrieving information in memory (as claimed: applying at least one of the first set of one or more models to generate the first output data and the second set of one or more models to generate the second output data is based on the graph structure representing the spatial relationships between the plurality of entities included in the extracted ERP information). See MPEP 2106.05(d)(II) where courts found to be WURC – iv. Storing and retrieving information in memory, 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;
Accordingly, when viewed alone and in ordered combination, these additional elements are not found to recite significantly more than the underlying abstract idea.
Independent claim 16 describes a system that is executed to perform functions based on instructions stored in a non-transitory computer readable storage medium, wherein said functions include receiving, generating, extracting, determining, interlinking and applying relating to observation, evaluation and judgement/ opinion to determine whether the extracted ERP information constitutes vouching evidence without additional elements beyond technical elements disclosed at a high level of generality such as a non-transitory computer readable storage medium, processor and models that provide significantly more than the abstract idea of mental processes of observation, evaluation and judgement/ opinion to determine whether the extracted ERP information constitutes vouching evidence as noted above regarding claim 1. Therefore, this independent claim is also not patent eligible.
Independent claim 17 describes a method that is performed by a system to execute functions including receiving, generating, extracting, determining, interlinking and applying relating to observation, evaluation and judgement/ opinion to determine whether the extracted ERP information constitutes vouching evidence without additional elements beyond technical elements disclosed at a high level of generality such as a processor and models that provide significantly more than the abstract idea of mental processes of observation, evaluation and judgement/ opinion to determine whether the extracted ERP information constitutes vouching evidence as noted above regarding claim 1. Therefore, this independent claim is also not patent eligible.
Dependent claims 3 and 7-10 further describes the abstract idea of mental processes. Dependent claims 3 and 7-10 add descriptive material relating to the types of data used by the system of independent claim 1, which keeps claims 3 and 7-10 disclosed at a high level of generality and does not integrate the abstract idea into a practical application or provide significantly more than the abstract idea. Therefore, dependent claims 3 and 7-10 are also not patent eligible. Further, the dependency of these claims on ineligible independent claim 1 also renders dependent claims 3 and 7-10 as not patent eligible.
Dependent claim 6 further describes the abstract idea of mental processes. Dependent claim 6 adds an augmenting step that is executed by one or more processors, however this additional element remains disclosed at a high level of generality and does not amount to more than mere computer implementation of the abstract idea, which does not integrate the abstract idea into a practical application or provide significantly more than the abstract idea. Therefore, dependent claim 6 is also not patent eligible. Further, the dependency of this claim on ineligible independent claim 1 also renders dependent claim 6 as not patent eligible.
Dependent claims 11 and 12 further describes the abstract idea of mental processes. Dependent claims 11 and 12 add generating steps relating to a similarity score, however these additional steps remain disclosed at a high level of generality and do not amount to more than mere computer implementation of the abstract idea, which does not integrate the abstract idea into a practical application or provide significantly more than the abstract idea. Therefore, dependent claims 11 and 12 are also not patent eligible. Further, the dependency of these claims on ineligible independent claim 1 also renders dependent claims 11 and 12 as not patent eligible.
Dependent claim 13 further describes the abstract idea of mental processes. Dependent claim 13 adds applying and determining steps that are executed by the one or more processors of independent claim 1, however these additional elements remain disclosed at a high level of generality and does not amount to more than mere computer implementation of the abstract idea, which does not integrate the abstract idea into a practical application or provide significantly more than the abstract idea. Therefore, dependent claim 13 is also not patent eligible. Further, the dependency of this claim on ineligible independent claim 1 also renders dependent claim 13 as not patent eligible.
Dependent claim 14 further describes the abstract idea of mental processes. Dependent claim 14 adds an applying step that is executed by the one or more processors of independent claim 1, however this additional element remains disclosed at a high level of generality and does not amount to more than mere computer implementation of the abstract idea, which does not integrate the abstract idea into a practical application or provide significantly more than the abstract idea. Therefore, dependent claim 14 is also not patent eligible. Further, the dependency of this claim on ineligible independent claim 1 also renders dependent claim 14 as not patent eligible.
Dependent claim 15 further describes the abstract idea of mental processes. Dependent claim 15 adds applying and generating steps for outputting data that are executed by the one or more processors of independent claim 1, however this additional element remains disclosed at a high level of generality and does not amount to more than mere computer implementation of the abstract idea, which does not integrate the abstract idea into a practical application or provide significantly more than the abstract idea. Therefore, dependent claim 15 is also not patent eligible. Further, the dependency of this claim on ineligible independent claim 1 also renders dependent claim 15 as not patent eligible.
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.
Claims 1, 3 and 6-17 are rejected under 35 U.S.C. 103 as being unpatentable over Lu (US 2019/0171944 A1) in view of Li et al. (US 2020/0012980 A1).
Regarding Claim 1, modified Lu teaches:
A system for determining whether data within an input electronic document constitutes vouching evidence for an enterprise resource planning (ERP) item (As the specification of the instant application [spec] uses vouching evidence to mean that there is sufficient data within a document to support the use of said document as reference material [evidence] in a verification process, such as an audit, of ERP data See Lu ¶ [0021-0024] – a system for extracting data from documents and predicting links between entities [items] using an assurance score threshold to determine if there is sufficient data to validate said extracted data from internal records, wherein said data relates to Enterprise Resource Planning), the system comprising one or more processors configured to cause the system to (See Lu ¶ [0037] – one or more processors executing the data extraction and analysis process of Lu):
receive data representing an ERP item (See Lu ¶ [0024] – ERP data processing and [0027-0028] – various communications channels used to send and receive data that includes ERP items such as a price discount for a particular inventory item);
generate … data based on the received data representing an ERP item and one or more reference electronic documents (See Lu ¶ [0033] – entity relationship extractor receiving information regarding entities identified by the entity processor and determines a relationship between various entities using link prediction methodologies, thereby showing predicted information content by example … The process extractor and reconstructor is configured to determine or reconstruct the process steps of the unstructured process based on the information obtained from or with the aid of the input [received data representing an ERP item by example] and stored in the knowledge storage [one or more reference electronic documents by example], [0037] – frame-based data extraction that examines entity document metadata to recognize whether data inputs are from a header, subject line, body or attachment [location] to an email when said input comprises an email and [0040-0041] – extracting first ERP data for an “item” and a second data recognizing the data source as the subject line of an email, thereby showing a location of data within a source document), wherein the … data comprises a predicted information content and a predicted location of the content in an electronic document not used to generate … data (As the specification of the instant application refers to content in an electronic document not used to generate hypothesis data as document content itself, see Lu ¶ [0033] – entity relationship extractor receiving information regarding entities identified by the entity processor and determines a relationship between various entities using link prediction methodologies, thereby showing predicted information content by example, [0037] – frame-based data extraction that examines entity document metadata to recognize whether data inputs are from a header, subject line, body or attachment [document content and location by example] to an email when said input comprises an email and [0040-0041] – extracting first ERP data for an “item” and a second data recognizing the data source as the subject line of an email, thereby showing a location of data within a source document);
receive the input electronic document (See Lu ¶ [0026] – receiving documents as inputs from electronic sources like text files, spreadsheets, word processor files, etc.);
extract ERP information from the input electronic document (See Lu ¶ [0031] – analyzing and storing data extracted from the various input sources), wherein extracting the ERP information comprises generating first data representing information content of the ERP information and second data representing a document location for the ERP information (See Lu ¶ [0037] – frame-based data extraction that examines document metadata to recognize whether data inputs are from a header, subject line, body or attachment [location] to an email when said input comprises an email and [0040-0041] – extracting first ERP data for an “item” and a second data recognizing the data source as the subject line of an email, thereby showing a location of data within a source document);
determine one or more spatial relationships between a plurality of entities included in the extracted ERP information (See Lu ¶ [0037] – frame-based data extraction that examines document metadata structure [spatial relationships] to recognize whether data inputs are from a header, subject line, body or attachment [location] to an email when said input comprises an email, [0038] - the document analyzer parses and tokenizes the documents to generate tokens which are discrete units of text data that are delimited by one or more of spaces or symbols in the documents. Thus, a sentence including words may be tokenized so that each token corresponds to a word or a symbol. A token selector is also included in the entity processor for discarding tokens corresponding to stop words, whitespaces and the like so that tokens including meaningful entity names and values thereof are selected for further processing [spatial relationships between a plurality of entities by example] and Figs. 9A and 9B – showing different ERP data in different locations of an email document):
generate a graph data structure representing the one or more spatial relationships between the plurality of entities (See Lu ¶ [0038] - the document analyzer parses and tokenizes the documents to generate tokens which are discrete units of text data that are delimited by one or more of spaces or symbols in the documents. Thus, a sentence including words may be tokenized so that each token corresponds to a word or a symbol. A token selector is also included in the entity processor for discarding tokens corresponding to stop words, whitespaces and the like so that tokens including meaningful entity names and values thereof are selected for further processing [spatial relationships between the plurality of entities by example], [0042] – Graphical models which encode dependencies between variables via representing the variables as nodes in a graph and dependencies between the variables as edges of the graph can be employed to infer the entity relationships and Figs. 11A – showing relationships between different ERP data nodes linked graphically);
apply a first set of one or more models to the … data and to extracted ERP information in order to generate first output data indicating whether the extracted ERP information constitutes vouching evidence for the ERP item (See Lu ¶ [0039-0041] – using category models/ algorithms to recognize that an email referring to an “item” is referencing a “product” from an inventory database based on a stored dictionary/ corpus of ERP information [data]);
apply a second set of one or more models to the extracted ERP information in order to generate second output data indicating whether the extracted ERP information constitutes vouching evidence for the ERP item (See Lu ¶ [0034] – machine learning models trained to generate an integrity assurance score [confidence score] of entity data extracted from a document [input] for similarity to values for respective entities from internal records, wherein said score is compared to a threshold to determine if sufficient data has been extracted [vouching evidence]),
wherein applying at least one of the first set of one or more models to generate the first output data and the second set of one or more models to generate the second output data is based on the graph structure representing the spatial relationships between the plurality of entities included in the extracted ERP information (See Lu ¶ [0037] – frame-based data extraction that examines document metadata structure [spatial relationships] to recognize whether data inputs are from a header, subject line, body or attachment [location] to an email when said input comprises an email, [0038] – as noted above and Figs. 9A and 9B – showing different ERP data in different locations of an email document), and
wherein at least one of the first output data and the second output data comprises a binary indication as to whether the extracted ERP information constitutes vouching evidence for the ERP item and a location within the electronic document corresponding to the determination as to whether the extracted ERP information constitutes vouching evidence for the ERP item (See Lu ¶ [0037] – frame-based data extraction that examines document metadata to recognize whether data inputs are from a header, subject line, body or attachment [location] to an email when said input comprises an email, [0040-0041] – extracting first ERP data for an “item” and a second data recognizing the data source as the subject line of an email, thereby showing a location of data within a source document and [0061] - entity values may be compared to internal record documents which may be stored in data sources in order to obtain the integrity assurance score. Based on the comparison of the integrity assurance score with an assurance score threshold, a process integrity report including the validity or invalidity of the corresponding unstructured process would be generated, wherein the validity or invalidity is the binary indication); and
determine whether the extracted ERP information constitutes vouching evidence for the ERP item, based on the first output data and the second output data (See Lu ¶ [0035] – aggregating process integrity evaluation tasks [second output] and unstructured processes that were determined to be valid or inconsistent [first output] and [0046] - to determine the authenticity of the entity values contained in the data structures, the assurance score threshold may be statistically determined based on observed data regarding correlation between the scores and accuracy of the audit results. For example, the assurance score threshold may be set at 50% so that an assurance score of greater than 50% indicates that the unstructured process and data generated from the unstructured process is accurate, thereby constituting vouching evidence for the ERP item by example).
While Lu teaches a system for extracting ERP data from electronic documents to generate prediction data based on relationships of document metadata from said extracted ERP data by example, (Lu ¶ [0033], [0037-0038] and [0040-0041]), Lu does not explicitly teach that said prediction data is hypothesis data. This is taught by Li (See Li ¶ [0087] - a hypothesis may be generated indicative of an explanation of certain items being non-reconcilable). It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include in the data extraction and prediction generating system of Lu the use of hypothesis data as taught by Li to enhance accuracy and minimize external intervention (Li ¶ [0048]), thereby improving the accuracy and efficiency of Lu’s data extraction and prediction generating system.
Regarding Claim 3, modified Lu teaches:
The system of claim 1, wherein the ERP information comprises one or more of: a purchase order number, a customer name, a date, a delivery term, a shipping term, a unit price, and a quantity (It is noted that these limitations relating to the ERP information are considered non-functional descriptive material because these limitations merely describe the types of data used by the system of independent claim 1 with no further functional limitation of said claim, therefore these limitations are not given patentable weight. Nonetheless, see Lu ¶ [0053-0055] and Figs. 9A and 9B – shows email correspondence between a product supplier [manufacturer] and a product seller [retailer] that discusses an amended contract [functioning as a purchase order number], names of both parties, wherein the retailer is functioning as the customer, current date, a delivery term of a price change after an effective date [9/1], unit pricing including F.O.B., which is a shipping term and unit quantities at various locations in a supply chain).
Regarding Claim 6, modified Lu teaches:
The system of claim 1, wherein the one or more processors are configured to cause the system to augment the … data based on one or more models representing contextual data (See Lu ¶ [0029] – using departmental data source as well as other relevant data sources [augmented data] to verify data extracted from an input document that is from a departmental data source, thereby showing use of the same contextual data as well as other contextual data [augmented] for input).
While Lu teaches a system for extracting ERP data from electronic documents to generate prediction data based on relationships of document metadata from said extracted ERP data by example, (Lu ¶ [0033], [0037-0038] and [0040-0041]), Lu does not explicitly teach that said prediction data is hypothesis data. This is taught by Li (See Li ¶ [0087] - a hypothesis may be generated indicative of an explanation of certain items being non-reconcilable). It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include in the data extraction and prediction generating system of Lu the use of hypothesis data as taught by Li to enhance accuracy and minimize external intervention (Li ¶ [0048]), thereby improving the accuracy and efficiency of Lu’s data extraction and prediction generating system.
Regarding Claim 7, modified Lu teaches:
The system of claim 6, wherein the contextual data comprises information regarding one or more synonyms for the information content of the ERP information (It is noted that these limitations relating to the contextual data are considered non-functional descriptive material because these limitations merely describe the types or aspects of data used by the system of independent claim 1 and dependent claim 6 with no further functional limitation of said claims, therefore these limitations are not given patentable weight. Nonetheless, see Lu ¶ [0041] – identifying synonyms for the words found in the input ERP data).
Regarding Claim 8, modified Lu teaches:
The system of claim 1, wherein the ERP information comprises a single word in the document (It is noted that these limitations relating to the ERP information are considered non-functional descriptive material because these limitations merely describe the types or aspects of data used by the system of independent claim 1 with no further functional limitation of said claim, therefore these limitations are not given patentable weight. Nonetheless, see Lu ¶ [0041] – identifying synonyms for the words found in the input ERP data based on single words such as “item” or “product”).
Regarding Claim 9, modified Lu teaches:
The system of claim 1, wherein the ERP information comprises a plurality of words in the document (It is noted that these limitations relating to the ERP information are considered non-functional descriptive material because these limitations merely describe the types or aspects of data used by the system of independent claim 1 with no further functional limitation of said claim, therefore these limitations are not given patentable weight. Nonetheless, see Lu ¶ [0050] – recognizing ERP information as a plurality of words representing a single entity such as “Apple Inc.” or “Banana Republic”).
Regarding Claim 10, modified Lu teaches:
The system of claim 1, wherein the second output data comprises one or more of (It is noted that these limitations relating to the second output data are considered non-functional descriptive material because these limitations merely describe the types or aspects of data used by the system of independent claim 1 with no further functional limitation of said claim, therefore these limitations are not given patentable weight. Nonetheless, see Lu below):
a confidence score indicating a confidence level as to whether the extracted ERP information constitutes vouching evidence for the ERP item (See Lu ¶ [0034] – machine learning models trained to generate an integrity assurance score [confidence score] of entity data extracted from a document [input] for similarity to values for respective entities from internal records, wherein said score is compared to a threshold to determine if sufficient data has been extracted [vouching evidence]);
Regarding Claim 11, modified Lu teaches:
The system of claim 1, wherein generating the second output data comprises generating a similarity score representing a comparison of the ERP information and the ERP item (See Lu ¶ [0019] – using an integrity assurance score as a similarity score for comparing ERP data from input sources to ERP data in knowledge storage).
Regarding Claim 12, modified Lu teaches:
The system of claim 11, wherein the similarity score is generated based on an entity graph representing contextual data (See Lu ¶ [0019] – using an integrity assurance score as a similarity score for comparing ERP data from input sources to ERP data in knowledge storage, including knowledge graphs/ Graph Computation as well as other contextual data formats).
Regarding Claim 13, modified Lu teaches:
The system of claim 1, wherein extracting the ERP information from the document comprises applying a fingerprinting operation to determine, based on the receive data representing an ERP item, a characteristic of a data extraction operation to be applied to the electronic document (As the spec uses fingerprinting to aid document understanding and vouching by using the context of ERP information, see Lu ¶ [0039-0041] – tokenizing various types of ERP data to obtain context information so that entities may be identified through context enriching techniques).
Regarding Claim 14, modified Lu teaches:
The system of claim 1, wherein applying the second set of one or more models is based at least in part on contextual data (See Lu ¶ [0041] – using categorizer/ classifier/ clustering models/ algorithms based on context data).
Regarding Claim 15, modified Lu teaches:
The system of claim 1, wherein applying the second set of one or more models comprises:
applying a set of document processing pipelines in parallel to generate a plurality of processing pipeline output data (As the spec describes pipelines as forms of data analysis, such as template-based analysis or templateless-based analysis, see Lu ¶ [0024] – using structured [template-based] and unstructured [templateless-based] data extraction and analysis across communication channels [pipelines] and [0026] – the data may also include real-time data, thereby showing data analysis occurring in parallel between a plurality of processing channels [pipelines]);
applying one or more data normalization operations to the plurality of processing pipeline output data to generate normalized data (See Lu ¶ [0050] – using a lemmatization [sorting/ normalization] process on input data to resolve entity conflicts or multiple names for a same entity); and
generating the second output data based on the normalized data (See Lu ¶ [0056] – generating tables of extracted ERP data that has been recognized [by sorting/ normalization] as a second output of normalizing said ERP data).
Regarding Claim 16, modified Lu teaches:
A non-transitory computer-readable storage medium storing instructions for determining whether data within an input electronic document constitutes vouching evidence for an enterprise resource planning (ERP) item (As the specification of the instant application [spec] uses vouching evidence to mean that there is sufficient data within a document to support the use of said document as reference material [evidence] in a verification process, such as an audit, of ERP data See Lu ¶ [0021-0024] – a system for extracting data from documents and predicting links between entities [items] using an assurance score threshold to determine if there is sufficient data to validate said extracted data from internal records, wherein said data relates to Enterprise Resource Planning and [0064] – operating on instructions stored in non-transitory computer readable medium), the instructions configured to be executed by a system comprising one or more processors to cause the system to (See Lu ¶ [0037] – one or more processors executing the data extraction and analysis process of Lu):
receive data representing an ERP item (See Lu ¶ [0024] – ERP data processing and [0027-0028] – various communications channels used to send and receive data that includes ERP items such as a price discount for a particular inventory item);
generate … data based on the received data representing an ERP item and one or more reference electronic documents (See Lu ¶ [0033] – entity relationship extractor receiving information regarding entities identified by the entity processor and determines a relationship between various entities using link prediction methodologies, thereby showing predicted information content by example … The process extractor and reconstructor is configured to determine or reconstruct the process steps of the unstructured process based on the information obtained from or with the aid of the input [received data representing an ERP item by example] and stored in the knowledge storage [one or more reference electronic documents by example], [0037] – frame-based data extraction that examines entity document metadata to recognize whether data inputs are from a header, subject line, body or attachment [location] to an email when said input comprises an email and [0040-0041] – extracting first ERP data for an “item” and a second data recognizing the data source as the subject line of an email, thereby showing a location of data within a source document), wherein the … data comprises a predicted information content and a predicted location of the content in an electronic document not used to generate … data (As the specification of the instant application refers to content in an electronic document not used to generate hypothesis data as document content itself, see Lu ¶ [0033] – entity relationship extractor receiving information regarding entities identified by the entity processor and determines a relationship between various entities using link prediction methodologies, thereby showing predicted information content by example, [0037] – frame-based data extraction that examines entity document metadata to recognize whether data inputs are from a header, subject line, body or attachment [document content and location by example] to an email when said input comprises an email and [0040-0041] – extracting first ERP data for an “item” and a second data recognizing the data source as the subject line of an email, thereby showing a location of data within a source document);
receive the input electronic document (See Lu ¶ [0026] – receiving documents as inputs from electronic sources like text files, spreadsheets, word processor files, etc.);
extract ERP information from the input electronic document (See Lu ¶ [0031] – analyzing and storing data extracted from the various input sources), wherein extracting the ERP information comprises generating first data representing information content of the ERP information and second data representing a document location for the ERP information (See Lu ¶ [0037] – frame-based data extraction that examines document metadata to recognize whether data inputs are from a header, subject line, body or attachment [location] to an email when said input comprises an email and [0040-0041] – extracting first ERP data for an “item” and a second data recognizing the data source as the subject line of an email, thereby showing a location of data within a source document);
determine one or more spatial relationships between a plurality of entities included in the extracted ERP information (See Lu ¶ [0037] – frame-based data extraction that examines document metadata structure [spatial relationships] to recognize whether data inputs are from a header, subject line, body or attachment [location] to an email when said input comprises an email, [0038] - the document analyzer parses and tokenizes the documents to generate tokens which are discrete units of text data that are delimited by one or more of spaces or symbols in the documents. Thus, a sentence including words may be tokenized so that each token corresponds to a word or a symbol. A token selector is also included in the entity processor for discarding tokens corresponding to stop words, whitespaces and the like so that tokens including meaningful entity names and values thereof are selected for further processing [spatial relationships between a plurality of entities by example] and Figs. 9A and 9B – showing different ERP data in different locations of an email document):
generate a graph data structure representing the one or more spatial relationships between the plurality of entities (See Lu ¶ [0038] - the document analyzer parses and tokenizes the documents to generate tokens which are discrete units of text data that are delimited by one or more of spaces or symbols in the documents. Thus, a sentence including words may be tokenized so that each token corresponds to a word or a symbol. A token selector is also included in the entity processor for discarding tokens corresponding to stop words, whitespaces and the like so that tokens including meaningful entity names and values thereof are selected for further processing [spatial relationships between the plurality of entities by example], [0042] – Graphical models which encode dependencies between variables via representing the variables as nodes in a graph and dependencies between the variables as edges of the graph can be employed to infer the entity relationships and Figs. 11A – showing relationships between different ERP data nodes linked graphically);
apply a first set of one or more models to the … data and to extracted ERP information in order to generate first output data indicating whether the extracted ERP information constitutes vouching evidence for the ERP item (See Lu ¶ [0039-0041] – using category models/ algorithms to recognize that an email referring to an “item” is referencing a “product” from an inventory database based on a stored dictionary/ corpus of ERP information [data]);
apply a second set of one or more models to the extracted ERP information in order to generate second output data indicating whether the extracted ERP information constitutes vouching evidence for the ERP item (See Lu ¶ [0034] – machine learning models trained to generate an integrity assurance score [confidence score] of entity data extracted from a document [input] for similarity to values for respective entities from internal records, wherein said score is compared to a threshold to determine if sufficient data has been extracted [vouching evidence]),
wherein applying at least one of the first set of one or more models to generate the first output data and the second set of one or more models to generate the second output data is based on the graph structure representing the spatial relationships between the plurality of entities included in the extracted ERP information (See Lu ¶ [0037] – frame-based data extraction that examines document metadata structure [spatial relationships] to recognize whether data inputs are from a header, subject line, body or attachment [location] to an email when said input comprises an email, [0038] – as noted above and Figs. 9A and 9B – showing different ERP data in different locations of an email document), and
wherein at least one of the first output data and the second output data comprises a binary indication as to whether the extracted ERP information constitutes vouching evidence for the ERP item and a location within the electronic document corresponding to the determination as to whether the extracted ERP information constitutes vouching evidence for the ERP item (See Lu ¶ [0037] – frame-based data extraction that examines document metadata to recognize whether data inputs are from a header, subject line, body or attachment [location] to an email when said input comprises an email, [0040-0041] – extracting first ERP data for an “item” and a second data recognizing the data source as the subject line of an email, thereby showing a location of data within a source document and [0061] - entity values may be compared to internal record documents which may be stored in data sources in order to obtain the integrity assurance score. Based on the comparison of the integrity assurance score with an assurance score threshold, a process integrity report including the validity or invalidity of the corresponding unstructured process would be generated, wherein the validity or invalidity is the binary indication); and
determine whether the extracted ERP information constitutes vouching evidence for the ERP item, based on the first output data and the second output data (See Lu ¶ [0035] – aggregating process integrity evaluation tasks [second output] and unstructured processes that were determined to be valid or inconsistent [first output] and [0046] - to determine the authenticity of the entity values contained in the data structures, the assurance score threshold may be statistically determined based on observed data regarding correlation between the scores and accuracy of the audit results. For example, the assurance score threshold may be set at 50% so that an assurance score of greater than 50% indicates that the unstructured process and data generated from the unstructured process is accurate, thereby constituting vouching evidence for the ERP item by example).
While Lu teaches a system for extracting ERP data from electronic documents to generate prediction data based on relationships of document metadata from said extracted ERP data by example, (Lu ¶ [0033], [0037-0038] and [0040-0041]), Lu does not explicitly teach that said prediction data is hypothesis data. This is taught by Li (See Li ¶ [0087] - a hypothesis may be generated indicative of an explanation of certain items being non-reconcilable). It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include in the data extraction and prediction generating system of Lu the use of hypothesis data as taught by Li to enhance accuracy and minimize external intervention (Li ¶ [0048]), thereby improving the accuracy and efficiency of Lu’s data extraction and prediction generating system.
Regarding Claim 17, modified Lu teaches:
A method for determining whether data within an electronic document constitutes vouching evidence for an enterprise resource planning (ERP) item (As the specification of the instant application [spec] uses vouching evidence to mean that there is sufficient data within a document to support the use of said document as reference material [evidence] in a verification process, such as an audit, of ERP data See Lu ¶ [0021-0024] – a system for extracting data from documents and predicting links between entities [items] using an assurance score threshold to determine if there is sufficient data to validate said extracted data from internal records, wherein said data relates to Enterprise Resource Planning), wherein the method is performed by a system comprising one or more processors, the method comprising (See Lu ¶ [0037] – one or more processors executing the data extraction and analysis process of Lu):
receiving data representing an ERP item (See Lu ¶ [0024] – ERP data processing and [0027-0028] – various communications channels used to send and receive data that includes ERP items such as a price discount for a particular inventory item);
generate … data based on the received data representing an ERP item and one or more reference electronic documents (See Lu ¶ [0033] – entity relationship extractor receiving information regarding entities identified by the entity processor and determines a relationship between various entities using link prediction methodologies, thereby showing predicted information content by example … The process extractor and reconstructor is configured to determine or reconstruct the process steps of the unstructured process based on the information obtained from or with the aid of the input [received data representing an ERP item by example] and stored in the knowledge storage [one or more reference electronic documents by example], [0037] – frame-based data extraction that examines entity document metadata to recognize whether data inputs are from a header, subject line, body or attachment [location] to an email when said input comprises an email and [0040-0041] – extracting first ERP data for an “item” and a second data recognizing the data source as the subject line of an email, thereby showing a location of data within a source document), wherein the … data comprises a predicted information content and a predicted location of the content in an electronic document not used to generate … data (As the specification of the instant application refers to content in an electronic document not used to generate hypothesis data as document content itself, see Lu ¶ [0033] – entity relationship extractor receiving information regarding entities identified by the entity processor and determines a relationship between various entities using link prediction methodologies, thereby showing predicted information content by example, [0037] – frame-based data extraction that examines entity document metadata to recognize whether data inputs are from a header, subject line, body or attachment [document content and location by example] to an email when said input comprises an email and [0040-0041] – extracting first ERP data for an “item” and a second data recognizing the data source as the subject line of an email, thereby showing a location of data within a source document);
receive the input electronic document (See Lu ¶ [0026] – receiving documents as inputs from electronic sources like text files, spreadsheets, word processor files, etc.);
extract ERP information from the input electronic document (See Lu ¶ [0031] – analyzing and storing data extracted from the various input sources), wherein extracting the ERP information comprises generating first data representing information content of the ERP information and second data representing a document location for the ERP information (See Lu ¶ [0037] – frame-based data extraction that examines document metadata to recognize whether data inputs are from a header, subject line, body or attachment [location] to an email when said input comprises an email and [0040-0041] – extracting first ERP data for an “item” and a second data recognizing the data source as the subject line of an email, thereby showing a location of data within a source document);
determine one or more spatial relationships between a plurality of entities included in the extracted ERP information (See Lu ¶ [0037] – frame-based data extraction that examines document metadata structure [spatial relationships] to recognize whether data inputs are from a header, subject line, body or attachment [location] to an email when said input comprises an email, [0038] - the document analyzer parses and tokenizes the documents to generate tokens which are discrete units of text data that are delimited by one or more of spaces or symbols in the documents. Thus, a sentence including words may be tokenized so that each token corresponds to a word or a symbol. A token selector is also included in the entity processor for discarding tokens corresponding to stop words, whitespaces and the like so that tokens including meaningful entity names and values thereof are selected for further processing [spatial relationships between a plurality of entities by example] and Figs. 9A and 9B – showing different ERP data in different locations of an email document):
generating a graph data structure representing the one or more spatial relationships between the plurality of entities (See Lu ¶ [0038] - the document analyzer parses and tokenizes the documents to generate tokens which are discrete units of text data that are delimited by one or more of spaces or symbols in the documents. Thus, a sentence including words may be tokenized so that each token corresponds to a word or a symbol. A token selector is also included in the entity processor for discarding tokens corresponding to stop words, whitespaces and the like so that tokens including meaningful entity names and values thereof are selected for further processing [spatial relationships between the plurality of entities by example], [0042] – Graphical models which encode dependencies between variables via representing the variables as nodes in a graph and dependencies between the variables as edges of the graph can be employed to infer the entity relationships and Figs. 11A – showing relationships between different ERP data nodes linked graphically);
apply a first set of one or more models to the … data and to extracted ERP information in order to generate first output data indicating whether the extracted ERP information constitutes vouching evidence for the ERP item (See Lu ¶ [0039-0041] – using category models/ algorithms to recognize that an email referring to an “item” is referencing a “product” from an inventory database based on a stored dictionary/ corpus of ERP information [data]);
apply a second set of one or more models to the extracted ERP information in order to generate second output data indicating whether the extracted ERP information constitutes vouching evidence for the ERP item (See Lu ¶ [0034] – machine learning models trained to generate an integrity assurance score [confidence score] of entity data extracted from a document [input] for similarity to values for respective entities from internal records, wherein said score is compared to a threshold to determine if sufficient data has been extracted [vouching evidence]),
wherein applying at least one of the first set of one or more models to generate the first output data and the second set of one or more models to generate the second output data is based on the graph structure representing the spatial relationships between the plurality of entities included in the extracted ERP information (See Lu ¶ [0037] – frame-based data extraction that examines document metadata structure [spatial relationships] to recognize whether data inputs are from a header, subject line, body or attachment [location] to an email when said input comprises an email, [0038] – as noted above and Figs. 9A and 9B – showing different ERP data in different locations of an email document), and
wherein at least one of the first output data and the second output data comprises a binary indication as to whether the extracted ERP information constitutes vouching evidence for the ERP item and a location within the electronic document corresponding to the determination as to whether the extracted ERP information constitutes vouching evidence for the ERP item (See Lu ¶ [0037] – frame-based data extraction that examines document metadata to recognize whether data inputs are from a header, subject line, body or attachment [location] to an email when said input comprises an email, [0040-0041] – extracting first ERP data for an “item” and a second data recognizing the data source as the subject line of an email, thereby showing a location of data within a source document and [0061] - entity values may be compared to internal record documents which may be stored in data sources in order to obtain the integrity assurance score. Based on the comparison of the integrity assurance score with an assurance score threshold, a process integrity report including the validity or invalidity of the corresponding unstructured process would be generated, wherein the validity or invalidity is the binary indication); and
determine whether the extracted ERP information constitutes vouching evidence for the ERP item, based on the first output data and the second output data (See Lu ¶ [0035] – aggregating process integrity evaluation tasks [second output] and unstructured processes that were determined to be valid or inconsistent [first output] and [0046] - to determine the authenticity of the entity values contained in the data structures, the assurance score threshold may be statistically determined based on observed data regarding correlation between the scores and accuracy of the audit results. For example, the assurance score threshold may be set at 50% so that an assurance score of greater than 50% indicates that the unstructured process and data generated from the unstructured process is accurate, thereby constituting vouching evidence for the ERP item by example).
While Lu teaches a system for extracting ERP data from electronic documents to generate prediction data based on relationships of document metadata from said extracted ERP data by example, (Lu ¶ [0033], [0037-0038] and [0040-0041]), Lu does not explicitly teach that said prediction data is hypothesis data. This is taught by Li (See Li ¶ [0087] - a hypothesis may be generated indicative of an explanation of certain items being non-reconcilable). It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include in the data extraction and prediction generating system of Lu the use of hypothesis data as taught by Li to enhance accuracy and minimize external intervention (Li ¶ [0048]), thereby improving the accuracy and efficiency of Lu’s data extraction and prediction generating system.
Response to Arguments
Applicant's arguments in the pre-appeal brief filed 12/29/2025 have been fully considered but they are not persuasive.
Rejection under 35 U.S.C. § 101:
In consideration of the applicant’s remarks asserting that the limitations of independent claim 1 (and similarly claims 16 and 17) recite reflection of a technological solution to a technological problem that improves computer processing of electronic documents with varied layouts, the rejection under 35 U.S.C. § 101 is maintained. The functional steps of claim 1 (and similarly claims 16 and 17), individually and as a whole, remain as executed by technical elements disclosed at a high level of generality such that said claim amounts to not more than computer implementation of the abstract idea noted above in the current rejection under 35 U.S.C. § 101. Therefore, any improvement shown in the claim is to the abstract idea itself and not to the underlying technology. The applicant is further reminded that the specification of an instant application is not read into the claims during examination and any improvement must be clearly reflected in the claim limitations.
Further, the current rejection under 35 U.S.C. § 101 regarding Step 2B has been corrected to remove technical elements that were not relevant to the instant application and revised to show how the current limitations
Independent claims 16 and 17 are similar in scope to independent claim 1 and remain rejected under 35 U.S.C. § 101 for the same reasons as noted above regarding claim 1. Dependent claims 3 and 6-15 also do not cure the deficiencies or their respective independent claims and do not show significantly more than an abstract idea as noted above in the current rejection under 35 U.S.C. § 101.
Rejection under 35 U.S.C. § 102:
In response to the applicant’s remarks, the previous rejection under 35 U.S.C. § 102 is withdrawn because the prior art reference of record, Lu, does not explicitly teach the generation of hypothesis data as required by the claim limitations as they are currently disclosed. However, as noted above in the current rejection under 35 U.S.C. § 103 regarding independent claims 1, 16 and 17, this functionality is taught by the combination of Lu and Li.
Contrary to the applicant’s assertion that Lu fails to disclose “determining one or more spatial relationships between a plurality of entities included in the extracted ERP information” and “generating a graph data structure representing the one or more spatial relationships between the plurality of entities”, as shown in claim 1, Lu does in fact teach these limitations. While the applicant argues against previously cited ¶ [0037] and [0042] of Lu, ¶ [0038] has been added to these citations to show teaching of spatial relationships between a plurality of entities by example as detailed above in the current rejection under 35 U.S.C. § 103. Moreover, the applicant’s arguments against Lu are based on an interpretation of said spatial relationships between a plurality of entities that is not required by these limitations, because the applicant’s interpretation is based on reading the specification of the instant application into the claim limitations. This is not done during examination.
Independent claims 16 and 17 comprise a similar scope as independent claim 1 and remain rejected for the same reasons noted above regarding claim 1. Dependent claims 3 and 6-15 also remain rejected for their dependency on their respective independent claims and for the reasons discussed above in the current rejection under 35 U.S.C. § 103.
The applicant is generally reminded that prior art must be considered in its entirety (MPEP 2141.02 (VI)).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Schaefer et al. (US 2022/0097228 A1) describes a system using optical character recognition on hand-written documents to digitize data for enterprise resource planning and Ponniah et al. (US 2020/0034842 A1) using binarized vectoring on extracted enterprise resource planning data.
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/MATTHEW S WERONSKI/ Examiner, Art Unit 3627
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