Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Claims 1-20 are pending in this office action. This action is responsive to Applicant’s application filed 02/17/2025.
Priority
3. Applicant’s claim for the benefit of a Continuation of 18378948 , filed 10/11/2023 ,now U.S. Patent # 12254280 claims foreign priority to 202311027147, filed 04/12/2023 is acknowledged.
Since the Continuation application relied on part of the priority document (Continuation), the claim of priority will be considered on a claim-by-claim basis. The priority date of the instant application is at least 02/17/2025 (the filing date), but depending upon the specific material claimed, could be as early as 04/12/2023.
Information Disclosure Statement
4. The references listed in the IDS filed 03/11/2025, 11/11/2025, and 01/15/2026 has been considered. A copy of the signed or initialed IDS is hereby attached.
.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
5. Claims 1-20 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,254,280. Although the conflicting claims are not identical, they are not patentably distinct from each other because they are substantially similar in scope and they use the same limitations.
The following table shows the claims 1-20 in Instant Application that are rejected by corresponding claim(s) 1-20 in US Patent No. 12,254,280.
Instant Application
US 12,254,280
A method comprising:
assigning, by one or more processors, a weight to each of document content concepts in a document, wherein the document content concepts include synonyms to respective document content concepts;
scoring, by the one or more processors, the document with a classification score based on a weight of each of the document content concepts and weights of one or more languages from different countries in the document;
searching, by the one or more processors, in a region for data relevant to an object within a pattern in the document;
classifying, by the one or more processors, the document based on the classification score; and
assigning, by the one or more processors, and based on the classifying, the document to at least one of a release report in response to the document being a valid document or an exemption report in response to the document being a rejected document.
2. The method of claim 1, further comprising determining, by the one or more processors, at least one of:
that the classification score meets a threshold;
the pattern in the document; or
the object within the pattern in the document.
3. The method of claim 1, further comprising creating, by the one or more processors, the region around the object using x-y coordinates.
4. The method of claim 1, further comprising
determining, by the one or more processors, the document content concepts in the document from a bag of words associated with the document, wherein the document content concepts include an attribute of a type of the document.
5. The method of claim 4, further comprising determining, by the one or more processors, that one or more rejected keywords are not in the bag of words.
6. The method of claim 1, further comprising determining, by the one or more processors, that the synonyms are associated with the document content concepts, wherein the synonyms include at least one of a wording variation, abbreviation or acronym of the document content concepts that is used to link the synonyms to the document content concepts.
7. The method of claim 1, further comprising
linking, by the one or more processors, synonyms to the document content concepts.
8. The method of claim 1, further comprising determining, by the processor,
that the document lacks at least one of personal health information or personal credit information.
9. The method of claim 1, further comprising avoiding, by the one or more processors, portions of the document based on an opt-out request.
10. The method of claim 9, wherein the opt-out request includes avoiding at least one of a terms and conditions (T&C) block of text in the document or avoiding a phrase in the document.
11. The method of claim 1, further comprising at least one of:
receiving, by a processor, the document;
validating, by the processor, at least one of a sender or a receiver of the document;
determining, by the processor, that a number of pages in the document is less than a threshold number of pages;
conducting, by the processor, optical character recognition (OCR) of the document;
storing, by the one or more processors, the bag of words associated with the document, based on the OCR of the document; or
determining, by the processor, that the document exceeds an image clarity threshold.
12. The method of claim 1, wherein the assigning the weight includes combining the synonyms of each document content concept into the document content concept, then assigning the weight to the document content concept.
13. The method of claim 1, wherein the supplier provides the rejected keywords.
14. The method of claim 1, further comprising sending, by the one or more processors, a
data packet with data about contents of the document to at least one of a procurement system to initiate a procurement process or an accounts receivable system to initiate an accounts receivable process.
15. The method of claim 1, further comprising determining, by the one or more processors, that the document includes a pre-determined format.
16. The method of claim 1, further comprising detecting, by the one or more processors, one or more tables in the document, wherein the classifying of the document is further based on the document containing the one or more tables.
17. The method of claim 1, further comprising determining, by the one or more processors, a country of origin of the document based on at least one of a fax number or email address associated with the document.
18. The method of claim 1, further comprising performing, by the one or more processors, a different process on the document based on one or more rejected keywords.
19. An article of manufacture including one or more non-transitory, tangible computer readable storage mediums having instructions stored thereon that, in response to execution by one or more processors, cause the one or more processors to perform operations comprising:
assigning, by the one or more processors, a weight to each of document content concepts in a document, wherein the document content concepts include synonyms to respective document content concepts;
scoring, by the one or more processors, the document with a classification score based on a weight of each of the document content concepts and weights of one or more languages from different countries in the document;
searching, by the one or more processors, in a region for data relevant to an object within a pattern in the document;
classifying, by the one or more processors, the document based on the classification score; and
assigning, by the one or more processors, and based on the classifying, the document to at least one of a release report in response to the document being a valid document or an exemption report in response to the document being a rejected document.
20. A system comprising:
one or more processors; and
one or more tangible, non-transitory memories configured to communicate with the one or more processors, the one or more tangible, non-transitory memories having instructions stored thereon that, in response to execution by the one or more processors, cause the one or more processors to perform operations comprising:
assigning, by the one or more
processors, a weight to each of document content concepts in a document, wherein the document content concepts include synonyms to respective document content concepts;
scoring, by the one or more processors, the document with a classification score based on a weight of each of the document content concepts and weights of one or more languages from different countries in the document;
searching, by the one or more processors, in a region for data relevant to an object within a pattern in the document;
classifying, by the one or more processors, the document based on the classification score; and
assigning, by the one or more processors, and based on the classifying, the document to at least one of a release report in response to the document being a valid document or an exemption report in response to the document being a rejected document.
1. A method comprising:
detecting, by one or more processors, one or more languages from different countries in a document;
assigning, by the one or more processors, a weight to the one or more languages from different countries;
determining, by the one or more processors, document content concepts in the document from a bag of words associated with the document, wherein the document content concepts include an attribute of a type of document;
determining, by the one or more processors, that synonyms are associated with the document content concepts, wherein the synonyms include at least one of a wording variation, abbreviation or acronym of the document content concepts that is used to link the synonyms to the document content concepts;
linking, by the one or more processors, the synonyms to the document content concepts;
assigning, by the one or more processors, a weight to each of the document content concepts, wherein the document content concepts include the synonyms to the respective document content concepts;
scoring, by the one or more processors, the document with a classification score based on the weight of each of the document content concepts and the weights of the one or more languages from different countries in the document;
determining, by the one or more processors, that the classification score meets a threshold;
determining, by the one or more processors, a pattern in the document;
determining, by the one or more processors, an object within the pattern in the document;
creating, by the one or more processors, a region around the object using x-y coordinates;
searching, by the one or more processors, in the region for data relevant to the object;
determining, by the processor, that the document lacks personal health information;
determining, by the processor, that the document lacks personal credit information;
determining, by the one or more processors, that one or more rejected keywords are not in the bag of words;
avoiding, by the one or more processors, portions of the document based on an opt-out request;
classifying, by the one or more processors, the document based on the classification score; and
assigning, by the one or more processors, and based on the classifying, the document to at least one of a release report in response to the document being a valid document or an exemption report in response to the document being a rejected document.
(From claim 1)
determining, by the one or more processors,
that the classification score meets a threshold;
determining, by the one or more processors,
a pattern in the document;
determining, by the one or more processors, an object within the pattern in the document;
(from claim 1)
creating, by the one or more processors, a region around the object using x-y coordinates;
(from claim 1)
determining, by the one or more processors, document content concepts in the document from a bag of words associated with the document, wherein the document content concepts include an attribute of a type of document;
(from claim 1)
determining, by the one or more processors, that one or more rejected keywords are not in the bag of words;
(from claim 1)
determining, by the one or more processors, that synonyms are associated with the document content concepts, wherein the synonyms include at least one of a wording variation, abbreviation or acronym of the document content concepts that is used to link the synonyms to the document content concepts;
(from claim 1)
linking, by the one or more processors, the synonyms to the document content concepts;
16. The method of claim 1, further comprising reviewing, by the one or more processors, regions of the document with patterns of data related to at least one of the personal health information or the personal credit information for the determining that the document lacks at least one of the personal health information or the personal credit information.
(from claim 1)
avoiding, by the one or more processors, portions of the document based on an opt-out request;
9. The method of claim 1, wherein the opt-out request includes avoiding at least one of a terms and conditions (T&C) block of text in the document or avoiding a phrase in the document.
2. The method of claim 1, further comprising at least one of:
receiving, by a processor, the document;
validating, by the processor, at least one of a sender or a receiver of the document;
determining, by the processor, that a number of pages in the document is less than a threshold number of pages;
conducting, by the processor, optical character recognition (OCR) of the document;
storing, by the one or more processors, the bag of words associated with the document, based on the OCR of the document; or
determining, by the processor, that the document exceeds an image clarity threshold.
3. The method of claim 1, wherein the assigning the weight includes combining the synonyms of each document content concept into the document content concept, then assigning the weight to the document content concept.
5. The method of claim 1, wherein the supplier provides the rejected keywords.
6. The method of claim 1, further comprising sending, by the one or more processors, a data packet with data about contents of the document to at least one of a procurement system to initiate a procurement process or an accounts receivable system to initiate an accounts receivable process.
8. The method of claim 1, further comprising determining, by the one or more processors, that the document includes a pre-determined format.
12. The method of claim 1, further comprising detecting, by the one or more processors, one or more tables in the document, wherein the classifying of the document is further based on the document containing the one or more tables.
14. The method of claim 1, further comprising determining, by the one or more processors, a country of origin of the document based on at least one of a fax number or email address associated with the document.
18. The method of claim 1, further comprising performing, by the one or more processors, a different process on the document based on the one or more rejected keywords.
19. An article of manufacture including one or more non-transitory, tangible computer readable storage mediums having instructions stored thereon that, in response to execution by one or more processors, cause the one or more processors to perform operations comprising:
detecting, by the one or more processors, one or more languages from different countries in a document;
assigning, by the one or more processors, a weight to the one or more languages from different countries;
determining, by the one or more processors, document content concepts in the document from a bag of words associated with the document, wherein the document content concepts include an attribute of a type of document;
determining, by the one or more processors, that synonyms are associated with the document content concepts, wherein the synonyms include at least one of a wording variation, abbreviation or acronym of the document content concepts that is used to link the synonyms to the document content concepts;
linking, by the one or more processors, the synonyms to the document content concepts;
assigning, by the one or more processors, a weight to each of the document content concepts, wherein the document content concepts include the synonyms to the respective document content concepts;
scoring, by the one or more processors, the document with a classification score based on the weight of each of the document content concepts and the weights of the one or more languages from different countries in the document;
determining, by the one or more processors, that the classification score meets a threshold;
determining, by the one or more processors, a pattern in the document;
determining, by the one or more processors, an object within the pattern in the document;
creating, by the one or more processors, a region around the object using x-y coordinates;
searching, by the one or more processors, in the region for data relevant to the object;
determining, by the processor, that the document lacks personal health information;
determining, by the processor, that the document lacks personal credit information; determining, by the one or more processors, that one or more rejected keywords are not in the bag of words;
avoiding, by the one or more processors, portions of the document based on an opt-out request;
classifying, by the one or more processors, the document based on the classification score; and
assigning, by the one or more processors, and based on the classifying, the document to at least one of a release report in response to the document being a valid document or an exemption report in response to the document being a rejected document.
20. A system comprising:
one or more processors; and
one or more tangible, non-transitory memories configured to communicate with the one or more processors, the one or more tangible, non-transitory memories having instructions stored thereon that, in response to execution by the one or more processors, cause the one or more processors to perform operations comprising:
detecting, by the one or more processors, one or more languages from different countries in a document;
assigning, by the one or more processors, a weight to the one or more languages from different countries;
determining, by the one or more processors, document content concepts in the document from a bag of words associated with the document, wherein the document content concepts include an attribute of a type of document;
determining, by the one or more processors, that synonyms are associated with the document content concepts, wherein the synonyms include at least one of a wording variation, abbreviation or acronym of the document content concepts that is used to link the synonyms to the document content concepts;
linking, by the one or more processors, the synonyms to the document content concepts;
assigning, by the one or more processors, a weight to each of the document content concepts, wherein the document content concepts include the synonyms to the respective document content concepts;
scoring, by the one or more processors, the document with a classification score based on the weight of each of the document content concepts and the weights of the one or more languages from different countries in the document;
determining, by the one or more processors, that the classification score meets a threshold;
determining, by the one or more processors, a pattern in the document;
determining, by the one or more processors, an object within the pattern in the document;
creating, by the one or more processors, a region around the object using x-y coordinates;
searching, by the one or more processors, in the region for data relevant to the object;
determining, by the processor, that the document lacks personal health information; determining, by the processor, that the document lacks personal credit information;
determining, by the one or more processors, that one or more rejected keywords are not in the bag of words;
avoiding, by the one or more processors, portions of the document based on an opt-out request;
classifying, by the one or more processors, the document based on the classification score; and
assigning, by the one or more processors, and based on the classifying, the document to at least one of a release report in response to the document being a valid document or an exemption report in response to the document being a rejected document.
Although the conflicting claims are not identical, they are not patentably distinct from each other because they are substantially similar in scope and they use the same limitations.
After analyzing the language of the claims, it is clear that claims 1-20 are merely an obvious variation of claims 1-20 of US Patent No. 12,253,280. It is clear that under the broadest reasonable interpretation of the claims. Therefore, these two sets of claims are not patentably distinct.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims under 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of 35 U.S.C. 103(c) and potential 35 U.S.C. 102(e), (f) or (g) prior art under 35 U.S.C. 103(a).
4. Claims 1-20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Lucas et al. (US Patent Publication No. 2020/0176098 A1, hereinafter “Lucas”) in view of Theile et al. (US Patent Publication No. 2022/0301070 A1, hereinafter “Theile”).
As to Claim 1, Lucas teaches the claimed limitations:
“A method comprising:” as a method includes the steps of determining a first concept from a text of a medical record from an electronic health record system, the first concept relating to a patient, identifying a match to the first concept in a first list of concepts (paragraph 0007).
“assigning, by one or more processors, a weight to each of document content concepts in a document, wherein the document content concepts include synonyms to respective document content concepts” as training may include providing optimized datasets, labeling these traits as they occur in patient records, and training the machine learning algorithm (MLA) to predict or classify based on new inputs. Artificial NNs are efficient computing models which have shown their strengths in solving hard problems in artificial intelligence. Some MLA may identify features of importance and identify a coefficient, or weight, to them. The coefficient may be multiplied with the occurrence frequency of the feature to generate a score, and once the scores of one or more features exceed a threshold, certain classifications may be predicted by the MLA. A coefficient schema may be combined with a rule-based schema to generate more complicated predictions, such as predictions based upon multiple features (paragraphs 0063, 0163).
“scoring, by the one or more processors, the document with a classification score based on a weight of each of the document content concepts and weights of one or more languages from different countries in the document” as a system is disclosed that identifies information in clinical documents or other records. The system may use a combination of text extraction techniques, text cleaning techniques, natural language processing techniques, machine learning algorithms, and medical concept identification, normalization, and structuring techniques. The system also maintains and utilizes a continuous collection of training data across clinical use cases that help to increase both accuracy and reliability of predictions specific to a patient record. The system accelerates a structuring of clinical data in a patient's record (paragraph 0024). These fields of medical data may identify concept candidates. In an embodiment, categorization of concept candidates may include: Performance Scores. In one example, Tylenol may be a concept candidate relating to the Medications or Outcomes category as a medication in treatment or a medication in outcomes (paragraphs 0042). Some MLA may identify features of importance and identify a coefficient, or weight, to them. The coefficient may be multiplied with the occurrence frequency of the feature to generate a score, and once the scores of one or more features exceed a threshold, certain classifications may be predicted by the MLA. A coefficient schema may be combined with a rule-based schema to generate more complicated predictions, such as predictions based upon multiple features. A list of coefficients may exist for the key features, and a rule set may exist for the classification. A rule set may be based upon the number of occurrences of the feature, the scaled weights of the features, or other qualitative and quantitative assessments of features encoded in logic known to those of ordinary skill in the art (paragraph 0063, 0155, 0161, 0163).
“searching, by the one or more processors, in a region for data relevant to an object within a pattern in the document” as the possible patterns of alternative splicing for a gene can be very complicated and the complexity increases rapidly as number of introns in a gene increases. In silico alternative splicing prediction may find large insertions or deletions within a set of mRNA sharing a large portion of aligned sequences by identifying genomic loci through searches of mRNA sequences against genomic sequences, extracting sequences for genomic loci and extending the sequences at both ends up to 20 kb, searching the genomic sequences extracting splicing pairs (paragraph 0048).
“classifying, by the one or more processors, the document based on the classification score” as the training data may be provisioned with the parts of speech assigned to words and the true classification for each patient. A machine learning algorithm or a neural network may process the training data to generate a rule set or a trained neural network, respectively. In an exemplary rule set, a list of words with corresponding weights may be generated based upon the frequency they appear in text with proper classification vs text without the proper classification. For example, a rule set for determining if a document for a patient is to be classified.
Lucas does not explicitly teach the claimed limitation “assigning, by the one or more processors, and based on the classifying, the document to at least one of a release report in response to the document being a valid document or an exemption report in response to the document being a rejected document”.
Theile teaches the insurance product may be disability (e.g., rejected) insurance. The plurality of specific drugs may include drugs used to treat serious medical conditions. The electronic processing element may confirm (e.g., valid) an occupation class assigned to the applicant based upon the occupation of the applicant, and may base the premium for the insurance product at least in part on the confirmed assigned occupation class. The electronic processing element may further obtain a motor vehicle report for the applicant based upon the identifier for the applicant, and may reject the applicant for the insurance product if the motor vehicle report shows that the applicant engages in one or more specific unsafe driving behaviors. The electronic processing element may further obtain an insurance information report for the applicant based upon the identifier for the applicant. This report may contain information on non-medical and medical conditions reported by other members who participate in the insurance information report, which may impact processing of the present products, a short-term disability product may be provided. Tools use to assess the risk on the short-term disability insurance may include consumer reports or other consumer database information that helps to mitigate anti-selection (abstract, paragraph 0008).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, having the teachings of Lucas and Theile before him/her, to modify Lucas document being a valid document or an exemption report in response to the document being a rejected document because that would an instant answer and online binding disability product provide to online customers, or the online customer experience may be enhanced as taught by Theile (paragraph 0005).
As to Claim 2, Lucas teaches the claimed limitations:
“determining, by the one or more processors, at least one of: that the classification score meets a threshold; the pattern in the document; or the object within the pattern in the document” as concept candidates may be determined by noting important phrase types and may be further refined by comparing any associated text against a list of weighted words, whereby words which are weighted above a threshold weight may be presented as concept candidate, the word Patient may be flagged as a concept candidate, but due to its low weighting factor, may be removed from the candidate list (paragraphs 0041, 0048, 0155, 0161, 0169, 0181,0187, 0252).
As to Claim 3, Lucas teaches the claimed limitations:
“creating, by the one or more processors, the region around the object using x-y coordinates” as (paragraphs 0153, 0161-0162, 0185).
As to Claim 4, Lucas teaches the claimed limitations:
“determining, by the one or more processors, the document content concepts in the document from a bag of words associated with the document, wherein the document content concepts include an attribute of a type of the document” as (abstract, paragraphs 0007-0009, 0047, 0155, 0169. 0187).
As to Claim 5, Lucas teaches the claimed limitations:
“determining, by the one or more processors, that one or more rejected keywords are not in the bag of words” as (paragraphs 0041, 0104-105, 0163-0166).
As to Claim 6, Lucas teaches the claimed limitations:
“determining, by the one or more processors, that the synonyms are associated with the document content concepts, wherein the synonyms include at least one of a wording variation, abbreviation or acronym of the document content concepts that is used to link the synonyms to the document content concepts” as (paragraphs 0029, 0045, 0048, 0175, 0183-0186, 0220).
As to Claim 7, Lucas teaches the claimed limitations:
“linking, by the one or more processors, synonyms to the document content concepts” as (paragraphs 0182-0186).
As to Claim 8, Lucas teaches the claimed limitations:
“determining, by the processor, that the document lacks at least one of personal health information or personal credit information” as (paragraphs 0003-0004,0230).
As to Claim 9, Lucas teaches the claimed limitations:
“avoiding, by the one or more processors, portions of the document based on an opt-out request” as (paragraphs 0041-0042, 0155).
As to Claim 10, Lucas teaches the claimed limitations:
“wherein the opt-out request includes avoiding at least one of a terms and conditions (T&C) block of text in the document or avoiding a phrase in the document” as (paragraphs 0028-0029, 0039, 0041, 0066-0067, 0074, 0076-0077, 0103-0105, 0119-0121, 0153-0156, 0163-0166, 0219-0225).
As to Claim 11, Lucas teaches the claimed limitations:
“at least one of: receiving, by a processor, the document; validating, by the processor, at least one of a sender or a receiver of the document; determining, by the processor, that a number of pages in the document is less than a threshold number of pages; conducting, by the processor, optical character recognition (OCR) of the document; storing, by the one or more processors, the bag of words associated with the document, based on the OCR of the document; or determining, by the processor, that the document exceeds an image clarity threshold” as (paragraphs 0095-0097, 0116-0118).
As to Claim 12, Lucas teaches the claimed limitations:
“wherein the assigning the weight includes combining the synonyms of each document content concept into the document content concept, then assigning the weight to the document content concept” as (paragraphs 0029, 0033, 0075, 0113, 0175, 0178, 0183-0184, 0186-0188, 0219-220).
As to Claim 13, Lucas teaches the claimed limitations:
“wherein the supplier provides the rejected keywords” as (paragraphs 0041, 0097, 0119, 0155, 0159-0164, 0242-0243; see also figure 4).
As to Claim 14, Lucas teaches the claimed limitations:
“sending, by the one or more processors, a data packet with data about contents of the document to at least one of a procurement system to initiate a procurement process or an accounts receivable system to initiate an accounts receivable process” as (paragraphs 0042, 0187).
Theile teaches (paragraphs 0020, 0068-0071).
As to Claim 15, Lucas teaches the claimed limitations:
“determining, by the one or more processors, that the document includes a pre-determined format” as (paragraphs 0034-0038, 0177-0178, 0181, 0249, 0253).
As to Claim 16, Lucas teaches the claimed limitations:
“detecting, by the one or more processors, one or more tables in the document, wherein the classifying of the document is further based on the document containing the one or more tables” as (paragraphs 0047-0049, 0063, 0106, 0113, 0160, 0169, 0174, 0185-0186, 0206, 0239, 0253).
As to Claim 17, Lucas teaches the claimed limitations:
“determining, by the one or more processors, a country of origin of the document based on at least one of a fax number or email address associated with the document” as (paragraphs 0106, 0187, 0277).
As to Claim 18, Lucas teaches the claimed limitations:
“performing, by the one or more processors, a different process on the document based on one or more rejected keywords” as (paragraphs 0018-0020,0041, 0048, 0060, 0072, 0079, 0085, 0093, 0102-0105, 0162).
As to claim 19 is rejected under 35 U.S.C 103(a), the limitations therein have substantially the same scope as claim 1. In addition, Lucas teaches the system provides mechanisms for automatically processing clinical documents in bulk, identifying and extracting key characteristics, and generating machine learning models that are refined and optimized through the use of continuous training data (paragraph 0006). Therefore, this claim is rejected for at least the same reasons as claim 1.
As to claim 20 is rejected under 35 U.S.C 103(a), the limitations therein have substantially the same scope as claim 1. In addition, Lucas teaches the system provides mechanisms for automatically processing clinical documents in bulk, identifying and extracting key characteristics, and generating machine learning models that are refined and optimized through the use of continuous training data (paragraph 0006). Therefore, this claim is rejected for at least the same reasons as claim 1.
Examiner’s Note
Examiner has cited particular columns/paragraph and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention. This will assist in expediting compact prosecution. MPEP 714.02 recites: “Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP § 2163.06. An amendment which does not comply with the provisions of 37 CFR 1.121(b), (c), (d), and (h) may be held not fully responsive. See MPEP § 714.” Amendments not pointing to specific support in the disclosure may be deemed as not complying with provisions of 37 C.F.R. 1.131(b), (c), (d), and (h) and therefore held not fully responsive. Generic statements such as “Applicants believe no new matter has been introduced” may be deemed insufficient.
Contact Information
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02/05/2026
/SHYUE JIUNN HWA/
Primary Examiner, Art Unit 2156