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 .
Detailed Actions
In amendments dated 5/8/26, Applicant amended claims 1, 4-6, 8-14, 16, and 18-20, canceled no claims, and added no new claims. Claims 1-20 are presented for examination.
Rejections under 35 U.S.C. 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to mental processes without significantly more. Independent claims 1, 19, and 20 each recites identifying a plurality of populations of subjects associated with the plurality of features; identifying, within the plurality of populations, sub-populations of subjects associated with the response or outcomes features; determining, based on the patient-centric knowledge graph, candidate feature subcombinations that exclude the established biomedical relationships of the background knowledge graph; and determining associations, based at least in part on the sub-populations or the plurality of populations, between the candidate feature subcombinations and the response or outcomes features. Identifying populations of subjects associated with the plurality of features, identifying sub-populations of subjects within the populations, determining candidate feature subcombinations, and determining associations between the candidate feature subcombinations and the response or outcomes features each recited broadly and involve evaluating and are mental processes. Each claim recites additional elements of receiving a plurality of features from a subject data store, including clinical features, therapeutic features, and at least one of response or outcomes features, an input step and insignificant extra-solution activity; constructing, based on at least a portion of the plurality of features, a patient-centric knowledge graph, which is storing data and also insignificant extra-solution activity; accessing a background knowledge graph representing established biomedical relationships, which is retrieving data and also insignificant extra-solution activity; and providing a summary of the determined associations, which is an output step and also insignificant extra-solution activity. Claim 19 recites a computer including a processing device and claim 20 recites a non-transitory computer-readable medium, which are each generic components of a computer system. Examiner notes specification paragraph 0002 states “there exists an unmet need in the biomedical market space for a platform that enables accelerated discovery of actionable knowledge.” Paragraph 0004 discusses how patient records exist in numerous formats and on different storage mediums and discusses difficulties in searching such records. Paragraph 0005 discusses how correlation analysis before more complex with the increase in the number of features being analyzed; and “what is needed is a more targeted generation process and/or filtering mechanism for directing the correlation discovery process to reduce the number of computations to include those which are most likely to reveal promising correlations;” and “what is needed is a directed generation process, filtering and ranking system and mechanism for prioritizing meaningful, undocumented biomarkers over spurious correlations which are already accepted in the field or containing relationships which are not meaningful as biomarkers for research, treatment, or other applications to particular diseases.” Paragraphs 0009-0014 discuss techniques in the invention to address these needs but such techniques are not claimed, also the claim steps are broad and do not recite a particular improvement in any technology or function of a computer per MPEP 2106.04(d) and do not recite any unconventional steps in the invention per MPEP 2106.05(a). Therefore, the recited mental processes are not integrated into a practical application. Taking the claims as a whole, the input steps and output step are each recited broadly and amount to sending and receiving data across a network per figure 1 and paragraph 0174, which are routine and conventional activities per the list of such activities in MPEP 2106.05(d) part II. The storing and retrieving data steps are also routine and conventional activities per the list in MPEP 2106.05(d) part II. The computer including a processing device and non-transitory computer-readable medium are each still generic components of a computer system. Thus the claims do not include additional elements that are sufficient to amount to significantly more than the recited mental processes.
Claim 2 recites wherein the method is implemented in conjunction with a large language model, which is applying the large language model and is not significantly more than a mental process per Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628). Claim 3 recites wherein subjects in respective populations of the plurality of populations have at least one feature in common, the at least one feature corresponding to a disease state, a therapy, a response, or an outcome, and subjects having features is data and a mental process accomplishable in the human mind or on paper. Claim 4 recites wherein identifying the sub-populations of subjects comprises associating one or more subjects within one or more of the plurality of populations with one or more other subjects using one or more of a prognostic, diagnostic, adverse effect, or therapeutic feature, and associating data is evaluating and a mental process. Claim 5 recites wherein identifying the sub-populations of subjects comprises associating one or more subjects within one or more of the plurality of populations with one or more other subjects based on response to a therapy or based on length of time between a therapy and a subsequent event, and associating data is evaluating and a mental process.
Claim 6 recites determining a likelihood of correlation among candidate subcombinations with respect to one or more of a disease state, a therapy, a response, or an outcome, and determining a likelihood is evaluating and a mental process; and selecting candidate relationships having the greatest likelihood of correlation, and selecting relationships is evaluating and a mental process. Claim 7 recites wherein the summary comprises a ranked list of the determined associations, and a summary comprising data is a mental process accomplishable in the human mind or on paper. Claim 8 recites further comprising semantically relating entities in a the patient-centric knowledge graph, wherein a relationship for at least a subset of the semantically related entities comprises a temporal element, and relating entities is recited broadly and is a mental process accomplishable in the human mind or on paper. Claim 9 recites inputting the patient-centric knowledge graph into a neural network, the associations between the candidate feature subcombinations and the response or outcomes features comprising outputs of the neural network, and inputting data into a neural network is a mental process accomplishable in the human mind or on paper.
Claims 10 and 13 each recites wherein: the background knowledge graph is associated with a background knowledge data store, and storing data is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II. Claim 11 recites receiving features from a plurality of background knowledge data stores, which is recited broadly and amounts to receiving data across a network per figure 1 and paragraph 0174, which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II; and ranking features based on which of the plurality of background knowledge data stores they are received from, and ranking features is recited broadly and is a mental process accomplishable in the human mind or on paper. Claim 12 recites referencing a plurality of established biomedical relationships to identify first biomedical relationships to be excluded from the determined candidate feature subcombinations, and referencing biomedical relationships to identify biomedical relationships is evaluating and a mental process; and excluding biomedical relationships from the determined candidate feature subcombinations that are also present within the first biomedical relationships, which is recited broadly and a mental process accomplishable in the human mind or on paper.
Claim 14 recites categorizing the plurality of features as source-type features and target-type features, which is recited broadly and is a mental process accomplishable in the human mind or on paper; and aggregating features, wherein the aggregation includes at least one source-type feature and at least one target-type feature from identified features of the plurality of features, which is recited broadly and is a mental process accomplishable in the human mind or on paper. Claim 15 recites wherein the aggregating is based at least in part on embeddings generated from the aggregation of features including the at least one source-type feature and the at least one target-type feature, which is recited broadly and is a mental process accomplishable in the human mind or on paper.
Claim 16 recites determining confidence scores, based at least in part on one or more of the plurality of populations or the sub-populations, of the determined associations between the candidate feature subcombinations and the response or outcomes features, and determining a confidence score is evaluating and a mental process; and ranking the confidence scores, and ranking is recited broadly and is a mental process accomplishable in the human mind or on paper. Claim 17 recites wherein providing the summary of the determined associations comprises providing at least a set of the ranked confidence scores and relationships identified from the generated embeddings, and providing scores and relationships is recited broadly and amounts to receiving data across a network per figure 1 and paragraph 0174, which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II. Claim 18 recites wherein the source-type features and target-type features comprise semantically related entities in the patient-centric knowledge graph, wherein a relationship for at least a subset of the semantically related entities further comprises a temporal element, and features are data and a mental process accomplishable in the human mind or on paper.
Rejections under 35 U.S.C. 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, 4-7, 10, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Barnes et al (US 20230352151), hereafter Barnes, in view of Hahm et al (US 12,694,948), hereafter Hahm, and further in view of Masters et al (US 20220003785) hereafter Masters.
With respect to claims 1, 19, and 20, Barnes teaches:
receiving a plurality of features from a subject data store, including clinical features, therapeutic features, and at least one of response or outcomes features (paragraph 0016 images with features from clinical trial data and biological samples of patients, features include drug treatments, outcome data);
identifying a plurality of populations of subjects associated with the plurality of features (paragraphs 0010, 0016 identify cohorts associated with drug or treatment protocols); and
identifying, within the plurality of populations, sub-populations of subjects associated with the response or outcomes features (paragraph 0018 stratifying patient cohorts, paragraph 0022 association with patient outcome data).
Barnes does not teach:
constructing, based on at least a portion of the plurality of features, a patient-centric knowledge graph;
accessing a background knowledge graph representing established biomedical relationships;
determining, based on the patient-centric knowledge graph, candidate feature subcombinations that exclude the established biomedical relationships of the background knowledge graph;
determining associations, based at least in part on the sub-populations or the plurality of populations, between the candidate feature subcombinations and the response or outcomes features; and
providing a summary of the determined associations.
Hahm teaches:
constructing, based on at least a portion of the plurality of features, a patient-centric knowledge graph (columns 234-235 lines 54-3 upload patient’s genetic data, medical history data to correlate with knowledge graph, column 235 lines 4-20 generate a knowledge graph of the patient/subject data (disease drug dosage, medical records EMR));
accessing a background knowledge graph representing established biomedical relationships (columns 232-233 lines 55-25 background knowledge graph build and accessed with genome, disease, treatment/outcome data and relationships);
determining, based on the patient-centric knowledge graph, candidate feature subcombinations that exclude the established biomedical relationships of the background knowledge graph (column 242 lines 29-46 use knowledge graph to determine alternate treatments as subcombinations of disease and treatment (excluding established treatments from the knowledge graph)); and
determining associations, based at least in part on the sub-populations or the plurality of populations, between the candidate feature subcombinations and the response or outcomes features (column 242 lines 29-46 determine an association between populations associated with features in knowledge graphs and subcombinations for patient outcome).
It would have been obvious to have combined the techniques for data analysis in Barnes with the knowledge graph functions in Hahm to establish a baseline for research with existing medical knowledge and determining the best possible treatments/outcomes for the patient.
The combination of Barnes and Hahm does not teach providing a summary of the determined associations. Masters teaches this with groupings summarized in a ranked list (Table 6) for acute exacerbations in a time period according to a treatment (paragraphs 0315-0322 Example 7). It would have been obvious to have combined the techniques for data analysis in Barnes and the knowledge graph functions in Hahm with the providing a summary of data in Masters as Masters is in the same field of endeavor of studies of subject outcomes for diseases and treatments, and the combination would provide useful information to a user about results of a study.
Regarding claim 19, Barnes teaches a computer including a processing device (paragraph 0052 figure 1 14).
Regarding claim 20, Barnes teaches a non-transitory computer-readable medium (paragraphs 0020, 0054 figure 2 201).
With respect to claim 4, all the limitations in claim 1 are addressed by Barnes, Hahm, and Masters above. Barnes also teaches wherein identifying the sub-populations of subjects comprises associating one or more subjects within one or more of the plurality of populations with one or more other subjects using one or more of a prognostic, diagnostic, adverse effect, or therapeutic feature (paragraph 0018 uses diagnostic feature metric).
With respect to claim 5, all the limitations in claim 1 are addressed by Barnes, Hahm, and Masters above. Barnes also teaches wherein identifying the sub-populations of subjects comprises associating one or more subjects within one or more of the plurality of populations with one or more other subjects based on response to a therapy or based on length of time between a therapy and a subsequent event (paragraphs 0059-0060 associating patients with other patients based on response to cancer therapy(survival rate), length of time between treatment (therapy) and reoccurrence/death (event)).
With respect to claim 6, all the limitations in claim 1 are addressed by Barnes, Hahm, and Masters above. Barnes also teaches wherein determining the associations between the candidate feature subcombinations comprises: determining a likelihood of correlation among the candidate feature subcombinations with respect to one or more of a disease state, a therapy, a response, or an outcome (paragraphs 0006-0007 likelihood of success among patients for therapy tested in Phase III trials); and
selecting candidate relationships having the greatest likelihood of correlation (paragraphs 0006-0007 selecting candidates based on greatest success with therapy (effectiveness, success of Phase III trials)).
With respect to claim 7, all the limitations in claim 1 are addressed by Barnes, Hahm, and Masters above. Barnes does not teach wherein the summary comprises a ranked list of the determined associations. Masters teaches this in Table 6 showing a summary of in a ranked list (Table 6) for acute exacerbations in a time period according to a treatment (paragraphs 0315-0322 Example 7).
Claims 2 and 3 are rejected under 35 U.S.C. 103 as being unpatentable over Barnes, Hahm, and Masters in further view of Boussios et al (US 20240153647), hereafter Boussios.
With respect to claim 2, all the limitations in claim 1 are addressed by Barnes, Hahm, and Masters above. The combination of Barnes, Hahm, and Masters does not teach wherein the method is implemented in conjunction with a large language model. Boussios teaches this in using a neural language model such as Word2Vec for identifying co-occurrence relations of medical codes using patient histories (paragraph 0082). It would have been obvious to have combined the use of a language model in Boussios with the analysis techniques in Barnes, Hahm, and Masters to look for matches in codes for patients and provide greater accuracy with modeling.
With respect to claim 3, all the limitations in claim 1 are addressed by Barnes, Hahm, and Masters above. The combination of Barnes, Hahm, and Masters does not teach wherein subjects in respective populations of the plurality of populations have at least one feature in common, the at least one feature corresponding to a disease state, a therapy, a response, or an outcome. Boussios teaches this in using a neural language model such as Word2Vec for identifying co-occurrence relations of medical codes (features in common) using patient histories (paragraph 0082).
With respect to claim 10, all the limitations in claim 1 are addressed by Barnes, Hahm, and Masters above. Hahm also teaches the background knowledge graph is associated with a background knowledge data store (column 239 lines 4-12 knowledge graph associated with a database, columns 234-235 lines 54-3 knowledge graph associated with pre-existing knowledge graph, column 233 lines 26-36 and column 234 lines 10-33 knowledge graph associated with data from other sources).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Barnes, Hahm, and Masters in further view of Russell et al (US 10,997,244), hereafter Russell.
With respect to claim 8, all the limitations in claim 1 are addressed by Barnes, Hahm, and Masters above. The combination of Barnes, Hahm, and Masters does not teach semantically relating entities in the patient-centric knowledge graph, wherein a relationship for at least a subset of the semantically related entities comprises a temporal element. Russell teaches this in searching metaset databases in knowledge graphs that use semantic searching to determine matches and finds matches on temporal attributes (column 12 lines 15-46). It would have been obvious to have combined the function of semantically relating entities in a knowledge base in Russell with the analysis techniques in Barnes, Hahm, and Masters top provide more information for a user about the analyses performed, making the combination more user-friendly.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Barnes, Hahm, Masters, and Russell and further in view of Prathap et al (US 20220366331), hereafter Prathap.
With respect to claim 9, all the limitations in claims 1 and 8 are addressed by the combination of Barnes, Hahm, Masters, and Russell above. The combination of Barnes, Hahm, Masters, and Russell does not teach inputting the patient-centric knowledge graph into a neural network, the associations between the candidate feature subcombinations and response or outcomes features comprising outputs of the neural network. Prathap teaches this in a semantic parser for entities in a database which are fed into a neural network to get matches output (paragraph 0041). It would have bene obvious to have combined the function of inputting a knowledge base into a neural network as in Prathap with the analysis techniques in Barnes, Hahm, Masters, and Russell to provide more information for the user, making the combination more user-friendly, and to use the network for more accuracy of results.
Claim 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Barnes, Hahm, and Masters and further in view of Ahmad et al (US 20200258629), hereafter Ahmad.
With respect to claim 12, all the limitations in claim 1 are addressed by Barnes, Hahm, and Masters above. The combination of Barnes, Hahm, and Masters does not teach:
referencing a plurality of established biomedical relationships to identify first biomedical relationships to be excluded from the determined associations candidate feature subcombinations; and
excluding biomedical relationships from the determined candidate feature subcombinations that are also present within the first biomedical relationships.
Ahmad teaches these things:
referencing a plurality of established biomedical relationships to identify first biomedical relationships to be excluded from the determined associations candidate feature subcombinations (paragraph 0070 determining correlations between features (associations) to remove due to biasing a model); and
excluding biomedical relationships from the determined candidate feature subcombinations that are also present within the first biomedical relationships (paragraph 0070 removing those correlations to prevent biasing the model).
It would have been obvious to have combined the function of excluding associations with the analysis techniques of Barnes, Hahm, and Masters to allow additional features/associations to come out in an analysis after certain associations are removed.
With respect to claim 13, all the limitations in claims 1 and 12 are addressed by Barnes, Hahm, Masters, and Ahmad above. Hahm also teaches the background knowledge graph is associated with a background knowledge data store (column 239 lines 4-12 knowledge graph associated with a database, columns 234-235 lines 54-3 knowledge graph associated with pre-existing knowledge graph, column 233 lines 26-36 and column 234 lines 10-33 knowledge graph associated with data from other sources).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Barnes, Hahm, and Masters and further in view of Ghiassian et al (US 12,523,318) hereafter Ghiassian.
With respect to claim 14, all the limitations in claim 1 are addressed by Barnes, Hahm, and Masters above. The combination of Barnes, Hahm, and Masters does not teach:
categorizing the plurality of features as source-type features and target-type features; and
aggregating features, wherein the aggregation includes at least one source-type feature and at least one target-type feature from identified features of the plurality of features.
Ghiassian teaches these things:
categorizing the plurality of features as source-type features and target-type features (); column 2 lines 52-64 categorize subjects (source) per likely benefit for a therapy (target)); and
aggregating features, wherein the aggregation includes at least one source-type feature and at least one target-type feature from identified features of the plurality of features (columns 4-5 lines 60-3 aggregate features for therapies for patients).
It would have been obvious to have combined the function of categorizing source-type and target-type features in Ghiassian with the analysis techniques in Barnes and Masters to be more cost-effective for therapies being effective for a disease.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Barnes, Hahm, and Masters and Ghiassian and further in view of Abraham et al (US 20220319658), hereafter Abraham.
With respect to claim 15, all the limitations in claims 1 and 14 are addressed by Barnes, Hahm, Masters, and Ghiassian above. The combination of Barnes, Hahm, Masters, and Ghiassian does not teach wherein the aggregating is based at least in part on embeddings generated from the aggregation of features including the at least one source-type feature and the at least one target-type feature. Abraham teaches this with a feature vector (embeddings) of biomarkers representing associations between biomarker data and outcome data (paragraph 0133). It would have been obvious to have combined the function of feature vector embeddings generated from associations between biomarkers and patient outcome data in Abraham with the analysis techniques in Barnes, Hahm, Masters, and Ghiassian to provide predictive ability for a therapy involving the biomarker outcome.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Barnes, Hahm, and Masters and Ghiassian and further in view of Russell (US 10,997,244).
With respect to claim 18, all the limitations in claims 1 and 14 are addressed by Barnes, Hahm, Masters, and Ghiassian above. The combination of Barnes, Hahm, Masters, and Ghiassian does not teach wherein the source-type features and target-type features comprise semantically related entities in the patient-centric knowledge graph, wherein a relationship for at least a subset of the semantically related entities further comprises a temporal element. Russell teaches this with metasets from datasets (column 7 lines 48-64), semantic searching of metasets for entities, finds matches between temporal attributes (column 12 lines 15-46). It would have been obvious to have combined the function of semantically-related entities in a knowledge base in Russell with the analysis techniques in Barnes, Hahm, Masters, and Ghiassian to provide more information for the user, making the combination more user-friendly.
Relevant Prior Art
During his search for prior art, Examiner found the following reference to be relevant to Applicant's claimed invention. Said reference is listed on the Notice of References form included in this office action:
Datla et al (US 20190252074) teaches automated clinical diagnosis via creating a knowledge graph of symptoms, diseases, and medical conditions, does not teach also creating a patient-centric knowledge graph of excluding biomedical relationships based on a knowledge graph (paragraphs 0006, 0029-0064 figures 1-2).
Responses to Applicant’s Remarks
Regarding objections to claims 1, 19, and 20 for antecedent basis of “features and response or outcomes features” in the recited subcombinations, in view of amendments reciting “the candidate feature subcombinations and the response or outcomes features,” these objections are withdrawn. Regarding rejections to clams 1-20 under 35 U.S.C. 101 for reciting mental processes without significantly more, Applicant’s remarks have been considered but are not persuasive. On pages 8-9 of his Remarks Applicant asserts the claims are not performable in the human mind. Examiner disagrees and notes that “specific computational data structures and operations” are not claimed, only generic computer components and broad operations like identifying populations and sub-populations of subjects, and determining candidate feature subcombinations and associations. MPEP 2106.04(a)(2)(III) states "The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation," and a BRI for each of the identified mental process operations include accomplishing them with a physical aid such as pen and paper. On pages 9-10 Applicant discusses the Enfish and Ex Parte Desjardins cases and asserts the claims are integrated into a practical application. The PTAB cited the Enfish case in its opinion in and the claims in Enfish recite a self-referential database which was determined to be an improvement to the function of a computer, and Examiner notes the present claims recite no such data structure. The claims do recite “graph-based exclusion of established biomedical relationships” but do not recite how or why biomedical relationships are excluded or how candidate feature subcombinations are determined based on the patient-centric knowledge graph, thus the claims do not recite how the invention addresses any computational challenges as described in specification paragraph 0005. Paragraph 0005 also discusses a filtering mechanism but the claims do not recite such a filtering mechanism either. On pages 10-11 Applicant discusses Step 2B and asserts the claims do not preempt all the ways of performing the abstract idea. Examiner notes this lack of preemption does not equate to eligibility per MPEP 2106.07(b). The claim recites additional elements of receiving a plurality of features, constructing a patient-centric knowledge graph, accessing a background knowledge graph, and providing a summary of the determined associations. Examiner notes that while these elements regard the type of patient features of clinical data being received or populated in a knowledge graph, they are not limiting of the abstract ideas to a particular way of using the knowledge graphs, identifying populations of subjects, excluding biomedical relationships, and determining subcombinations and associations. Therefore Examiner believes the steps amount to collecting information, analyzing it, and outputting a summary of the analysis, and do not amount to significantly more than the recited abstract ideas.
Regarding rejections of claims 1, 4-7, and 19-20 under 35 U.S.C. 103 by Barnes in view of Masters, claims 2 and 3 by Barnes and Masters in further view of Boussios, claim 8 by Barnes and Masters in further view of Russell, claim 9 by Barnes and Masters and Russell and further in view of Prathap, claim 10 by Barnes and Masters and further in view of Rahman, claim 12 by Barnes and Masters and further in view of Ahmad, claim 13 by Barnes and Masters and Ahmad and further in view of Rahman, claim 14 by Barnes and Masters and further in view of Ghiassian, claim 15 by Barnes and Masters and Ghiassian and further in view of Abraham, and claim 18 by Barnes and Masters and Ghiassian and further in view of Russell, Applicant’s remarks on pages 12-13 asserting neither Barnes nor Masters teaches a knowledge graph is persuasive. Examiner conducted another search of the prior art and found Hahm, which he believes teaches these claims in the new grounds of rejection set forth above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRUCE M MOSER whose telephone number is (571)270-1718. The examiner can normally be reached M-F 9a-5p.
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/BRUCE M MOSER/Primary Examiner, Art Unit 2154 8/1/26
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