NON-FINAL REJECTION, FIRST DETAILED ACTION
Status of Prosecution
The present application, 18/116,176 filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The application, filed in the Office on March 1, 2023. A continuation-in-part was filed off of this application on September 29, 2023 (18/477,817) and also claims priority to intervening provisional application 63/468,192 filed May 22, 2023.
The Office mailed a first detailed action, non-final rejection, on January 26, 2026.
Applicant filed amendments with accompanying remarks and arguments on April 27, 2026.
Claims 21-40 are pending and are all rejected. Claims 21, 30 and 39 are independent. Claims 1-20 are canceled.
Claim Status
Claims 21, 25-30 and 34-40 are rejected under 35 U.S.C. § 103 as being unpatentable over Wall et al. (“Wall”), United States Patent Application Publication 2021/0133509, published on May 6, 2021 in view of Preuss et al. (“Preuss”), United States Patent Application Publication 2021/0233030, published on July 29, 2021.
Claims 22-24 and 31-33 are rejected under 35 U.S.C. § 103 as being unpatentable over Wall in view of Preuss and in further view of Gal et al. (“Gal”), United States Patent Application Publication 2010/0190142, published on July 29, 2010.
Response to Remarks and Arguments
Examiner has considered Applicant’s new claims and rejects them as noted below.
Regarding the § 101 rejection it is withdrawn.
The claims stand rejected.
Claim Interpretation – Definitions
Applicant is acting as their own lexicographer for certain terms, which are reproduced here for readers’ convenience:
[0037] As used herein, the term "machine learning" is intended to mean the application of one or more software application techniques or models that process and analyze data to draw inferences and/or predictions from patterns in the data. The machine learning techniques can include a variety of models or algorithms, including supervised learning techniques, unsupervised learning techniques, reinforcement learning techniques, knowledge-based learning techniques, natural- language-based learning techniques such as natural language generation, natural language processing (NLP) and named entity recognition (NER), deep learning techniques, and the like. The machine learning techniques are trained using training data. The training data is used to modify and fine-tune any weights associated with the machine learning models, as well as record ground truth for where correct answers can be found within the data. As such, the better the training data, the more accurate and effective the machine learning model.
[0038] As used herein, the term "trustworthiness" is intended to mean the ability to be relied upon as being honest or truthful. As used herein, the term "certainty" is intended to mean a quality of being reliably true. As used herein, the term "uncertainty" is intended to mean a quality of not being reliably true.
Claim Interpretation – Invocation of 35 USC § 112(f)
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: each of the modules in each of the claims.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
A.
Claims 21, 25-30 and 34-40 are rejected under 35 U.S.C. § 103 as being unpatentable over Wall et al. (“Wall”), United States Patent Application Publication 2021/0133509, published on May 6, 2021 in view of Preuss et al. (“Preuss”), United States Patent Application Publication 2021/0233030, published on July 29, 2021.
As to Claim 21, Wall teaches: A system for making an assessment of an assessment object, the system comprising:
at least one processor (Wall: par. 0032).; and
at least one memory device comprising computer readable code configured, when executed(Wall: par. 0163, the memory and processor have a computer program), to cause the at least one processor to:
generate questions directed to one or more characteristics of the assessment object within an environment context (Wall: par. 0359, a question recommendation module to select “the most predictive next question to be presented”; “The feature recommendation module can thus help to dynamically tailor the assessment procedure to the subject (i.e. “assessment object”), so as to enable the prediction module to produce a prediction with a reduced length of assessment and improved sensitivity and accuracy;” Examiner asserts that the questions selected are based on previous ones, which would suggest an “environment context”);
receive, from a user, responses to the questions, wherein each response of the responses comprises an answer to a respective question of the questions (Wall: pars. 0285, answers (i.e.) are generated in response to questions),
determine base scores for the responses, wherein a respective base score is determined for each response of the responses(Wall: Fig. 14, pars. 0419-20, numerical scores are output and combined in a model combinator to generate a numerical score output);
determine weighted scores for each response of the responses, wherein the weighted scores are determined by applying at least one scoring adjustment to each base score of the base scores to generate the weighted scores (Wall: par. 0412, the tuning parameters may be various parameters including a threshold of positive determination score). Examiner asserts a person having ordinary skill in the art would have understood that the base score for a response may be tuned as a parameter per the disclosure of Wall as well; par. 0412, “and any other tuning parameter as known in the art.” The base score could be adjusted per the tuning (i.e. a weighting and scoring adjustment). This would consequently result in the decision module being affected in making its final determination score and assessment.) and
aggregate the weighted scores to generate an assessment score (Wall: par. 0359, the prediction module will generate a final prediction; par 0341, the prediction module will output a prediction [170]).
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Wall may not explicitly teach: receive, from a user, responses to the questions, wherein each response of the responses comprises an answer to a respective question of the questions, a rationale provided by the user in support of the answer or an indication that no rationale was provided, and evidence provided by the user in support of at least one of the answer or the rationale or an indication that no evidence was provided;
determine weighted scores for each response of the responses, wherein the weighted scores are determined by applying at least one scoring adjustment to each base score of the base scores to generate the weighted scores and the at least one scoring adjustment is based at least in part on the rationale, the indication that no rationale was provided, the evidence of the respective response, or the indication that no evidence was provided.
Preuss teaches in general recorded candidate assessments for available positions with scores being calculated per interview question (Preuss: Abstract). Specifically, Preuss teaches that the trustworthiness of a candidate’s response during the interview assessment process may be calculated (Preuss: par. 0080, a quality indicator may be composed of confidence, trustworthiness, etc.). Responses are also assessed to determine the sufficiency in answer for a rationale presented (Preuss: par. 0130, the response may assessed to determine if the question is sufficiently answered. Examiner asserts that a thorough response as contemplated by Preuss would include a rationale in support of the answer and/or evidence).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have modified the Wall disclosures and teachings by calculating a trustworthiness score as taught by Preuss. Both Wall and Preuss are involved in the field of endeavor of automated assessments. Such a person would have been motivated to do so with a reasonable expectation of success to capture the quality and amount of information for scoring purposes (Preuss: par. 0080).
As to Claim 25, Wall and Preuss teach the limitations of claim 21.
Preuss further teaches: wherein the computer readable code is further configured to cause the at least one processor to apply the at least one scoring adjustment based at least in part on the at least one of an importance level of a respective question, a trustworthiness of the respective response(Preuss: par. 0080, a quality indicator may be composed of confidence, trustworthiness, etc.), or an uncertainty level of the respective response.
As to Claim 26, Wall teaches the limitations of claim 25.
Wall further teaches: wherein the at least one scoring adjustment comprises a trustworthiness scoring adjustment based on at least one of a detected bias in the respective response based on a role or type of the user relative to the assessment object, a consistency between the respective response and at least one other response of the responses, or an inconsistency between the respective response and the at least one other response.
Preuss teaches in general recorded candidate assessments for available positions with scores being calculated per interview question (Preuss: Abstract). Specifically, Preuss teaches that the trustworthiness of a candidate’s response during the interview assessment process may be calculated (Preuss: par. 0080, a quality indicator may be composed of confidence, trustworthiness, etc.)
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have modified the Wall disclosures and teachings by calculating a trustworthiness score as taught by Preuss. Both Wall and Preuss are involved in the field of endeavor of automated assessments. Such a person would have been motivated to do so with a reasonable expectation of success to capture the quality and amount of information for scoring purposes (Preuss: par. 0080).
As to Claim 27, Wall and Preuss teach the limitations of claim 21.
Preuss further teaches: wherein the computer readable code is further configured to cause the at least one processor to: identify one or more potential tasks that would improve the assessment score (Preuss: par. 0091, a word count anomaly related to the data quality score is assessed); and
cause a notification to be generated informing the user of the one or more potential tasks (Preuss: par. 0091, a notification may be given for the word count anomaly).
As to Claim 28, Wall and Preuss teach the limitations of claim 21.
Wall further teaches: wherein the computer readable code is further configured to cause the at least one processor to:
generate at least one refined question based on a previously received response of the responses (Wall: par. 0359: the module can select the most predictive next question to be presented to a subject or caretaker, based on the answers to previously presented questions.)
As to Claim 29, Wall and Preuss teach the limitations of claim 21.
Wall further teaches: wherein the assessment object is a business opportunity, and the assessment comprises a recommendation not to proceed with the business opportunity, to adjust an approach with the business opportunity, or to persevere with the business opportunity (Examiner asserts that a business opportunity is a broad term that could reasonably include whether or not to proceed with a treatment as discussed by Wall).
As to Claim 30, it is rejected for similar reasons as claim 21.
As to Claim 34, it is rejected for similar reasons as claim 25.
As to Claim 35, it is rejected for similar reasons as claim 26.
As to Claim 36, it is rejected for similar reasons as claim 27.
As to Claim 37, it is rejected for similar reasons as claim 28.
As to Claim 38, it is rejected for similar reasons as claim 29.
As to Claim 39, it is rejected for similar reasons as claim 21 and 30.
As to Claim 40, it is rejected for similar reasons as claim 25.
B.
Claims 22-24 and 31-33 are rejected under 35 U.S.C. § 103 as being unpatentable over Wall et al. (“Wall”), United States Patent Application Publication 2021/0133509, published on May 6, 2021 in view of Preuss et al. (“Preuss”), United States Patent Application Publication 2021/0233030, published on July 29, 2021 in view of Gal et al. (“Gal”), United States Patent Application Publication 2010/0190142, published on July 29, 2010.
As to Claim 22, Wall and Pruess teach the limitations of claim 21.
Wall and Pruess may not explicitly teach: wherein the computer readable code is further configured to cause the at least one processor to:
generate the assessment of the assessment object based on the assessment score and at least one ontology stored in the at least one memory device, the at least one ontology defining a relationship between an intrinsic characteristic of the assessment object and an extrinsic characteristic of the environment context; and
identify, based on the at least one ontology, a causal relationship between an extrinsic characteristic of the environment context and a dependent intrinsic characteristic of the assessment object.
Gal teaches in general concepts related to automated pedagogical assessments (Gal: Abstract). Specifically, Gal teaches that a knowledge map engine may receive inputs such as answers to questions, and other elements for the knowledge map (Gal: par. 0084). An ontology component stores concepts and definitions of terms, mappings and relationships as well (Gal: pars. 0082-84, a mapping and tagging component [172] may map between different objects and entities to the ontology concepts). Preuss teaches the responses are extracted and analyzed (i.e. per an extraction module) (Preuss: par. 0130).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have modified the Wall-Preuss combination disclosures and teachings by utilizing a knowledge base with ontologies for the questions and responses as taught by Gal. Wall, Preuss and Gal are involved in the field of endeavor of automated assessments. Such a person would have been motivated to do so with a reasonable expectation of success to allow for the ready storage and access of needed information in a knowledge base.
As to Claim 23, Wall, Pruess and Gal teach the limitations of claim 22.
Wall may not explicitly teach: wherein the at least one ontology represents a relationship between an independent intrinsic characteristic of the assessment object and a dependent intrinsic characteristic of the assessment object, the independent intrinsic characteristic is a characteristic of the assessment object inherent to the assessment object and not affected by other characteristics, and the dependent intrinsic characteristic is a characteristic of the assessment object that is affected by at least one of an extrinsic characteristic of the environment context or another intrinsic characteristic of the assessment object.
Wall does teach however that environmental influence (i.e. extrinsic characteristic) which may influence the severity of symptoms related to a developmental disorder (i.e intrinsic characteristic of the assessment object) (Wall: par. 0453, the environmental influence may comprise an insult from a toxin, virus or other substance, for example.). Predicted questions are selected based on the answers to previously presented questions and also, “to recommend one or more next questions or features having the highest predictive utility in classifying a particular subject's developmental disorder.” (Wall: par. 0359, the feature recommendation module can be a question recommendation module to do so). Examiner asserts that a causal relationship between various features that include environmental extrinsic ones and the intrinsic characteristic ones (i.e. an identified disorder).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have implemented the Wall disclosures and teachings by implementing the feature recommendation module as a question recommendation module and as the claim’s relationship module to utilize the identified causal relationships between extrinsic characteristic of an environment context and the intrinsic characteristic of the assessment object. Such a person would have been motivated to do so with a reasonable expectation of success to allow for the improved assessment sensitivity and accuracy (Wall: par. 0359, “The feature recommendation module can thus help to dynamically tailor the assessment procedure to the subject, so as to enable the prediction module to produce a prediction with a reduced length of assessment and improved sensitivity and accuracy.)
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have implemented the Wall-Preuss-Gal combination disclosures and teachings with the various ontologies are required by the claim. Such a person would have been motivated to do so with a reasonable expectation of success to allow for the ready storage and access of needed information in a knowledge base for the various tasks needed to perform the combination.
As to Claim 24, Wall, Preuss and Gal teach the limitations of claim 22.
Wall, Preuss and Gal further teach: wherein the at least one ontology includes at least one of an assessment theory ontology, a question survey ontology, a journey ontology, a decision ontology, a decision gate ontology, or an assessment analysis ontology.
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have implemented the Wall-Preuss-Gal combination disclosures and teachings with the various ontologies are required by the claim. Such a person would have been motivated to do so with a reasonable expectation of success to allow for the ready storage and access of needed information in a knowledge base for the various tasks needed to perform the combination.
As to Claim 31, it is rejected for similar reasons as claim 22.
As to Claim 32, it is rejected for similar reasons as claim 23.
As to Claim 33, it is rejected for similar reasons as claim 24.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES T TSAI whose telephone number is (571)270-3916. The examiner can normally be reached M-F 8-5 Eastern.
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/JAMES T TSAI/ Primary Examiner, Art Unit 2147
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