DETAILED ACTION
Claims 2-21 are pending. Claims 2-21 are considered in this Office action.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 3/14/2025 has been acknowledged. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. The initialed and dated copy of Applicant’s IDS form 1449 is attached to the instant Office action.
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 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); 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 nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 2-21 of the current application, hereby known as ‘392, are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 11, and 20 of U.S. Patent No. 12,307,402, hereby known as ‘402. Although the claims at issue are not identical, they are not patentably distinct from each other because:
Regarding Claims 2, 12, and 20, Claims 2, 12, and 20 of ‘392 recite substantially similar steps of '402 Claims 1, 11, and 20, respectively.
Claims 2, 12, and 20 of ‘392 recite the limitations of:
receiving, by a computer system including one or more processors, assessment data indicative of performances of a plurality of respondents with respect to a plurality of assessment items;
determining, by the computer system, using the assessment data, (i) for each assessment item of the plurality of assessment items, a difficulty level and (ii) for each respondent of the plurality of respondents an ability level;
determining, by the computer system, for each respondent of the plurality of respondents, one or more respondent-specific parameters using ability levels of the plurality of respondents and difficulty levels of the plurality of assessment items, the one or more respondent-specific parameters including an expected performance parameter of the respondent;
determining, by the computer system, one or more contextual parameters using the item difficulty levels and the ability levels, the one or more contextual parameters indicative of at least one of an aggregate characteristic of the plurality of assessment items or an aggregate characteristic of the plurality of respondents; and
storing, by the computer system, the respondent-specific parameters and the one or more contextual parameters in a database;
generating, by the computer system, as part of the database a graphical probabilistic model for a Bayesian network to depict a relative importance of the respondent-specific parameters of the plurality of assessment items and the one or more contextual parameters using a size and color of nodes of the graphical probabilistic model corresponding to the plurality of assessment items; and
providing, by the computer system, via one or more user interfaces (UIs), access to the database and a visual representation of the size and color of nodes for the respondent-specific parameters and the one or more contextual parameters
Whereas Claims 1, 11, and 20 of ‘402 states:
receiving, by a computer system including one or more processors, assessment data indicative of performances of a plurality of respondents with respect to a plurality of assessment items;
determining, by an item response theory (IRT) tool programmed on the one or more processors of the computer system executing an IRT model on the assessment data of the plurality of respondents, (i) a difficulty level for each assessment item of the plurality of assessment items, and (ii) an ability level for each respondent of the plurality of respondents, the IRT model defining, for each pair of an assessment item and a respondent, a corresponding probability of success as a nonlinear function of the difficulty level of the assessment item and the ability level of the respondent;
determining, by the IRT tool executing the IRT model, for each assessment item of the plurality of assessment items, one or more item-specific parameters indicative of one or more characteristics of the assessment item using difficulty levels for the plurality of assessment items and ability levels for the plurality of respondents, the one or more item-specific parameters of the assessment item including at least one of an item importance value or an item entropy, the item importance value indicative of dependency of an overall performance with respect to the plurality of assessment items on performance with respect to the assessment item, and the item entropy representing an expectation of assessment information of the assessment item as a function of respondent ability;
determining, by the IRT tool executing the IRT model, one or more contextual parameters using the item difficulty parameters and the respondent ability parameters, the one or more contextual parameters indicative of at least one of an aggregate characteristic of the plurality of assessment items or an aggregate characteristic of the plurality of respondents;
storing, by the computer system, the item-specific parameters and the one or more contextual parameters in a database;
generating, by the computer system, as part of the database a graphical probabilistic model for a Bayesian network to depict a relative importance of the item-specific parameters of the plurality of assessment items and the one or more contextual parameters using a size and color of nodes of the graphical probabilistic model corresponding to the plurality of assessment items; and
providing, by the computer system, via one or more user interfaces (UIs), access to the database and a visual representation of the size and color of nodes for the item-specific parameters and the one or more contextual parameters.
The broader claims of the instant application are anticipated by the narrower claims of the patent. See MPEP 804(II)(B)(1).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Alice - Claims 2-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 2, 12, and 21 recite limitations for receiving assessment data indicative of performances of a plurality of respondents with respect to a plurality of assessment items (Collecting Information, an Observation, a Mental Process; Managing Human Behavior, i.e. managing abilities of respondents; a Certain Method of Organizing Human Activity), determining using the assessment data, (i) for each assessment item of the plurality of assessment items, a difficulty level and (ii) for each respondent of the plurality of respondents an ability level (Analyzing the Information, an Evaluation, a Mental Process; Managing Human Behavior, i.e. managing abilities of respondents; a Certain Method of Organizing Human Activity), determining for each respondent of the plurality of respondents, one or more respondent-specific parameters using ability levels of the plurality of respondents and difficulty levels of the plurality of assessment items, the one or more respondent-specific parameters including an expected performance parameter of the respondent (Analyzing the Information, an Evaluation, a Mental Process; Managing Human Behavior, i.e. managing abilities of respondents; a Certain Method of Organizing Human Activity), determining one or more contextual parameters using the item difficulty levels and the ability levels, the one or more contextual parameters indicative of at least one of an aggregate characteristic of the plurality of assessment items or an aggregate characteristic of the plurality of respondents (Analyzing the Information, an Evaluation, a Mental Process; Managing Human Behavior, i.e. managing abilities of respondents; a Certain Method of Organizing Human Activity), and generating a graphical probabilistic model for a Bayesian network to depict a relative importance of the respondent-specific parameters of the plurality of assessment items and the one or more contextual parameters using a size and color of nodes of the graphical probabilistic model corresponding to the plurality of assessment items (Analyzing the Information, an Evaluation, a Mental Process; Managing Human Behavior, i.e. managing abilities of respondents; a Certain Method of Organizing Human Activity), and providing access to a visual representation of the size and color of nodes for the respondent-specific parameters and the one or more contextual parameters (Transmitting the Analyzed Information, a Judgment, a Mental Process; Managing Human Behavior, i.e. managing abilities of respondents; a Certain Method of Organizing Human Activity), which under their broadest reasonable interpretation, covers performance of the limitation in the mind for the purposes of Managing Human Activity, i.e. managing respondents abilities, but for the recitation of generic computer components. That is, other than reciting a computer system, one or more processors, a database, one or more user interfaces, memory, a medium, and storing the respondent-specific parameters and the one or more contextual parameters in a database, nothing in the claim element precludes the step from practically being performed or read into the mind for the purposes of a Commercial Interaction. For example, determining a difficulty and ability level for each assessment and respondent encompasses a supervisor, data analyst, surveyor, or employer at a workplace doing surveys about each employee and accessing how hard each job was and assigning a score, and then accessing how each person did with the job and assigning a score, which is an observation, evaluation, and judgment. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas, an observation, evaluation, and judgment. Further, as described above, the claims recite limitations for Managing Human Behavior, a “Certain Method of Organizing Human Activity”. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the above stated additional elements to perform the abstract limitations as above. The computer system, one or more processors, database, storing of parameters, graphical user interface, memory, and medium are recited at a high-level of generality (i.e., as a generic software/module performing a generic computer function of storing, retrieving, sending, and processing data) such that they amount to no more than mere instructions to apply the exception using generic computer components. Even if taken as an additional element, the receiving and transmitting steps above are insignificant extra-solution activity as these are receiving, storing, and transmitting data as per the MPEP 2106.05(d). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered both individually and as an ordered combination. As discussed above with respect to integration of the abstract idea into a practical application, the additional element being used to perform the abstract limitations stated above amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible. Applicant’s Specification states:
“[0054] The computer system 100 can be any workstation, telephone, desktop computer, laptop or notebook computer, netbook, ULTRABOOK, tablet, server, handheld computer, mobile telephone, smartphone or other portable telecommunications device, media playing device, a gaming system, mobile computing device, or any other type and/or form of computing, telecommunications or media device that is capable of communication.”
Which shows that any generic computer can be used to perform the abstract limitations, such as a laptop, phone, desktop, etc., and from this interpretation, one would reasonably deduce the aforementioned steps are all functions that can be done on generic components, and thus application of an abstract idea on a generic computer, as per the Alice decision and not requiring further analysis under Berkheimer, but for edification the Applicant’s specification has been used as above satisfying any such requirement. This is “Applying It” by utilizing current technologies. For the receiving and transmitting steps that were considered extra-solution activity in Step 2A above, if they were to be considered additional elements, they have been re-evaluated in Step 2B and determined to be well-understood, routine, conventional, activity in the field. The background does not provide any indication that the additional elements, such as the computer system, processor, memory, etc., nor the receiving, storing, or transmitting steps as above, are anything other than a generic, and the MPEP Section 2106.05(d) indicates that mere collection or receipt, storing, or transmission of data is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). For these reasons, there is no inventive concept. The claim is not patent eligible.
Claims 2-11, 13-19, and 21 contain the identified abstract ideas, further narrowing them, with no new additional elements to be considered as part of a practical application or under prong 2 of the Alice analysis of the MPEP, thus not integrated into a practical application, nor are they significantly more for the same reasons and rationale as above.
After considering all claim elements, both individually and in combination, Examiner has determined that the claims are directed to the above abstract ideas and do not amount to significantly more. Therefore, the claims and dependent claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. v. CLS Bank International, No. 13–298.
Allowable Subject Matter
Claims 2-21 have overcome the prior art and would be allowable if amended to overcome the 35 USC 101 and Double Patenting rejections.
The closest prior art of record are Essafi (U.S. Publication No. 2017/012,4894), Lee (U.S. Publication No. 2017/001,8198), and Monk (U.S. Publication No. 2014/017,2417). Essafi, system and method for instrumentation of education processes, teaches determining by iteratively solving a model using the assessment data, a difficulty level for each assessment item of the plurality of assessment
items, determining for each assessment item of the plurality of assessment items, one or more item-
specific parameters indicative of one or more characteristics of the assessment item using difficulty
levels for the plurality of assessment items, the one or more item-specific parameters of the assessment
item including at least one of an item importance value, the item importance value indicative of
dependency of an overall performance with respect to the plurality of assessment items on performance with respect to the assessment item, and the item entropy representing an expectation of assessment information of the assessment item as a function of respondent ability, but it does not explicitly teach
the ability groupings, nor does it teach the iterative solving of that specific type of equation. Lee, a
system and method for automated assessment of cognitive, fine-motor, and memory skills, teaches an
ability level of the workers, and use a modeling, but not the contextual parameters, nor does it teach
the iterative process in such a specific manner. Monk, a vital text analytics system and method for the
enhancement of requirements engineering documents and other documents, also teaches modeling of
corresponding probability of success as a nonlinear function of the difficulty level of the assessment
item and the ability level of the respondent and use of contextual parameters indicative of at least one
of an aggregate characteristic of the plurality of assessment items or an aggregate characteristic of the
plurality of respondents, storing the item-specific parameters and the one or more contextual parameters in a database; and providing, by the computer system, via one or more user interfaces (UIs),
access to the database, but not by performing the modeling in a probabilistic and iterative manner. None of the above prior art explicitly teaches this specific manner of iterative modeling using nonlinear
probabilistic models to determine both a difficulty level and ability level, and then assessing them for
items in the specific manner as laid out by the claims, and these are the reasons which adequately
reflect the Examiner's opinion as to why Claims 2-21 are allowable over the prior art of record, and are
objected to as provided above.
Conclusion
The prior art made of record is considered pertinent to applicant's disclosure.
US 20170124894 A1
Essafi; Lassaad et al.
SYSTEMS AND METHODS FOR INSTRUMENTATION OF EDUCATION PROCESSES
US 20170018198 A1
Lee; Kiju
AUTOMATED ASSESSMENT OF COGNITIVE, FINE-MOTOR, AND MEMORY SKILLS
US 20140172417 A1
Monk, II; Gordon H. et al.
VITAL TEXT ANALYTICS SYSTEM FOR THE ENHANCEMENT OF REQUIREMENTS ENGINEERING DOCUMENTS AND OTHER DOCUMENTS
US 20170124894 A1
ESSAFI L et al.
System for computing the performance metrics of the education systems and and predicting learning outcomes, comprises multiple computer servers that are connected with multiple client applications running on the client devices
US 20220284374 A1
CAHALANE; Diarmuid John et al.
SKILLS GAP MANAGEMENT PLATFORM
US 20220265212 A1
LINDEMANN; Michael et al.
MEASURING SPATIAL WORKING MEMORY USING MOBILE-OPTIMIZED SOFTWARE TOOLS
US 20220004890 A1
Essafi; Lassaad et al.
SYSTEMS AND METHODS FOR AUTOMATED DESIGN OF ASSESSMENT INSTRUMENTS
US 20200302810 A1
Lee; Kiju
AUTOMATED ASSESSMENT OF COGNITIVE, FINE-MOTOR, AND MEMORY SKILLS
US 20200302370 A1
Mathiesen; Christian V. et al.
MAPPING ASSESSMENT RESULTS TO LEVELS OF EXPERIENCE
US 20190009133 A1
Mettler May; Bérénice
SYSTEMS AND METHODS FOR DATA-DRIVEN MOVEMENT SKILL TRAINING
US 20130211238 A1
DeCharms; R. Christopher
METHODS FOR PHYSIOLOGICAL MONITORING, TRAINING, EXERCISE AND REGULATION
US 20110039247 A1
Packard; Ronald Jay et al.
SYSTEMS AND METHODS FOR PRODUCING, DELIVERING AND MANAGING EDUCATIONAL MATERIAL
US 20060229505 A1
Mundt; James C. et al.
Method and system for facilitating respondent identification with experiential scaling anchors to improve self-evaluation of clinical treatment efficacy
US 9792829 B2
Rudolph; Laurence
System and method for conducting multi-layer user selectable electronic testing
US 9691289 B2
Kullok; Jose Roberto et al.
Monotonous game-like task to promote effortless automatic recognition of sight words
US 9569729 B1
Oehrle; Richard et al.
Analytical system and method for assessing certain characteristics of organizations
US 9530329 B2
Rudolph; Laurence
System and method for conducting multi-layer user selectable electronic testing
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH M WAESCO whose telephone number is (571)272-9913. The examiner can normally be reached on 8 AM - 5 PM M-F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, BETH BOSWELL can be reached on (571) 272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-1348.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JOSEPH M WAESCO/Primary Examiner, Art Unit 3683 12/22/2025