DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This is a Non-Final Office Action in response to application 18/886,931 entitled "METHOD AND APPARATUS FOR PREDICTING INJURIES" filed on January 23, 2026, with claims 1 to 10 pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on April 17, 2025, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the Examiner.
Election/Restrictions
Applicant’s election without traverse of Claims 1- 10 in the reply filed on January 23, 2026, is acknowledged.
Claim Interpretation
The Specification reads [00179] “The machine learning model 604 uses the historical data and feature set to generate a frailty score. At step 605 the system performs data pre-processing to normalize the data. Data preprocessing in a machine learning model involves preparing raw data to make it suitable for building a machine learning model.”
Considering the Specification, Examiner interprets the machine learning model as generating the frailty score which is the function claimed for the artificial intelligence (AI) engine and the processor is configured to execute the machine learning algorithms.
Specification Objections
The use of the terms CBS SPORTS® and DRAFTKINGS®, which are a trade names or marks used in commerce, have been noted in this application. They should be CAPITALIZED wherever they appear and be accompanied by the generic terminology.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Please see MPEP 2106 for additional information regarding Patent Subject Matter Eligibility Guidance.
Claims 1-10 are directed to a method/process, machine/apparatus, (article of) manufacture, or composition of matter, which are/is one of the statutory categories of invention, which are/is one of the statutory categories of invention. (Step 1: YES).
The claimed invention is directed to an abstract idea without significantly more.
Independent Claim 1 recites:
“A system for predicting a likelihood of injury of an individual, comprising: …
a specialized dataset comprising physical, medical, or behavioral data of the individual;
….operable to process the specialized dataset to generate a frailty score for the individual, the frailty score representing the likelihood of the individual being injured;
an action module configured to trigger one or more actions based on the frailty score, wherein the actions are aimed at reducing the likelihood of injury or advising the individual on whether to engage in potentially injury-inducing behaviors.”
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. Specific instances include instructing to “generate a frailty score for the individual” and “reducing the likelihood of injury or advising the individual” recite Managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as Managing personal behavior then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. (Step 2A-Prong 1: YES. The claims recite an abstract idea).
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of:
[a processor configured to execute machine learning algorithms]:
merely applying computer processing, storage, and networking technology as tools to perform an abstract idea
[an artificial intelligence (AI) engine]:
merely applying the generic machine learning and artificial intelligence functions to the abstract idea.
are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer components and/or electronic processes. For example, the Applicant’s Specification reads:
[0222] The system provides an app that is accessed by a team owner on a smartphone 201 (or desktop, pad, etc.). The owner communicates to a system server 204 via network 203 (e.g., the Internet). The system server receives communications and checks player status, payment operations, and other services, using system database 205 to store relevant information.
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, Claim 1 is directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f). Accordingly, the additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. The claim further defines the abstract idea and hence is abstract for the reasons presented above. The claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the claim is directed to an abstract idea. Thus, the claim is not patent eligible. (Step 2B: NO. The claim does not provide significantly more)
Dependent Claims recite additional elements.
This judicial exception is not integrated into a practical application. In particular, the recited additional elements of
Claim 2:
“AI processes … using feature engineering, model training, hyperparameter tuning, cross-validation, ensemble learning”: merely applying machine learning and artificial intelligence technologies as a tool to perform an abstract idea
Claims 3-9: (none found: does not include additional elements and merely narrows the abstract idea)
Claim 10:
“AI engine utilizes … neural networks”: merely applying machine learning and artificial intelligence technologies as a tool to perform an abstract idea
are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer components and/or electronic processes. For support from the Applicant’s Specification, see the analysis as applied to Independent Claim 1 (Step 2A-Prong 2) earlier. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, the claim is directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f). Accordingly, these additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. Dependent claims further define the abstract idea that is present in their respective independent claims and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims are directed to an abstract idea. Thus, the dependent claims are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3, 4, and 7-9 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Palamadai ("PREDICTING AND MINIMIZING RISKS ASSOCIATED WITH PERFORMANCE OF PHYSICAL TASKS", U.S. Publication Number: 20240062909 A1).
Regarding Claim 1,
Palamadai teaches,
A system for predicting a likelihood of injury of an individual, comprising: a processor configured to execute machine learning algorithms;
(Palamadai [0009] an example machine learning framework that facilitates predicting and minimizing risks associated with performance of physical tasks
Palamadai [0026] one or more machines (e.g., processors, computers, computing devices, virtual machines, etc.)
Palamadai [0029] as computer-readable instructions, data structures, models, algorithms)
a specialized dataset comprising physical, medical, or behavioral data of the individual;
(Palamadai [0002] improve or accommodate an individual's physical and physiological activity.
Palamadai [0033] current health status profile for a user can include information that relates to the current physical and mental health status of the user.... general physical capabilities
Palamadai [0034] further include information regarding current (e.g., existing) physical limitations and injuries of the user and current medical conditions of the user.
Palamadai [0022] track and monitor user activity and behavior over time)
an artificial intelligence (AI) engine operable to process the specialized dataset to generate a frailty score for the individual, the frailty score representing the likelihood of the individual being injured; an action module configured to trigger one or more actions based on the frailty score, wherein the actions are aimed at reducing the likelihood of injury or advising the individual on whether to engage in potentially injury-inducing behaviors.
(Palamadai [0025] terms “artificial intelligence (AI) model” and “machine learning (ML) model” are used
Palamadai [0058] can further determine one or more risk scores for the user and task based on the respective probabilities of occurrence
Palamadai [0061] a recommendation that a user should not proceed with can be based on the results of the task risk assessment indicating that performance of the task by the user is attributed to one or more potential risks being too high (e.g., relative to a threshold risk level or risk score), such as the risk of an injury to the user and/or others being too high.)
Regarding Claim 3,
Palamadai teaches the injury prediction of Claim 1 as described earlier.
Palamadai teaches,
further including performing recursive optimization on the frailty score.
(Palamadai [0022] The system can further track and monitor user activity and behavior over time, employing a continuous feed-back loop to regularly reassess....refine and optimize
Palamadai [0096] machine learning component 602 to define, and/or update/refine ... employed by the machine learning component 602 to train and/or retrain
Palamadai [0020] generate an overall risk score based on the risk assessment)
Regarding Claim 4,
Palamadai teaches the injury prediction of Claim 3 as described earlier.
Palamadai teaches,
wherein the recursive optimization comprises identifying changeable features of a feature set, adjusting the changeable features,
(Palamadai [0095] SVM's are configured via a learning or training phase within a classifier constructor and feature selection module.
Palamadai [0022] The system can further track and monitor user activity and behavior over time, employing a continuous feed-back loop to regularly reassess....refine and optimize
Palamadai [0096] machine learning component 602 to define, and/or update/refine ... employed by the machine learning component 602 to train and/or retrain)
recomputing a frailty score and comparing it to a previously generated frailty score and determining if the recomputed frailty score is an improvement.
(Palamadai [0020] generate an overall risk score based on the risk assessment
Palamadai [0058] can further determine one or more risk scores for the user and task based on the respective probabilities of occurrence
Palamadai [0022] continuous feed-back loop to regularly reassess....refine and optimize the risk assessment
Palamadai [0096] update/refine...the risk assessment data 142...employed by the machine learning component 602 to train and/or retrain the task capabilities models
Palamadai [0067] risk assessment results over time (e.g.,... reduced frequency and/or degree of performance of risky tasks... and/or increased frequency and/or degree of performance of risky tasks).)
Regarding Claim 7,
Palamadai teaches the injury prediction of Claim 1 as described earlier.
Palamadai teaches,
wherein the individual is in an industry.
(Palamadai [0051] consider the scenario involving workers on an assembling line)
Regarding Claim 8,
Palamadai teaches the injury prediction of Claim 7 as described earlier.
Palamadai teaches,
wherein the specialized dataset comprises injury information for the industry.
(Palamadai [0051] If one of the workers is unequipped to perform their assembly line task and/or suffers an injury as a result, the tasks of the rest of the workers along the assembling line will be hindered. In accordance with this example, the risk analysis component 122 can assess financial costs
Palamadai [0055] wherein the baseline probability for each potential risk represents the average probability of occurrence of the risk at the optimal health profile for the task.
Palamadai [0075] one or more environment data sources 220 (e.g., type of location, tasks performed at the location, places of business at the location, events at the location, current weather, etc.).)
Regarding Claim 9,
Palamadai teaches the injury prediction of Claim 8 as described earlier.
Palamadai teaches,
wherein the specialized dataset further comprises environmental data including weather, time of day, day of week, and type of activity of the individual.
(Palamadai [0075] one or more environment data sources 220 (e.g., type of location, tasks performed at the location, places of business at the location, events at the location, current weather, etc.).
Palamadai [0076] based on their current context (e.g., location, time/day, schedule, job/role, movement pattern and activity, etc.) and behavior.)
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2, 5, 6, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Palamadai ("PREDICTING AND MINIMIZING RISKS ASSOCIATED WITH PERFORMANCE OF PHYSICAL TASKS", U.S. Publication Number: 20240062909 A1),in view of Dirac (“EFFICIENT DUPLICATE DETECTION FOR MACHINE LEARNING DATA SETS”, U.S. Publication Number: 20150379430 A1).
Regarding Claim 2,
Palamadai teaches the injury prediction of Claim 1 as described earlier.
Palamadai does not teach wherein the AI processes the specialized dataset using feature engineering, model training, hyperparameter tuning, cross-validation, ensemble learning, and model evaluation.
Dirac teaches,
wherein the AI processes the specialized dataset using feature engineering, model training, hyperparameter tuning, cross-validation, ensemble learning, and model evaluation.
(Dirac [0134] performing feature processing
Dirac [0154] Automated Parameter Tuning for Recipe Transformations...The values of such parameters (which may also be referred to as hyper-parameters in some environments) may have a significant impact on the predictions
Dirac [0161] for cross-validating a classification model
Dirac [0044] uses an ensemble of decision trees at a machine learning service
Dirac [0225] tree ensemble-based algorithms such as Random Forest
Dirac [0231] the model's execution for a prediction/evaluation run)
It is prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the injury prediction system of Palamadai to incorporate the machine learning techniques of Dirac where “Machine learning combines techniques from statistics and artificial intelligence.” (Dirac [0002]). The modification would have been obvious, because it is merely applying a known technique (i.e. machine learning techniques) to a known concept (i.e. injury prediction) ready for improvement to yield predictable result (i.e. “to create algorithms that can learn from empirical data and generalize to solve problems in various domains such … human health diagnosis and the like.” Dirac [0002])
Regarding Claim 5,
Palamadai teaches the injury prediction of Claim 4 as described earlier.
Palamadai does not teach wherein the adjusting of the changeable features is saved when the frailty score is an improvement.
Dirac teaches,
wherein the adjusting of the changeable features is saved when the frailty score is an improvement.
(Dirac [0263] the previously-stored parameters or weights may be updated... As more and more observation records are examined, more and more (feature, parameter) key-value pairs may be added into the feature set.....in which the memory available at an MLS server
Dirac [0032] may be used to improve the quality of predictions made by a machine learning model)
It is prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the injury prediction system of Palamadai to incorporate the machine learning techniques of Dirac where “Machine learning combines techniques from statistics and artificial intelligence.” (Dirac [0002]). The modification would have been obvious, because it is merely applying a known technique (i.e. machine learning techniques) to a known concept (i.e. injury prediction) ready for improvement to yield predictable result (i.e. “to create algorithms that can learn from empirical data and generalize to solve problems in various domains such … human health diagnosis and the like.” Dirac [0002])
Regarding Claim 6,
Palamadai and Dirac teach the injury prediction of Claim 5 as described earlier.
Palamadai does not teach wherein the adjusting of the changeable features is reverted when the frailty score is not an improvement.
Dirac teaches,
wherein the adjusting of the changeable features is reverted when the frailty score is not an improvement.
(Dirac [0134] performing feature processing
Dirac [0184] perform the reverse operation
Dirac [0320] make one change, observe the impact of that change, undo that change, then make another change and view its impact, and so on.
Dirac [0085] detecting and recovering from failures)
It is prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the injury prediction system of Palamadai to incorporate the machine learning techniques of Dirac where “Machine learning combines techniques from statistics and artificial intelligence.” (Dirac [0002]). The modification would have been obvious, because it is merely applying a known technique (i.e. machine learning techniques) to a known concept (i.e. injury prediction) ready for improvement to yield predictable result (i.e. “to create algorithms that can learn from empirical data and generalize to solve problems in various domains such … human health diagnosis and the like.” Dirac [0002])
Regarding Claim 10,
Palamadai teaches the injury prediction of Claim 1 as described earlier.
Palamadai teaches,
tree-based models, and neural networks.
(Palamadai [0095] decision trees
Palamadai [0096] neural networks...a set of convolution neural network computations)
Palamadai does not teach wherein the AI engine utilizes linear models.
Dirac teaches,
wherein the AI engine utilizes linear models.
(Dirac [0260] Optimizations for Training Linear Models...Linear prediction models are the most popular)
It is prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the injury prediction system of Palamadai to incorporate the machine learning techniques of Dirac where “Machine learning combines techniques from statistics and artificial intelligence.” (Dirac [0002]). The modification would have been obvious, because it is merely applying a known technique (i.e. machine learning techniques) to a known concept (i.e. injury prediction) ready for improvement to yield predictable result (i.e. “to create algorithms that can learn from empirical data and generalize to solve problems in various domains such … human health diagnosis and the like.” Dirac [0002])
Prior Art Cited But Not Applied
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Kearney (“ARTIFICIAL INTELLIGENCE PLATFORM FOR DETERMINING DENTAL READINESS”, U.S. Publication Number: 20210353393 A1) proposes dental patient data including an image, proposed treatments, and a dental form are received and processed by first machine learning models to obtain clinical findings and predicted values for fields of the dental form. The clinical findings and other results are processed by a second machine learning model to obtain predictions of a future dental condition of a patient. The second machine learning model utilizes an ensemble of Transformer Neural Networks, Long-Short-Term-Memory Networks, Convolutional Neural Networks, and Tree-Based Algorithms to predict the dental readiness classification, dental readiness durability, dental readiness error, dental emergency likelihood, prognosis, and alternative treatment options.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHINEDU EKECHUKWU whose telephone number is (571)272-4493. The examiner can normally be reached on Mon-Fri 10am to 4pm ET.
Examiner interviews are available via telephone 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, Christine Tran, can be reached on (571) 272-8103. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/C.E./Examiner, Art Unit 3695
/CHRISTINE M Tran/Supervisory Patent Examiner, Art Unit 3695