DETAILED ACTION
This action is made in response to the amendments/remarks filed on November 12, 2025. This action is made final.
Claims 1-20 are pending. Claims 1, 2, 5, 12, 14, 16, and 19 have been amended. Claims 1, 16, and 19 are independent claims.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s argument filed November 12, 2025 have been fully considered but are not persuasive.
With respect to the previous 101 rejection, Applicant argues the claims are not directed to a method of organizing human activity and furthermore the claims integrate the abstract idea into a practical application. Applicant contends the claims, when viewed as a whole, do not recite methods of organizing human activity, but rather the steps recited in the claims include computer-implemented steps for executing algorithms for assigning patients to one of a plurality of digital phenotype sub-cohorts based on similarity measures, determining a probability of success for the treatment options, and collecting real-time patient data of the patient during treatment, executing a feedback loop to update the simulation, wherein the feedback loop is based on: data specific to the target patient, the treatment pathway recommendation to the target patient, and the real-time patient data collected. However, the examiner respectfully disagrees.
MPEP 2106. 04(a)(2)(II) states that a claimed invention is directed to certain methods of organizing human activity if the identified claim elements contain limitations that encompass fundamental economic principles or practices, commercial or legal interactions, or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The identified claim elements of the present application represent a series of rules or instructions that a person or persons, with or without the aid of a computer, would follow to recommend a treatment. For example, a medical practitioner can assess which treatment options are best suited for a patient based on similar patients, estimate a probability of success and can further monitor the patient progress and their treatment to update/modify their treatment recommendations for future use. The Examiner notes that Applicant’s Background describes the retrieving and analyzing of information used to provide guidance to health care professionals for diagnosis and treatment as a human task (e.g., see [0004], [0005], [0008]). Furthermore, the Examiner submits that healthcare itself is inherently represents the organization of human activity. Applicant has not pointed to anything in the claims that fall outside of this characterization. Because the claim elements fall under a series of rules or instructions that a person or persons would follow to recommend a treatment to a patient, the claimed invention is directed to an abstract idea.
With regards to claim 16, the claim recites “wherein the medical treatment is a robotic surgical procedure; and providing the guidance for performing the treatment to a surgical robot”. The “surgical robot” is recited at a high level of generality and amounts to generally linking the abstract idea to a particular technological environment or field of use. Notably, no treatment is actually provided and the claims merely state providing a guidance for performing the treatment, which is further encompassed in the abstract idea.
Applicant further argues the claims, as a whole, integrate the abstract idea into a practical application in that the additional elements or combination improves the functioning of a computer or improves another technology or technical field. Specifically, Applicant argues the method optimizes the processing of electronic information to determine a probability of success for each of a plurality of treatment options and generating treatment pathway recommendation and provides “a significant technical advantage over traditional approaches to generating a treatment pathway recommendation”. However, the examiner respectfully disagrees.
As stated above, the claimed invention is directed to the abstract idea of recommending a treatment to a patient. Applicant’s purported improvement, even if implemented through technical means, does not provide an improvement to a “computer, or any other technology or technical field” as per MPEP 2106.04(d)(1) and 2106.05(a). Rather, as stated by Applicant, the improvement is to the “treatment pathway recommendation”, which the examiner has identified as an abstract idea. Accordingly, any purported improvement is to the abstract idea and not that of a computer, technology, or technical field and, therefore, is not indicative of integration into a practical application.
Applicant argues the previously cited references fails to teach “clustering the patient population into the patient population into a plurality of digital phenotype sub-cohorts based on the selected similarity measure” and “executing a feedback loop to updated the simulation-based algorithm, wherein the feedback loop is based on: data set specific to the target patient; the treatment pathway recommendation for the target patient; and the real-time patient data collected during the treatment”. However, the examiner respectfully disagrees.
Nawana is directed to a system and method for surgical and interventional planning, support, post-operative follow-up, and functional recovery tracking. Nawana teaches patient data, of multiple patients can be collected and analyzed to determine possible treatment options by the use of decision-making algorithms, by allowing comparisons between similar aspects of medical treatments learned over time through continual data gather, analysis, and assimilation to decision making algorithms (e.g., see Abstract). Nawana teaches, for each patient, identifying patients with similar symptoms, patient types, procedures, etc. (i.e., digital phenotype sub-cohorts, consistent with Applicant’s originally filed specification of “digital phenotype sub-cohorts as per [0043], [0064]) for determining which treatment plan to use (e.g., see [0032], [0106], [0120]). Nawana further teaches receiving patient feedback of the patient and/or similarly situated patient as well as real-time patient data that is continuously collected and reviewed to make modifications or new recommendations to the treatment plan (e.g., see Fig. 39, [0148], [0166], [0167]). Accordingly, for at least the above stated reasons, and further outlined below, the previous grounds of rejection are maintained.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-20 recite a method of recommending a treatment, which is within the statutory category of a process. Claim 16-18 recites a system memory for generating guidance of a treatment, which is within the statutory class of a machine. Claims 19-20 recites a method of generating a rating associated with a treatment, which is within the statutory class of a method.
Claims are eligible for patent protection under § 101 if they are in one of the four statutory categories and not directed to a judicial exception to patentability. Alice Corp. v. CLS Bank Int'l, 573 U.S. ___ (2014). Claims 1-20, each considered as a whole and as an ordered combination, are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
MPEP 2106 Step 2A – Prong 1:
The bolded limitations of:
Claim 1
receiving, by one or more processors, at least one prior data set comprising one or more of: i) a prior data set specific to the target patient from the target patient, ii) a prior data set specific to the target patient from a health care service provider, and iii) a prior data set specific to a patient population with at least one common attribute with the target patient from a health care service provider; executing an algorithm, stored in a non-transitory computer-readable storage medium, to identify a pattern within the at least one prior data sets, the pattern including clustering the patient population into a plurality of digital phenotype sub-cohorts based on a selected similarity measure; assigning the target patient to one of the plurality of digital phenotype sub-cohorts based on the selected similarity measure; executing a simulation-based algorithm to determine a probability of success for each of a plurality of potential treatment options for the target patient based on the assigned digital phenotype sub-cohort of the target patient; automatically generating, by the one or more processors, a treatment pathway recommendation for the target patient based on the probability of success for each of the plurality of potential treatment options; inputting the at least one data set specific to the target patient and the treatment pathway recommendation for the target patient to update the simulation-based algorithm; and generating and presenting an electronic display of the treatment pathway recommendation; collecting real-time patient data of the target patient during a treatment; and executing a feedback loop to update the simulation-based algorithm wherein the feedback loop is based on: the at least one data set specific to the target patient and the treatment pathway recommendation for the target patient and the real-time patient data collected during the treatment
Claim 16
computer-readable storage medium storing instructions for generating and presenting an electronic display of guidance for medical treatment; and one or more processors configured to execute the instructions to perform a method including: receiving a plurality of prior procedure data sets, wherein each prior procedure data set includes one or more of: i) at least one data set specific to a target patient, ii) at least one data set specific to the target patient and received from a health care service provider, and iii) at least one data set specific to a patient population with at least one common attribute with the target patient from a health care service provider; identifying objective data identifying a predicted outcome of a medical treatment; identifying a pattern across the plurality of prior procedure data sets by clustering one or more other patients from the patient population into one or more sub-cohorts within the patient populations and adding the target patient to one of the one or more sub-cohorts based on a similarity score between the target patient and the sub-cohort, the pattern describing a characteristic of the medical treatment that achieves the patient outcome defined by the objective data; receiving information about an instance of the medical treatment to be performed in the future to the target patient from a health care provider; automatically generating guidance for performing the medical treatment based on the characteristic identified by the pattern and the information received about the instance of the medical treatment to be performed; inputting the at least one data set specific to the target patient and the guidance for medical treatment to update the instructions for generating and presenting an electronic display of guidance for medical treatment; and generating and presenting an electronic display of the guidance for performing the medical treatment, wherein the medical treatment is a robotic surgical procedure; and providing the guidance for performing the medical treatment to a surgical robot.
Claim 19
receiving, by one or more processors, a plurality of prior procedure data sets, wherein each prior procedure data set includes one or more of: i) at least one data set specific to the target patient from the patient, ii) at least one data set specific to the patient from a health care service provider, and iii) at least one data set specific to a patient population with at least one common attribute with the target patient from a health care service provider, wherein each of the patients of the patient population received the same surgical procedure as the surgical procedure for the target patient; executing a[n] algorithm to determine whether the surgical procedure for the target patient is predicted to be more difficult than the average difficulty of the surgical procedure based on the at least one data set specific to the target patient from the patient, the at least one data set specific to the patient from a health care service provider, and the at least one data set specific to a patient population with at least one common attribute with the target patient from a health care service provider, wherein one or more other patients from the patient population are established as one or more agents with characteristics identified as similar to the target patient, wherein the characteristics include one or more of: age, co-morbidities, treatment regime, probability of adverse outcomes, and expected timespan-related outcomes; automatically generating, by the one or more processors, a difficulty rating for the surgical procedure to treat the target patient; inputting the at least one data set specific to the target patient and the difficulty rating for the target patient to update the simulation-based algorithm; and generating and presenting an electronic display of the difficulty rating prior to the surgical procedure; and executing a feedback loop to update the simulation-based algorithm wherein the feedback loop is based on: the at least one data set specific to the target patient, the difficulty rating for the target patient, and the real-time patient data collected during the treatment
as presently drafted, under the broadest reasonable interpretation, covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for the recitation of generic computer components. For example, but for the noted computer elements, the claim encompasses a person following rules or instructions to process data in the manner described in the abstract idea, such as analyzing patient data and comparing to similarly situation patient to predict an optimal treatment plan, wherein the plan can be continuously updated based on feedback information. The examiner further notes that “methods of organizing human activity” includes a person’s interaction with a computer (see October 2019 Update: Subject Matter Eligibility at Pg. 5). If the claim limitation, under its broadest reasonable interpretation, covers managing persona behavior or interactions between people but for the recitation of generic computer components, then it falls within the “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Furthermore, insomuch as an “algorithm” is recited, the lack of disclosure as to what the “algorithm” entails, under the broadest reasonable interpretation, additionally covers a mathematical concept and mental process. The above limitations are steps in a mathematical concept such as mathematical relationships, mathematical formulas or equations, and mathematical calculations. If a claim, under its broadest reasonable interpretation, is directed towards a mathematical concept, then it falls within the Mathematical Concepts grouping of abstract ideas. See MPEP § 2106.04(a)(2).
The mathematical concept is claimed in such a generalized manner that the mathematical concept also encompasses a person mentally performing the math, see MPEP § 2106.04(a)(2) as well, including that for a mental process “Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer' s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675.
Furthermore, the algorithm is a process step that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper but for the recitation of a generic computer component. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the "Mental Process" grouping of abstract ideas. A person would readily be able to perform this process either mentally or with the assistance of pen and paper. See MPEP § 2106.04(a)(2).
MPEP 2106 Step 2A – Prong 2:
This judicial exception is not integrated into a practical application because there are no meaningful limitations that transform the exception into a patent eligible application. The additional elements merely amount to instructions to apply the exception using generic computer components (“an electronic display”; “one or more processors”; “computer-readable storage medium” —all recited at a high level of generality). Although they have and execute instructions to perform the abstract idea itself, this also does not serve to integrate the abstract idea into a practical application as it merely amounts to instructions to "apply it." (See MPEP 2106.04(d)(2) indicating mere instructions to apply an abstract idea does not amount to integrating the abstract idea into a practical application). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
The “simulation-based algorithm to identify…”, an “agent-based model”, and a “surgical robot” are not a generic computer component; however it is recited at a high levels of generality and similarly amount to generally linking the abstract idea to a particular technological environment. (See MPEP 2106.04(d)(1) indicating generally linking an abstract idea to a particular technological environment does not amount to integrating the abstract idea into a practical application).
The claims only manipulate abstract data elements into another form. They do not set forth improvements to another technological field or the functioning of the computer itself and instead use computer elements as tools in a conventional way to improve the functioning of the abstract idea identified above. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. None of the additional elements recited "offers a meaningful limitation beyond generally linking 'the use of the [method] to a particular technological environment,' that is, implementation via computers." Alice Corp., slip op. at 16 (citing Bilski v. Kappos, 561 U.S. 610, 611 (U.S. 2010)).
At the levels of abstraction described above, the claims do not readily lend themselves to a finding that they are directed to a nonabstract idea. Therefore, the analysis proceeds to step 2B. See BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1349 (Fed. Cir. 2016) ("The Enfish claims, understood in light of their specific limitations, were unambiguously directed to an improvement in computer capabilities. Here, in contrast, the claims and their specific limitations do not readily lend themselves to a step-one finding that they are directed to a nonabstract idea. We therefore defer our consideration of the specific claim limitations’ narrowing effect for step two.") (citations omitted).
MPEP 2106 Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the same reasons as presented in Step 2A Prong 2. Moreover, the additional elements recited are known and conventional generic computing elements (“an electronic display”; “one or more processors”; “computer-readable storage medium”—see Specification [0031], [0034] describing the various components as general purpose, common, standard, known to one of ordinary skill, and at a high level of generality, and in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy the statutory disclosure requirements). Therefore, these additional elements amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept that amounts to significantly more. See MPEP 2106.05(f).
The Federal Circuit has recognized that "an invocation of already-available computers that are not themselves plausibly asserted to be an advance, for use in carrying out improved mathematical calculations, amounts to a recitation of what is 'well-understood, routine, [and] conventional.'" SAP Am., Inc. v. InvestPic, LLC, 890 F.3d 1016, 1023 (Fed. Cir. 2018) (alteration in original) (citing Mayo v. Prometheus, 566 U.S. 66, 73 (2012)). Apart from the instructions to implement the abstract idea, they only serve to perform well-understood functions (e.g., receiving, translating, and displaying data—see Specification above as well as Alice Corp.; Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307 (Fed. Cir. 2016); and Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015) covering the well-known nature of these computer functions).
Furthermore, as discussed above, the additional element of the “algorithm to identify…”, “agent-based model”, and a “surgical robot” are recited at high levels of generality and were determined to generally link the abstract idea into a particular technological environment or field of use. This additional element have been re-evaluated under step 2B and have also been found insufficient to provide significantly more. (See MPEP 2106.05(A) indicating generally linking an abstract idea to a particular technological environment does not amount to significantly more).
Dependent Claims
The limitations of dependent but for those addressed below merely set forth further refinements of the abstract idea without changing the analysis already presented. Claims 2-4 merely recite stratifying the patient population and determining a probability of success for the treatment options, claims 4-10 merely recites the type of input data and type of treatment plan, claims 11, 15, and 20 merely recites displaying the probability of success, updated treatment plan, difficulty level, respectively, which covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions).
Claims 5 further refine the abstract idea described in the independent claim and merely recite further a wearable electronic device for receiving patient data and the use of machine learning algorithms. These additional elements are considered to “generally link” and/or “apply it” under both the practical application and significantly more analysis, as detailed in the analysis above.
Claims 12-14, 17-18 and 3 further refine the abstract idea described in the independent claim and further recite robotic surgical procedures. These additional elements are considered to “generally link” under both the practical application and significantly more analysis, as detailed in the analysis above.
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
Claim(s) 1-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nawana et al. (USPPN: 2016/0338685; hereinafter Nawana).
As to claim 1, Nawana teaches A computer-implemented method for generating and presenting an electronic display of guidance for treating a target patient with a musculoskeletal disease or musculoskeletal disorder (e.g., see abstract), comprising:
receiving, by one or more processors, at least one prior data set comprising one or more of: i) a prior data set specific to the target patient from the target patient, ii) a prior data set specific to the target patient from a health care service provider, and iii) a prior data set specific to a patient population with at least one common attribute with the target patient from a health care service provider (e.g., see Fig. 3, [0125], [0129], [0130], [0148] teaching a plurality of data sets including specific patient data as received from the patient, specific patient data as received from the patient’s primary care provider, and prior patient data of a similar type based on similar variables and/or attributes);
executing an algorithm, stored in a non-transitory computer-readable storage medium, to identify a pattern across within the at least one prior data set, the pattern including clustering the patient population into a plurality of digital phenotype sub-cohorts based on a selected similarity measure (e.g., see [0120], [0145], [0148], [0155], [0166], [0175], [0180] teaching identifying treatment options based on discovered trends in the variable between patients, patient type, procedure, etc. such that patients are compared and consolidated to patients with similar symptoms, which is consistent with at least [0043], [0064] describing similar symptoms or treatments as digital phenotype sub-cohorts);
assigning the target patient to one of the plurality of digital phenotype sub-cohorts based on the selected similarity measure (e.g., see Fig. 41C, [0148], [0168], [0324], [0325] wherein the patients are grouped with similar patient comparison groups);
executing a simulation-based algorithm to determine a probability of success for each of a plurality of potential treatment options for the target patient based on the assigned digital phenotype sub-cohort (e.g., see Fig. 39, [0032], [0150], [0152], [0296] wherein each recommended treatment determines a probability of success based on attributes associated with the proposed treatment);
automatically generating, by the one or more processors, a treatment pathway recommendation for the target patient based on the probability of success for each of the plurality of potential treatment options (e.g., see [0150], [0152], [0154], [0155] wherein possible treatments are suggested by the treatment options module, wherein a potential success rate of various treatment options can be considered in suggesting the particular treatment);
inputting the at least one data set specific to the target patient and the treatment pathway recommendation for the target patient to update the simulation-based algorithm (e.g., see [0311], [0316], [0324] wherein patient monitored data can be used to determine the effectiveness of the treatment plan and make updates/modifications to the recommended treatment plan); and
generating and presenting an electronic display of the treatment pathway recommendation (e.g., see [0150] wherein a list of possible treatment options are presented);
collecting real-time patient data of the target patient during a treatment (e.g., see [0148] wherein patient data is collected in real-time); and
executing a feedback loop to update the simulation-based algorithm (e.g., see Figs. 36-39, [0162], [0164]-[0165] wherein feedback is provided by the patient and other users to modify the treatment plan for similar patients. See also Figs. 36-39 illustrating a loop based on received data (i.e., feedback) to maintain the original treatment or recommend a new one), wherein the feedback loop is based on:
the at least one data set specific to the target patient (e.g., see [0166] wherein the recommended treatment plan can be updated based on benchmarks such as progress in addressing symptoms); and
the treatment pathway recommendation for the target patient (e.g., see [0166], [0167] wherein a treatment can be modified, more likely to be recommended or less likely to be recommended based on treatment); and the real-time patient data collected during the treatment (e.g., see [0148] wherein the treatment recommendation is based on real-time patient data and is continually reviewed to provide more accurate recommendations for treatment).
While Nawana teaches updating a treatment plan based on real-time patient data and further teaches updating a treatment plan based on the treatment and data specific to the target patient, Nawana fails to explicitly teach the feedback based on each, collectively. However, it would have at least been obvious to receive feedback based on the at least one data set specific to the target patient, the treatment pathway recommendation for the target patient, and the real-time patient data collected during the treatment in order to continually review data to discover trends to patient type, procedure, and functional outcomes, to provide more accurate recommendations of treatments for the patient using (e.g., see [0148] of Nawana).
As to claim 2, the rejection of claim 1 is incorporated. Nawana further teaches executing the algorithm identifies the pattern within the at least one prior data sets includes: clustering one or more patients from the patient population into one or more sub-cohorts within the patient populations; and adding the target patient to one of the one or more sub-cohorts based on a similarity score between the target patient and the sub-cohort (e.g., see [0032], [0148], [0271] wherein the trends are identified based on patients having similar symptoms, patient type, procedure, etc., wherein the data is continuously updated and aggregated for optimal collection of data of most similar matches).
As to claim 3, the rejection of claim 2 is incorporated. Nawana further teaches executing the simulation-based algorithm includes determining a probability of success for each of the plurality of potential treatment options for the target patient based on the assigned digital phenotype sub-cohort of other patients in the sub-cohort (e.g., see also Fig. 39, [0032], [0150], [0152], [0296] wherein teach recommended treatment determines a probability of success based on attributes associated with the proposed treatment, wherein the proposed treatment and probability of success is based from data of similar patients).
As to claim 4, the rejection of claim 1 is incorporated. Nawana further teaches wherein the simulation-based algorithm is an agent-based model, where one or more other patients from the patient population are established as one or more agents with characteristics identified as similar to the target patient, wherein the characteristics include one or more of: age, co-morbidities, treatment regime, probability of adverse outcomes, and expected timespan-related outcomes (e.g., see [0148] wherein an attribute includes the patient’s age, height, weight, symptoms, test results, procedure type, functional outcomes, etc.).
As to claim 5, the rejection of claim 1 is incorporated. Nawana further teaches wherein the at least one prior data set comprises the prior data set specific to the target patient from the target patient, and wherein one or more processor receives the at least one data set specific to the target patient from the patient via a wearable electronic device (e.g., see [0135] wherein patient data can be received from a head-mounted device from the patient); and comprises:
generating a virtual representation of the musculoskeletal anatomy of the target patient by executing machine learning algorithms to create anatomy models of the target patient based on prior data set specific to the target patient from the patient via the wearable electronic device (e.g., see [0145], [0148], [0155], [0166], [0175], [0180] teaching identifying treatment options based on discovered trends using learning methods in the variable between patients, patient type, procedure, etc., wherein a treatment option includes a surgical procedure in which a virtual patient is created based on at least part of the received patient data and learning algorithms for determining the treatment).
As to claim 6, the rejection of claim 1 is incorporated. Nawana further teaches wherein the treatment pathway recommendation includes a non-surgical treatment plan, wherein the non- surgical treatment plan includes one or more rehabilitation exercises, one or more treatments from a health care provider, and/or one or more drug treatments (e.g., see [0154] wherein treatment options can include non-surgical plans including exercise, medications, diet, etc.).
As to claim 7, the rejection of claim 1 is incorporated. Nawana teaches wherein the treatment pathway recommendation includes a recommended implant and a recommended joint replacement surgical plan (The particular type of surgery, “recommended implant” and “recommended joint replacement” are interpreted as being an intended use. Applicant is remined that, typically, no patentable distinction is made by an intended use or result unless some structural difference is imposed by the use or result on the structure or material recited in the claim, or some manipulative difference is imposed by the use or result on the action recited in the claim. An intended use generally does not impart a patentable distinction if it merely states an intention or is a description of how the claimed apparatus is to be used. Here, Nawana, having taught recommending various surgical plans including grafts and implants, therefore, teaches the claimed limitation. e.g., see [0154], [0185], [0210]).
As to claim 8, the rejection of claim 7 is incorporated. Nawana further teaches wherein the recommended joint replacement surgical plan is a full knee replacement plan or a partial knee replacement plan (The type and degree of the surgical plan, “full knee replacement” or “partial knee replacement” are interpreted as being an intended use. Applicant is remined that, typically, no patentable distinction is made by an intended use or result unless some structural difference is imposed by the use or result on the structure or material recited in the claim, or some manipulative difference is imposed by the use or result on the action recited in the claim. An intended use generally does not impart a patentable distinction if it merely states an intention or is a description of how the claimed apparatus is to be used. Here, Nawana, having taught recommending various surgical plans including replacements, therefore, teaches the claimed limitation. e.g., see [0154], [0185], [0210]).
As to claim 9, the rejection of claim 1 is incorporated. Nawana further teaches wherein the treatment pathway recommendation includes a recommended pre-surgical treatment schedule for the patient and a recommended joint replacement surgical plan (e.g., see [0005], [0154] wherein treatment options can include surgical, including those relating to the joints, and non-surgical plans including exercise, medications, diet, etc.); and
wherein the recommended joint replacement surgical plan is a posterior cruciate-retaining knee replacement, a posterior-stabilized total knee replacement, a cemented total knee replacement, or a cementless total knee replacement (The type of surgical plan, “posterior cruciate knee replacement” or “posterior-stabilized total knee replacement”, “cemented total knee replacement”, or “cementless total knee replacement” are interpreted as being an intended use. Applicant is remined that, typically, no patentable distinction is made by an intended use or result unless some structural difference is imposed by the use or result on the structure or material recited in the claim, or some manipulative difference is imposed by the use or result on the action recited in the claim. An intended use generally does not impart a patentable distinction if it merely states an intention or is a description of how the claimed apparatus is to be used. Here, Nawana, having taught recommending various surgical plans including replacements, therefore, teaches the claimed limitation. e.g., see [0154], [0185], [0210]).
As to claim 10, the rejection of claim 1 is incorporated. Nawana further teaches wherein the treatment pathway recommendation includes a recommended pre-surgical drug treatment (e.g., see [0154] wherein treatment options can include non-surgical plans including exercise, medications, diet, etc.).
As to claim 11, the rejection of claim 1 is incorporated. Nawana further teaches wherein generating and presenting an electronic display of the treatment pathway recommendation includes: displaying a probability of successful treatment for a recommended surgical treatment of the patient, and displaying a probability of successful treatment for a recommended non-surgical treatment of the patient (e.g., see [0150], [0154] wherein the success rates of the treatment options are presented to a user, wherein the treatment options included surgical and non-surgical options),
wherein the probability of successful treatment for a recommended surgical treatment and the probability of successful treatment for a recommended non-surgical treatment are determined using each of i) the at least one data set specific to the target patient from the patient, ii) the at least one data set specific to the patient from a health care service provider, and iii) the at least one data set specific to a patient population with at least one common attribute with the target patient from a health care service provider (e.g., see [0145], [0148], [0155], [0166] teaching treatment options to recommend based on similar patient types/attributes and further identifying a potential success rate thereof).
As to claim 12, the rejection of claim 1 is incorporated. Nawana further teaches wherein the at least one prior data set comprises the prior data set specific to a patient population with at least one common attribute with the target patient from a health care service provider, and wherein the prior data set specific to a patient population with at least one common attribute with the target patient from a health care service provider includes robot data from a plurality of prior robotic surgical procedures, wherein the robot data includes a movement of a robotic arm during a surgical procedure for each of the patients within the patient population with at least one common attribute with the target patient (e.g., see [0266], [0272]-[0274] teaching tracking robotic motion during a surgical procedure and tracking of new innovations to surgical procedures for similar patients/procedures provided by the healthcare provider/hospital).
Claim(s) 13-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nawana et al. (USPPN: 2016/0338685; hereinafter Nawana) in further view of Kim et al. (USPPN: 2015/0202014; hereinafter Kim).
As to claim 13, the rejection of claim 12 is incorporated. While Nawana teaches recommending surgical plans and further teaches tracking the use of surgical robots and new innovations during surgical procedures, Nawana fails to explicitly teach the surgical plan for a robotic medical procedure, and wherein the recommended surgical plan includes a recommended movement of a robotic tool to treat the target patient.
However, in the same field of endeavor of medical procedures, Kim teaches wherein the treatment pathway recommendation includes a recommended surgical plan for a robotic medical procedure, and wherein the recommended surgical plan includes a recommended movement of a robotic tool to treat the target patient (e.g., see [0026]-[0027] teaching recommending movement of the surgical robot to treat the patient). Accordingly, it would have been obvious to modify Nawana in view of Kim with a reasonable expectation of success. One would have been motivated to make the modification in order to enhance an accuracy, precision, and delicacy of surgical procedures (e.g., see [0005] of Kim).
As to claim 14, the rejection of claim 12 is incorporated. Nawana further teaches wherein the at least one prior data set comprises: i) the prior data set specific to the target patient from the target patient, ii) the prior data set specific to the target patient from a health care service provider, and ii) the prior data set specific to a patient population with at least one common attribute with the target patient from a health care service provider (e.g., see Fig. 3, [0125], [0129], [0130], [0148] teaching a plurality of data sets including specific patient data as received from the patient, specific patient data as received from the patient’s primary care provider, and prior patient data of a similar type based on similar variables and/or attributes), and wherein:
the prior data set specific to the patient from a health care service provider includes data from a surgical procedure conducted to treat the target patient (e.g., see [0316] teaching collecting post-surgery monitoring information entered by a medical practitioner);
the prior data set specific to a patient population with at least one common attribute with the target patient from a health care service provider includes post-operative data collected from patients who received a prior surgical procedure (e.g., see [0311] teaching comparing post-surgery data to those having similar demographics to the patient); and
the treatment pathway recommendation includes a recommended post-operative rehabilitation exercise schedule for the target patient (e.g., see [0321] wherein post-surgery physical therapy may be recommended).
While Nawana teaches recommending surgical plans and further teaches tracking the use of surgical robots and new innovations during surgical procedures, Nawana fails to explicitly teach robotic surgical procedure.
However, in the same field of endeavor of medical procedures, Kim teaches robotic surgical procedure (e.g., see [0026]-[0027] teaching recommending movement of the surgical robot to treat the patient). Accordingly, it would have been obvious to modify Nawana in view of Kim with a reasonable expectation of success. One would have been motivated to make the modification in order to enhance an accuracy, precision, and delicacy of surgical procedures (e.g., see [0005] of Kim).
As to claim 15, the rejection of claim 14 is incorporated. Nawana further teaches executing a second algorithm, stored in a non-transitory computer-readable storage medium, to identify a pattern across the plurality of prior data sets, the pattern describing a probability of success for each of a plurality of potential treatment options for the target patient (e.g., see [0320] teaching comparing data with historical data of similar patients to identify success rates of treatement);
automatically generating, by the one or more processors, an updated treatment pathway recommendation for the target patient using the probability of success for each of a plurality of potential treatment options for the target patient (e.g., see [0316] wherein an updated treatment plan may be recommended); and
generating and presenting an electronic display of the updated treatment pathway recommendation, wherein the updated treatment pathway recommendation includes a recommended adjustment to the target patient's post-operative rehabilitation exercise schedule (e.g., see [0321], [0322] teaching displaying updated treatment recommendations, which may include updated treatments and/or physical therapy).
As to claim 16, Nawana teaches A system for generating and presenting an electronic display of guidance for performing medical treatment (e.g., see abstract), comprising:
a computer-readable storage medium storing instructions for generating and presenting an electronic display of guidance for medical treatment (e.g., see Abstract, Fig. 10; and
one or more processors configured to execute the instructions to perform (e.g., see Fig. 1) a method including:
receiving a plurality of prior data sets wherein each prior procedure data set includes one or more of: i) at least one data set specific to a target patient, ii) at least one data set specific to the patient from a health care service provider, and iii) at least one data set specific to a patient population with at least one common attribute with the target patient from a health care service provider (e.g., see Fig. 3, [0125], [0129], [0130], [0148] teaching a plurality of data sets including specific patient data as received from the patient, specific patient data as received from the patient’s primary care provider, and prior patient data of a similar type based on similar variables and/or attributes);
identifying objective data identifying a predicted outcome of a medical treatment for a target patient (e.g., see [0144], [0186], [0271], [0284] teaching identifying various parameters for aiding in the prediction of a successful treatment plan);
identifying a pattern across the plurality of prior data sets by clustering one or more other patients from the patient population into one or more sub-cohorts within the patient populations and adding the target patient and the sub-cohort, the pattern describing a characteristic of the medical treatment that achieves the patient outcome defined by the objective data (e.g., see [0032], [0145], [0148], [0155], [0166], [0271], [0284] teaching treatment options to recommend based on patients having similar symptoms, patient type, procedure, etc., wherein the data is continuously updated and aggregated for optimal collection of data of most similar matches and further identifying a potential success rate and the continued analysis of various parameters for prediction of a successful treatment);
receiving information about an instance of the medical treatment to be performed in the future to the target patient from a health care provider (e.g., see [0197], [0201] wherein the system can further schedule specific treatment for the patient);
automatically generating guidance for performing the medical treatment based on the characteristic identified by the pattern and the information received about the instance of the medical treatment to be performed (e.g., see [0201] teaching generating patient preparation information for the procedure they are to receive. See also [0204] teaching providing data for simulated surgery for the planned procedure. Notably, both the patient pre-surgery preparation and surgeon simulate surgery read upon the claimed limitation);
inputting the at least one data set specific to the target patient and the guidance for performing the medical treatment to update the instructions for generating and presenting an electronic display of guidance for medical treatment (e.g., see [0311], [0316], [0324] wherein patient monitored data can be used to determine the effectiveness of the treatment plan and make updates/modifications to the recommended treatment plan, wherein the recommended treatments can be displayed, see [0150]); and
generating and presenting an electronic display of the guidance for performing the medical treatment, wherein the medical treatment is a robotic surgical procedure (e.g., see [0150], [0201], [0204], [0266] wherein the guidance information is provided to a user and the procedure can be a robotic procedure).
While Nawana teaches recommending surgical plans and further teaches tracking the use of surgical robots and new innovations during surgical procedures, Nawana fails to explicitly teach providing guidance for performing the medical treatment to a surgical robot.
However, in the same field of endeavor of medical procedures, Kim teaches providing guidance for performing the medical treatment to a surgical robot (e.g., see [0026]-[0027] teaching recommending movement of the surgical robot to treat the patient). Accordingly, it would have been obvious to modify Nawana in view of Kim with a reasonable expectation of success. One would have been motivated to make the modification in order to enhance an accuracy, precision, and delicacy of surgical procedures (e.g., see [0005] of Kim).
As to claims 17 and 18, the claims are directed to the system performing the computer-implemented method of claims 12 and 13 and are similarly rejected.
As to claim 19, Nawana teaches A computer-implemented method for generating and presenting an electronic display of pre-operative guidance for surgical procedure to treat a target patient with a musculoskeletal disease or musculoskeletal disorder (e.g., see abstract), comprising:
receiving, by one or more processors, a plurality of prior procedure data sets wherein each prior procedure data set includes one or more of: i) at least one data set specific to the target patient from the patient, ii) at least one data set specific to the patient from a health care service provider, and iii) at least one data set specific to a patient population with at least one common attribute with the target patient from a health care service provider, wherein each of the patients of the patient population received the same surgical procedure as the surgical procedure for the target patient (e.g., see Fig. 3, [0125], [0129], [0130], [0148], [0272] teaching a plurality of data sets including specific patient data as received from the patient, specific patient data as received from the patient’s primary care provider, and prior patient data of a similar type and procedure based on similar variables and/or attributes);
executing a simulation-based algorithm to determine whether the surgical procedure for the target patient is predicted to be more difficult than an average difficulty of the surgical procedure based on the at least one data set specific to the target patient from the patient, the at least one data set specific to the patient from a health care provider, and the at least one data set specific to a patient population with at least one common attribute with the target patient from a health care service provider, wherein the simulation-based algorithm is an agent-based model, where one or more other patients from the patient population are established as one or more agents with characteristics identified as similar to the target patient, wherein the characteristics include one or more of: age, co-morbidities, treatment regime, probability of adverse outcomes, and expected timespan-related outcomes (e.g., see [0145], [0148], [0155], [0166] teaching treatment options to recommend based on similar patient types/attributes and further identifying a potential success rate, wherein an attribute includes the patient’s age, height, weight, symptoms, test results, procedure type, functional outcomes, etc.);
automatically generating, by the one or more processors, a [difficulty] rating for the surgical procedure to treat the target patient (e.g., see [0145], [0148], [0155], [0166] teaching identifying potential success rates for the treatment); and
inputting the at least one data set specific to the target patient to update the simulation-based algorithm (e.g., see [0311], [0316], [0324] wherein patient monitored data can be used to determine the effectiveness of the treatment plan and make updates/modifications to the recommended treatment plan);
generating and presenting an electronic display prior to the surgical procedure (e.g., see [0201] wherein the display is presented prior to a surgical procedure); and
executing a feedback loop to update the simulation-based algorithm (e.g., see Figs. 36-39, [0162], [0164]-[0165] wherein feedback is provided by the patient and other users to modify the treatment plan for similar patients. See also Figs. 36-39 illustrating a loop based on received data (i.e., feedback) to maintain the original treatment or recommend a new one), wherein the feedback loop is based on:
the at least one data set specific to the target patient (e.g., see [0166] wherein the recommended treatment plan can be updated based on benchmarks such as progress in addressing symptoms); and
the [difficulty] rating for the target patient (e.g., see [0165] wherein a treatment can be modified based on the success rate of a treatment plan); and the real-time patient data collected during the treatment (e.g., see [0148] wherein the treatment recommendation is based on real-time patient data and is continually reviewed to provide more accurate recommendations for treatment).
While Nawana teaches identifying a success rate for a particular procedure, including surgical procedures, Nawan fails to teach a difficulty rating.
However, in the same field of endeavor of medical procedures, Kim teaches a difficulty rating (e.g., see [0026]-[0027] teaching displaying a risk/difficulty level associated with a particular procedure). Accordingly, it would have been obvious to modify Nawana in view of Kim with a reasonable expectation of success. One would have been motivated to make the modification in order to enhance an accuracy, precision, and delicacy of surgical procedures (e.g., see [0005] of Kim).
As to claim 20, the rejection of claim 19 is incorporated. Nawana further teaches executing an algorithm, stored in a non-transitory computer-readable storage medium, to identify a pattern across the plurality of prior procedure data sets, the pattern describing whether the surgical procedure for the target patient is predicted to be more difficult than the average difficulty of the surgical procedure based on the at least one data set specific to the target patient from the patient, the at least one data set specific to the patient from a health care provider, and the at least one data set specific to a patient population with at least one common attribute with the target patient from a health care service provide for each of a (e.g., see [0145], [0148], [0155], [0166] teaching treatment options to recommend based on similar patient types/attributes and further identifying a potential success rate);
automatically generating, by the one or more processors, a [difficulty] rating for the surgical procedure to treat the target patient (e.g., see [0145], [0148], [0155], [0166] teaching identifying potential success rates for the treatment); and
generating and presenting an electronic display prior to the surgical procedure (e.g., see [0201] wherein the display is presented prior to a surgical procedure).
While Nawana teaches identifying a success rate for a particular procedure, including surgical procedures, Nawan fails to teach a portion of a surgical plan for the surgical procedure and a difficulty rating.
However, in the same field of endeavor of medical procedures, Kim teaches a portion of a surgical plan for the surgical procedure and a difficulty rating (e.g., see [0026]-[0027] teaching displaying a risk/difficulty level associated with a particular procedure). Accordingly, it would have been obvious to modify Nawana in view of Kim with a reasonable expectation of success. One would have been motivated to make the modification in order to enhance an accuracy, precision, and delicacy of surgical procedures (e.g., see [0005] of Kim).
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). Further, a reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co. v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); Celeritas Technologies Ltd. v. Rockwell International Corp., 150 F.3d 1354, 1361, 47 USPQ2d 1516, 1522-23 (Fed. Cir. 1998).
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.
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