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 communication is in response to the amendment received on 09/17/2025. Claims 16-35 remain pending in this application.
Claim 35 has been amended to recite “a non-transitory computer-readable medium”. Therefore, the 35 USC 101 rejection for this claim (for being directed to a non-statutory subject matter) has been withdrawn.
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 16-35 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 16-31 are drawn to a system which is within the four statutory categories (i.e. machine). Claims 32-34 are drawn to a method which is within the four statutory categories (i.e. process). Claim 35 is drawn to a non-transitory storage medium which is within the four statutory categories (i.e. manufacture).
Step 2A, Prong 1:
The claim limitations of “determine relevant parameters for treatment types from the treatment data set via a static model, said relevant parameters being decisive for success of said treatment types, and … for an upcoming treatment intervention, compares for the relevant parameters values of the upcoming treatment intervention with the corresponding values of the plurality of preceding treatment interventions, … determines at least one selected treatment type from the group of treatment types with respect to a successful treatment intervention, and …provides a treatment suggestion about the at least one selected treatment type …” of claim 16 and the limitations of “…reading of a treatment data set of preceding treatment interventions from a memory device by a data processing device; determining a correlation between successful treatment types and a selection quantity of relevant parameters from the treatment data set by a static model by the data processing device, wherein the relevant parameters significantly influence the successful treatment type; reading of an initial data set by the data processing device based on the image of the patient’s physiology; calculating a healthy-state data set from the initial data set by the data processing device; determining differences or deviations of parameter information by comparing the healthy- state data set with the initial data set by the data processing device; filtering of the determined differences or deviations with regard to the determined relevant parameters by the data processing device; comparing the filtered differences with those corresponding to historically successful treatment interventions, and determining a quantity of historical treatment data for which the differences lie within a predetermined tolerance range by the data processing device…; evaluating the surgical intervention on the patient’s physiology;…” of claim 32 are directed to an abstract idea of “certain method of organizing human activity” with a recitation of a generic computing device (data processing device).
Claim 35 recites the same limitations and therefore, is rejected for the same reason given for claim 32.
These limitations correspond to certain methods of organizing human activity. This is a method of managing interactions between people, such as user following rules and instructions. The mere nominal recitation of a generic processing device does not take the claims out of the methods of organizing human interactions grouping.
In particular the current specification recites “The system 10 comprises a data processing device 12, a memory device 14, an input device 16, a notice device 18, an analysis device 20 and a stockage device 22. The aforementioned components of the system 10 may be positioned centrally, at the same location, or spatially distributed. For example, the data processing device 12 may be centrally configured and positioned, for example as a computer. Alternatively, the data processing device 12 may be configured by spatially distributed computers or servers, for example using a cloud service. The same applies to the memory device 14, which may be configured centrally or spatially distributed. The memory device 14 may be integrated into the data processing device 12 or may be included in it.” On page 19, lines 12-23.
Thus, the claims recite an abstract idea.
After considering all claim elements, both individually and in combination and in ordered combination, it has been determined that the claims do not amount to significantly more than the abstract idea itself.
Dependent claims also correspond to an abstract idea of certain methods of organizing human activity, such as claim 18 recites “determine at least a part of the values and the objectifiable parameters for providing the parameter information on the basis of the initial data set”, claim 19 recites “determine a healthy-state data set of the patient based on the initial data set”, claim 20 recites “determines the at least part of the values by comparing the healthy-state data set with the initial data set or the initial data set after computational removal of the implant.”, claim 22 recites “determine the at least one selected treatment type linked with a probability for a successful treatment intervention and that the treatment suggestion to the user comprises an indication of the probability”, claim 23 recites “determine two or more selected treatment types from the group of treatment types and to provide the treatment suggestion about the two or more selected treatment types to the notice device”.
Dependent claims also recite the following limitations: claim 21 recites “computationally remove an implant existing in the bone from the initial data set for the actual state of the patient”, claim 24 recites “create a quantitative relationship between characteristic features of a respective treatment type and at least selected parameters of the parameter information for treatment interventions”, claim 25 recites “the system uses a machine learning algorithm and/or a neural network of the data processing device to create the quantitative relationship”, claim 26 recites “feature the success information in this quantitative relationship for the respective treatment intervention and uses this quantitative relationship as the basis for determining the at least one selected treatment type, wherein values of parameters that match the corresponding values of the upcoming treatment within a predetermined or predeterminable threshold are predominantly decisive parameters for the suggestion”, claim 28 recites “create the quantitative relationship without user intervention”, and these limitations correspond to performing mathematical calculations, therefore the limitation falls within the “mathematical concept” grouping of abstract ideas.
Claims 17, 27, 29, 30, 31, 33, 34 are ultimately dependent from claims 16, 32 and include all the limitations of claims 16, 32. Therefore, claims 17, 27, 29, 30, 31, 33, 34 recite the same abstract idea. Claims 17, 27, 29, 30, 31, 33, 34 describe a further limitation regarding the basis for determining a treatment. These are all just further describing the abstract idea recited in claims 16, 32, without adding significantly more.
Step 2A, Prong 2:
This judicial exception is not integrated into a practical application. In particular, claims recite the additional elements of “a data processing device; a memory device; and a notice device, wherein in the memory device, based on a plurality of preceding treatment interventions, the following is stored linked to each other and assigned or assignable to a respective treatment intervention in a treatment data set:…”, using processing device to perform the determining, providing, outputting steps.
These additional elements are hardware and software elements, these limitations are not enough to qualify as “practical application” being recited in the claims along with the abstract idea since these elements are merely invoked as a tool to apply instructions of the abstract idea in a particular technological environment, and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not provide practical application for an abstract idea (MPEP 2106.05(f) & (h)).
Claim 32 has been amended to recite “creating an image of a patient’s physiology;… and providing success information to the data processing device for incorporation into the historical treatment data” and these limitations correspond to insignificant extra-solution activities. Claim 32 also has been amended to recite “performing a surgical intervention on the patient’s physiology;”, which corresponds to insignificant application (see MPEP 2106.05 (g)).
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 claims are directed to an abstract idea.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processing device to perform both the determining and providing steps amounts 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.
The claims are not patent eligible.
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 (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 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 16-30, 32-35 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wiebe, III et al. (hereinafter Wiebe) (US 9,468,502 B2).
Claim 16 recites a medical technology system for use in a treatment of a bone with an implant, the medical technology system comprising:
a data processing device (Wiebe; col. 3, lines 62-66, col. 4, lines 28-37);
a memory device (Wiebe; col. 3, lines 62-66, col. 4, lines 28-37); and
a notice device (Wiebe; col. 3, lines 62-66, col. 4, lines 38-43),
wherein in the memory device, based on a plurality of preceding treatment interventions, the following is stored linked to each other and assigned or assignable to a respective treatment intervention in a treatment data set: parameter information comprising a plurality of objectifiable parameters that are indicative of a pathological condition of the bone; treatment information that is indicative of a conducted treatment type for the patient for the treatment intervention, from a group of preferably predefined treatment types; and success information that is indicative of the success of the treatment intervention (Wiebe; col. 3, line 67 to col. 4, line 9),
wherein the data processing device is configured to determine relevant parameters for treatment types from the treatment data set via a static model, said relevant parameters being decisive for success of said treatment types (Wiebe; col. 20, lines 8-16, col. 21, lines 11-28), and
wherein the data processing device, for an upcoming treatment intervention, compares for the relevant parameters values of the upcoming treatment intervention with the corresponding values of the plurality of preceding treatment interventions (Wiebe; col. 20, lines 8-48),
wherein the data processing device determines at least one selected treatment type from the group of treatment types with respect to a successful treatment intervention (Wiebe; col. 23, lines 16-44), and
wherein the data processing device provides a treatment suggestion about the at least one selected treatment type to a user at the notice device (Wiebe; col. 25, lines 32-44).
Claim 17 recites the system according to claim 16, wherein a deviation between the values of the upcoming treatment intervention and the corresponding values of the preceding treatment interventions for the determined relevant parameters lie within a predetermined tolerance range (Wiebe; col. 25, lines 32-44).
Claim 18 recites the system according to claim 16, wherein the system comprises an analysis device for providing an initial data set that is indicative of an actual state of the bone, and wherein the data processing device is configured and programmed to determine at least a part of the values and the objectifiable parameters for providing the parameter information on the basis of the initial data set (Wiebe; col. 23, lines 16-44).
Claim 19 recites the system according to claim 18, wherein the data processing device is configured and programmed to computationally determine a healthy-state data set of the patient based on the initial data set (Wiebe; col. 25, lines 32-44).
Claim 20 recites the system according to claim 19, wherein the data processing device determines the at least part of the values by comparing the healthy-state data set with the initial data set or the initial data set after computational removal of the implant (Wiebe; col. 13, lines 20-39).
Claim 21 recites the system according to claim 16, wherein the data processing device is configured and programmed to computationally remove an implant existing in the bone from the initial data set for the actual state of the patient (Wiebe; col. 13, lines 20-39).
Claim 22 recites the system according to claim 16, wherein the data processing device is configured and programmed to determine the at least one selected treatment type linked with a probability for a successful treatment intervention and that the treatment suggestion to the user comprises an indication of the probability (Wiebe; col. 25, lines 32-44).
Claim 23 recites the system according to claim 16, wherein the data processing device is configured and programmed to determine two or more selected treatment types from the group of treatment types and to provide the treatment suggestion about the two or more selected treatment types to the notice device (Wiebe; col. 25, lines 32-44).
Claim 24 recites the system according to claim 16, wherein the data processing device is configured and programmed to create a quantitative relationship between characteristic features of a respective treatment type and at least selected parameters of the parameter information for treatment interventions (Wiebe; col. 25, lines 32-44).
Claim 25 recites the system according to claim 24, wherein the system uses a machine learning algorithm and/or a neural network of the data processing device to create the quantitative relationship (Wiebe; col. 21, lines 11-28).
Claim 26 recites the system according to claim 24, wherein the data processing device is configured and programmed to feature the success information in this quantitative relationship for the respective treatment intervention and uses this quantitative relationship as the basis for determining the at least one selected treatment type, wherein values of parameters that match the corresponding values of the upcoming treatment within a predetermined or predeterminable threshold are predominantly decisive parameters for the suggestion (Wiebe; col. 25, lines 32-44).
Claim 27 recites the system according to claim 25, wherein the selected treatment type is selected if the treatment intervention with the selected treatment type was successful (Wiebe; col. 25, lines 32-44).
Claim 28 recites the system according to claim 24, wherein the data processing device is configured to be self-learning and programmed to create the quantitative relationship without user intervention (Wiebe; col. 21, lines 11-28).
Claim 29 recites the system according to claim 16, wherein the system comprises at least one input device for receiving data input from a user after completion of the treatment intervention, and wherein the data processing device is configured and programmed to create the success information based on the data input and to store it together with the parameter information and the treatment information in the treatment data set (Wiebe; col. 24, lines 54-67).
Claim 30 recites the system according to claim 29, wherein the input device is at least one portable additional device arranged spatially remote from the data processing device, or wherein the at least one additional device comprises the input device, wherein a user application program is stored executably on the additional device, and wherein the data input of the user can be transmitted from the additional device to the data processing device via a communication connection (Wiebe; col. 29, lines 18-23).
Claim 32 has been amended to recite a computer-assisted surgical method comprising the steps of:
creating an image of a patient’s physiology (Wiebe; col. 29, lines 47-54);
reading of a treatment data set of preceding treatment interventions from a memory device by a data processing device (Wiebe; col. 3, line 67 to col. 4, line 9);
determining a correlation between successful treatment types and a selection quantity of relevant parameters from the treatment data set by a static model by the data processing device, wherein the relevant parameters significantly influence the successful treatment type (Wiebe; col. 20, lines 8-16, col. 21, lines 11-28);
reading of an initial data set by the data processing device based on the image of the patient’s physiology (Wiebe; col. 3, line 67 to col. 4, line 9);
calculating a healthy-state data set from the initial data set by the data processing device (Wiebe; col. 20, lines 8-48);
determining differences or deviations of parameter information by comparing the healthy- state data set with the initial data set by the data processing device (Wiebe; col. 20, lines 8-48);
filtering of the determined differences or deviations with regard to the determined relevant parameters by the data processing device (Wiebe; col. 25, lines 32-44);
comparing the filtered differences with those corresponding to historically successful treatment interventions (Wiebe; col. 25, lines 32-44), and
determining a quantity of historical treatment data for which the differences lie within a predetermined tolerance range by the data processing device (Wiebe; col. 3, lines 46-61, col. 25, lines 32-44);
outputting the determined quantity of historical successful treatment data, in particular the treatment information, by a notice device in communication with the data processing device (Wiebe; col. 25, lines 32-44)
performing a surgical intervention on the patient’s physiology based on the treatment information (Wiebe; col. 22, lines 9-61);
evaluating the surgical intervention on the patient’s physiology (Wiebe; col. 22, lines 9-61); and
providing success information to the data processing device for incorporation into the historical treatment data (Wiebe; col. 22, lines 9-61).
Claim 33 recites the computer-assisted surgical method according to claim 32, wherein the treatment data set comprises parameter information, treatment information and/or success information of the preceding treatment interventions and/or the data processing device calculates a healthy-state data set from the initial data set with the help of a molded bone model and/or the determined quantity of historical successful treatment data is the treatment information (Wiebe; col. 20, lines 8-16, col. 21, lines 11-28).
Claim 34 recites the computer- assisted surgical method according to claim 32, wherein the treatment information comprises a group of treatment types for a treatment intervention, the parameter information comprises parameters for the treatment interventions and the success information comprises a success of the preceding treatment interventions, and the treatment data set links the respective treatment types with the parameters and the success of the treatment intervention (Wiebe; col. 3, line 67 to col. 4, line 9).
As per claim 35, it is an article of manufacture claim which repeats the same limitations of claim 32, the corresponding method claim, as a collection of executable instructions stored on machine readable media as opposed to a series of process steps. Since the teachings of Wiebe disclose the underlying process steps that constitute the method of claim 32, it is respectfully submitted that they likewise disclose the executable instructions that perform the steps as well. As such, the limitations of claim 35, are rejected for the same reasons given above for claim 32.
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 31 is rejected under 35 U.S.C. 103 as being unpatentable over Wiebe, III et al. (hereinafter Wiebe) (US 9,468,502 B2) in view of Kim (CN 104582624 A).
Claim 31 recites the system according to claim 16, wherein the static model is a principal component analysis.
Wiebe fails to expressly teach the static model is a principal component analysis. However, this feature is well known in the art, as evidenced by Kim.
In particular, Kim discloses “…the robot learning technique to realize similar strategy wherein when executed collecting sensory data from the professional operator. sensory data (e.g. input device of track) are scaled and standardized and then parameterized. learning parameter from the strategy repeating of the same task using, for example, linear sub space method (such as principal component analysis (PCA))…”.
It would have been obvious to one of ordinary skill in the art to include in the patient specific implant positioning system of Wiebe the ability to include the static system of principal component analysis as taught by Kim since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Response to Arguments
Applicant's arguments filed 09/17/2025 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed below in the order in which they appear.
Arguments about 35 USC 101 rejection:
Applicant argues that claim 16 is directed to a technology that provides identifying the parameters that are most relevant to lead to success, and generating a treatment suggestion based on those parameters, without potentially corrupting the analysis by also considering parameters that are not relevant or less relevant. Applicant argues that claim 16 recites significantly more than an abstract idea and provide an improvement to the technology.
In response, Examiner submits that claims recite “the data processing device is configured to determine relevant parameters for treatment types from the treatment data set via a static model, said relevant parameters being decisive for success of said treatment types”. The feature of “determining relevant parameters for treatment types from the treatment data set” corresponds to certain methods of organizing human activity, (user following rules and instructions). Using a generic data processing device to perform the static model in order to determine the relevant parameters corresponds to mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not provide practical application for an abstract idea.
Examiner also submits that, filtering data, so that less relevant data being analyzed may be an improvement to the outcomes, however, it is not directed to an improvement to the technology. There is no indication in the current claims nor in the current specification reciting any improvement to the functioning of the computer or any other improvement to the technology.
Applicant argues that claim 32 has been amended to recite “creating an image of a patient’s physiology; reading of a treatment data set of preceding treatment interventions from a memory device by a data processing device” and the amendment cause the claim as a whole integrating the recited judicial exception into a practical application of the exception, since the claim is directed to treating a patient and similar to the Vanda Pharmaceutical claims, it is directed to patent eligible subject matter. Applicant argues that claims 32 recites a step of creating an image of the patient’s physiology (comparable to Vanda’s “determining” step) and a step of performing a surgical intervention (comparable to Vanda’s “administering” step).
In response, Examiner submits that creating a patient’s image using a CT device is a well-understood, routine and conventional activity in the field. The limitation of “determining a correlation between successful treatment types and a selection quantity of relevant parameters from the treatment data set” corresponds to certain methods of organizing human activity, such as user following rules and instructions to make a correlation determination.
The step of “performing a surgical intervention on the patient’s physiology based on the treatment information” does not corresponds to ”a particular treatment”, since this feature corresponds to a suitable treatment to the patient. The MPEP recite “…consider a claim that recites the same abstract idea and "administering a suitable medication to a patient." This administration step is not particular, and is instead merely instructions to "apply" the exception in a generic way. Thus, the administration step does not integrate the mental analysis step into a practical application” in §2106.04(d)(2) (a. The Particularity Or Generality Of The Treatment Or Prophylaxis).
Therefore, the arguments are not persuasive and claims are rejected under 35 U.S.C. §101 as being directed to non-statutory subject matter.
Arguments about 35 USC 102 rejection:
Applicant argues that Wiebe does not teach suggest or identify the data to identify the particular “relevant parameters” that are decisive for success of said treatment types as recited in claim 16 and “significantly influence the successful treatment type” as recited in claim 32. Applicant argues that Wiebe does not compare these relevant parameters with the patient data for the upcoming intervention.
In response, Examiner submits that Wiebe discloses “The system 200 stores the outline representation in a database with sample outline representations determined for other bone portions that correspond to the portion of the bone (1610). For instance, the system 200 maintains a database of sample outline representations for a particular bone, or Subgroup or cluster of bones (e.g., a femur or a distal end of a femur) and adds the determined outline representation to the database. The sample outline representations may include outline representations determined for many different patients, including outline representations determined for bones of cadavers analyzed during a cadaver study. The sample outline representations may be complete outline representations for the particular bone or partial outline representations for the particular bone (e.g., an anterior flange profile of a distal end of femur). The system 200 also may store, in the database, other patient information related to the patient (e.g., age, gender, ethnicity, weight, height, etc.) and/or information related to the particular bone (e.g., size measurements of the particular bone). The system 200 may use the other patient information to assist in grouping the sample outline representations and matching a new patient to a grouping of the sample outline representations. The system 200 analyzes characteristics of the outline representations stored in the database (1620). For example, the system 200 determines size and/or shape characteristics of the sample outline representations stored in the database and compares the determined size and/or shape characteristics of the sample outline representations to one another. In this example, the system 200 determines a level of similarity between sample outline representations based on the comparison.” in col. 20, lines 18-48. Examiner submits that Wiebe’s system compares size/shape characteristics (parameters) of the patient’s and other patient’s information to determine the level of similarity between the comparison, therefore, determining “relevant parameters” that are decisive for success of said treatment types.
Wiebe also discloses “In addition to selecting an implant design for the patient, the system also adds 180 the three-dimensional bone data 140 to the data repository 110. Accordingly, the system is able to continue to collect additional sample data, which the system may use to redefine the implant designs included in the library stored in the database 130. As additional data is collected (e.g., when a threshold number of new samples has been collected), the system repeats the clustering and implant design definition operations discussed above. In this regard, the system is able to routinely update the library of 55 implant designs to add new implant designs to cover new groups of patients and/or modify existing designs to better cover a group of patients or cover a larger group of patients. With these updates, the library of implant designs may expand and provide better coverage of the general population.” in col. 3, lines 46-61.
Wiebe also discloses “…After rejecting the implant design, the system 200 may attempt to use another technique to generate a new implant design based on the outline representations included in the particular group. The system 200 may evaluate the new implant design to determine whether the new design matches each of the outline representations included in the particular group within the threshold degree….” in col. 22, lines 9-61.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DILEK B COBANOGLU whose telephone number is (571)272-8295. The examiner can normally be reached 8:30-5:00 ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Obeid Mamon can be reached at (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DILEK B COBANOGLU/ Primary Examiner, Art Unit 3687
Prosecution Insights