DETAILED ACTION
This action is made in response to the amendments/remarks filed on April 6, 2026. This action is made final.
Claims 1, 2, 4, 5, 8, 10, 11, 13, 14, and 17-20 are pending. Claims 6, 7, 9, 15, and 16 have been previously cancelled. Claims 3 and 12 are presently cancelled. Claims 1, 2, 4, 5, 10, 11, 13, and 18 have been amended. Claims 1, 10, and 18 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 arguments filed April 6, 2026 have been fully considered.
Applicant’s arguments with respect to the prior art rejection has been fully considered but is considered moot in light of the new grounds of rejection.
Applicant’s argument with respect to the previous 101 rejection has been fully considered but is not persuasive.
Applicant argues the claimed invention recites an improvement in technology. However, the examiner respectfully disagrees.
MPEP 2106.04(d)(1) states “the word ‘improvements’ in the context of this consideration is limited to improvements to the functioning of a computer or any other technology/technical field, whether in Step 2A Prong Two or in Step 2B.” Here, there is no improvement to the computer nor is there an improvement to another technology.. Because neither type of improvement is present in the claims, an improvement to technology is not present and there is no practical application.
Applicant’s argument that the use of a neural network and search query is an improvement in technology is not persuasive. Insomuch as neural network is recited, the claimed invention is using a computer as a tool and any improvement present is an improvement to the abstract idea of, to paraphrase, identifying an appropriate therapy/medication, and therefore, is not integrated into a practical application nor amount to significantly more.
Accordingly, the previous grounds of rejection with respect to the 101 rejection is 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 1-5, 8, 10-14, and 17-20 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 1-5 and 8 recite a method of identifying a therapy, which is within the statutory category of a process. Claims 10-14 and 17 recite a system for identifying a therapy, which is within the statutory class of a machine. Claims 18-20 recite a method of identifying a therapy, which is within the statutory category of a process.
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 1-5, 8, 10-14, and 17-20, 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:
Claims 1, 10, and 18 (claim 1 being representative)
Generating, by a processor, a nodal graph data structure comprising a plurality of nodes connected by edges indicating relationships between the nodes, the plurality of nodes corresponding to one or more genetic mutations, one or more molecular alterations, and one or more therapy protocols, nodes for the one or more molecular alterations and for the one or more therapy protocols each at the end of the nodal graph data structure; receiving, by a processor, an image of a tumor of a patient; extracting, by a processor executing an object recognition machine learning model, one or more visual attributes of the tumor using the image of the tumor as input; executing, by a processor, a neural network using the one or more visual attributes as input to predict a genetic mutation of the patient; querying, by a processor, the nodal graph data structure using the genetic mutation to traverse a first set of edges of the nodal graph data structure by: identifying a node corresponding to the genetic mutation and traversing the first set of edges connected with the node to identify a node for a molecular alteration of the patient at a first end of the nodal graph data structure; generating, by a processor, a nodal graph data structure comprising a plurality of nodes connected by edges indicating relationships between the nodes, the plurality of nodes corresponding to one or more genetic mutations, one or more molecular alterations, and one or more therapy protocols, nodes for the one or more molecular alterations and for the one or more therapy protocols each at the end of the nodal graph data structure; querying, by a processor, the nodal graph data structure using the genetic mutation to traverse a first set of edges of the nodal graph data structure by: identifying a node corresponding to the genetic mutation and traversing the first set of edges connected with the node to identify a node for a molecular alteration of the patient at a first end of the nodal graph data structure; generating, by the processor, a search query for the nodal graph data structure constrained by an identification of the molecular alteration and the genetic mutation, the identification of the molecular alteration restricting paths of the nodal graph data structure that can be traversed based on execution of the search query; executing, by a processor, the search query on the nodal graph data structure to traverse a third set of edges of the nodal graph data structure by: identifying the node corresponding to the genetic mutation based on the identification of the genetic mutation in the constrained search query; identifying a second set of edges connected with the node corresponding to the genetic mutation and having a node corresponding to a first therapy protocol at a second end of the nodal graph data structure and a third set of edges connected with the node corresponding to the genetic mutation and having a node corresponding to a second therapy protocol at a third end of the nodal graph data structure; selecting and traversing the third set of edges instead of the second set of edges based on the identification of the molecular alteration in the constrained search query restricting traversal of the second set of edges; and identifying the node corresponding to the second therapy protocol associated with the tumor at the third end of the nodal graph data structure based on the traversal of the third set of edges; and aggregating, by a processor, the patient into a list of patients corresponding to the second therapy protocol based on the identification of the nod corresponding to the second therapy protocol associated with the tumor
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. That is, other than reciting a processor, the claimed invention amounts to managing personal behavior. For example, but for the noted computer elements, the claim encompasses a person following rules or instructions to analyze data for determining one or more therapy protocols for a patient based on identifying one or more genetic mutations or molecular alterations based on the therapy protocol in the manner described in the abstract idea. 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.
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 (“a processor”—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.
The claims further recite a “object recognition machine learning model” and “neural network” and the execution of those models. When given the broadest reasonable interpretation in light of the specification, the use of the trained model and neural network provides nothing more than mere instructions to implement the abstract idea, supra. July 2024 Subject Matter Eligibility Examples, Example 47, Claim 2, discussion of item (c) at Pgs. 7-9. See MPEP 2106.05(f). MPEP 2106.05(f); July 2024 Subject Matter Eligibility Examples, Example 47, Claim 2, discussion of items (d) and (e) at Pgs. 8-9.
The claims only manipulate abstract data elements as part of performing the abstract idea. 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 (“a processor”—see Specification Fig. 1, [0002]-[0003] 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).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of executing the object recognition machine learning model and neural network was considered to be part of the abstract idea and “apply it,” respectively. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. The use of the models represented saying “apply it” and has been revaluated under the “significantly more” analysis and does not provide “significantly more” to the abstract idea. MPEP 2106.05(A) indicates also indicates that merely adding the words “apply it” or equivalent use cannot provide significantly more. Accordingly, even in combination, this additional element does not provide significantly more. As such the claim is not patent eligible.
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, 4-5, 11, 13-14 and 19-20 merely recite additional data for training or inputting/outputting, and claims 8 and 17 merely recites identifying which alteration indicates a therapy resistance, which covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions).
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, 2, 8, 10, 11, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tiwari et al. (USPPN: 2022/0156935; hereinafter Tiwari) in further view of Kobayashi et al. (USPPN: 2021/0263933; hereinafter Kobayashi), Utro et al. (USPPN: 2021/0012861; hereinafter Utro), and Grove et al. (USPPN: 2015/0356730; hereinafter Grove).
As to claim 1, Tiwari teaches A method for constraining search queries for efficient nodal graph data structure retrieval (e.g., see Abstract. Notably, the “constraining search queries for efficient nodal graph data structure retrieval” is interpreted as a statement reciting purpose or intended use and provides no structural difference between the claimed invention and the prior art, see MPEP 2111.02) comprising:
receiving, by the processor, an image of a tumor of a patient (e.g., see Fig. 1, [0017] teaching receiving a medical image of a tumor of a patient);
extracting, by the processor executing machine learning model one or more visual attributes of the tumor using the image of the tumor as input (e.g., see Fig. 2, [0018], [0026] teaching a machine learning model for identifying features of the imaging scan of the tumor);
executing, by the processor, a neural network using the one or more visual attributes as input to predict a genetic mutation of the patient (e.g., see Fig. 1, [0020]-[0021], [0032] wherein a machine learning model predicts a gene mutation status based on the features extract from the imaging tumor).
While Tiwari teaches a machine learning model to predict genetic mutations of a patient based on visual attributes of a tumor, Tiwari fails to teach generating, by a processor, a nodal graph data structure comprising a plurality of nodes connected by edges indicating relationships between the nodes, the plurality of nodes corresponding to one or more genetic mutations, one or more molecular alterations, and one or more therapy protocols, nodes for the one or more molecular alterations and for the one or more therapy protocols each at the end of the nodal graph data structure; querying, by a processor, the nodal graph data structure using the genetic mutation to traverse a first set of edges of the nodal graph data structure by: identifying a node corresponding to the genetic mutation and traversing the first set of edges connected with the node to identify a node for a molecular alteration of the patient at a first end of the nodal graph data structure; generating, by the processor, a search query for the nodal graph data structure constrained by an identification of the molecular alteration and the genetic mutation, the identification of the molecular alteration restricting paths of the nodal graph data structure that can be traversed based on execution of the search query; executing, by a processor, the search query on the nodal graph data structure to traverse a third set of edges of the nodal graph data structure by: identifying the node corresponding to the genetic mutation based on the identification of the genetic mutation in the constrained search query; identifying a second set of edges connected with the node corresponding to the genetic mutation and having a node corresponding to a first therapy protocol at a second end of the nodal graph data structure and a third set of edges connected with the node corresponding to the genetic mutation and having a node corresponding to a second therapy protocol at a third end of the nodal graph data structure; selecting and traversing the third set of edges instead of the second set of edges based on the identification of the molecular alteration in the constrained search query restricting traversal of the second set of edges; and identifying the node corresponding to the second therapy protocol associated with the tumor at the third end of the nodal graph data structure based on the traversal of the third set of edges; and aggregating, by a processor, the patient into a list of patients corresponding to the second therapy protocol based on the identification of the nod corresponding to the second therapy protocol associated with the tumor.
However, in the same field of endeavor of searching for relevant data, Kobayashi teaches generating, by a processor, a nodal graph data structure comprising a plurality of nodes connected by edges indicating relationships between the nodes, the plurality of nodes corresponding to one or more [genetic mutations], one or more [molecular alterations], and one or more [therapy protocol]s, nodes for the one or more [molecular alterations] and for the one or more [therapy protocols] each at the end of the nodal graph data structure (e.g., see Figs. 3-7, [0004], [0030]-[0031] wherein a graph is generated comprising a plurality of nodes connected by edges indicating relationships of those nodes);
querying, by a processor, the nodal graph data structure using the [genetic mutation] to traverse a first set of edges of the nodal graph data structure by: identifying a node corresponding to the [genetic mutation] and traversing the first set of edges connected with the node to identify a node for a [molecular alteration of the patient] at a first end of the nodal graph data structure (e.g., see Figs. 3-7, [0037] wherein a search can be performed on the graph structure wherein an end node is identified from following the edges/path of the first node);
generating, by the processor, a search query for the nodal graph data structure constrained by an identification of the [molecular alteration] and the [genetic mutation], the identification of the [molecular alteration] restricting paths of the nodal graph data structure that can be traversed based on execution of the search query (e.g., see [0032], [0033], [0036] wherein a search query is generated for searching the graph structure having constraint data, which prevents search of data that does not satisfy the constraint data);
executing, by a processor, the search query on the nodal graph data structure to traverse a third set of edges of the nodal graph data structure by: identifying the node corresponding to the [genetic mutation] based on the identification of the [genetic mutation] in the constrained search query (e.g., see [0004], [0036]-[0040] wherein a search query is performed on the graph structure based on whether is matches the search criterion of the search query);
identifying a second set of edges connected with the node corresponding to the [genetic mutation] and having a node corresponding to a first [therapy protocol] at a second end of the nodal graph data structure and a third set of edges connected with the node corresponding to the [genetic mutation] and having a node corresponding to a second [therapy protocol] at a third end of the nodal graph data structure (e.g., see Figs. 3-7, [0004], [0030]-[0031] teaching a graph structure having a plurality of nodes connected by edges indicating relationships of those nodes);
selecting and traversing the third set of edges instead of the second set of edges based on the identification of the [molecular alteration] in the constrained search query restricting traversal of the second set of edges (e.g., see [0032], [0037], [0039], [0041] wherein nodes not included in the search query constraint rules are excluded in the search result); and
identifying the node corresponding to the second [therapy protocol] associated with the [tumor] at the third end of the nodal graph data structure based on the traversal of the third set of edges (e.g., see [0037], [0038] wherein the search result based on the search query is provided);
and aggregating, by a processor, the patient into a list of patients [corresponding to the second therapy protocol] based on the identification of the node corresponding to the [second therapy protocol associated with the tumor] (e.g., see Fig. 14, [0147] wherein the search result of the remaining nodes meeting the search criteria, which includes subject information, are output and/or saved). Accordingly, it would have been obvious to modify Tiwari in view of Kobayashi in order to quickly search graph data even with an increase scale of the graph (e.g., see [0026] of Kobayashi).
While Kobayashi teaches a nodal graph structure in which the nodes are searched and/or excluded based on the data in the search query, Kobayashi fails to teach the nodes corresponding to genetic mutation and therapy protocols and the search query constrained by an identification of genetic mutation. It is further noted that the claim language of nodes corresponding to “genetic mutation” and “therapy protocol” as well as the search constraint being a “molecular alteration” are interpreted as nonfunctional descriptive information as they are not functionally required in the claimed method. See MPEP 2111.05. The function described in the claimed method would be performed the same regardless of whether the claimed type of nodes and/or search query constraint existed. Therefore, Kobayashi teaches graphical node searching of the claimed limitation. Furthermore, it would have been obvious to substitute any type of node data to correspond to any type of information as a simple substitution. As such, it would have been obvious at the time of filing to substitute the node information including gender and age of the prior art with genetic mutation and therapy protocols because the results would have been predictable for easily identifying desired data). See KSR Int’l v. Teleflex Inc., 127 S. Ct. 1727, 1740-41, 82 USPQ2d 1385, 1396 (2007); and MPEP 2143.
Nonetheless, for the purposes of compact prosecution, and in the same field of endeavor of healthcare management, Utro teaches genetic mutation and therapy protocol (e.g., see Figs. 1, 2, [0032]-[0034] wherein a plurality of nodes and edges can be identified for identifying those nodes which correspond to a genomic alteration and further identifying corresponding edges and nodes, wherein a treatment is identified by the mutations).
Accordingly, it would have been obvious to modify Tiwari in view of Utro with a reasonable expectation of success. One would have been motivated to make the modification in order to predict drug resistance thereby improving treatment options (e.g., see [0003] of Utro).
While Tiwari teaches machine learning models for identifying one or more visual attributes of the tumor, Tiwari fails to explicitly teach the use of object recognition.
However, in the same field of endeavor of healthcare management, Grove teaches object recognition (e.g., see [0040], [0069] teaching object recognition in machine learning). Accordingly, it would have been obvious to modify Tiwari in view of Grove before the effective filing date with a reasonable expectation of success. One would have been motivated to make the modification to optimize prognostic tools utilizing minimally-invasive imaging techniques (e.g., see [0193] of Grove).
As to claim 2, the rejection of claim 1 is incorporated. Tiwari-Grove further teaches the object recognition machine learning model is a machine learning model trained based on historical data of a plurality of images of tumors, each of the plurality of images of tumors associated with a list of one or more visual attributes (e.g., see [0025], [0034], [0041] of Tiwari teaching the training data based on historical data of a plurality of known imaging scans and features. See also [0069] of Grove explicitly teaching object recognition model).
As to claim 8, the rejection of claim 1 is incorporated. Tiwari fails to teach wherein the molecular alteration indicates a resistance to the first therapy protocol.
However, in the same field of endeavor of healthcare management, Utro teaches wherein the molecular alteration indicates a resistance to the first therapy protocol (e.g., see [0015], [0016], [0034] teaching using patient profile data to predict a genomic alteration of the user for which a treatment and/or different treatment are determined). Accordingly, it would have been obvious to modify Tiwari-Hogue in view of Utro with a reasonable expectation of success. One would have been motivated to make the modification in order to predict drug resistance thereby improving treatment options (e.g., see [0003] of Utro).
As to claims 10-12, the claims are directed to the system implementing the method of claims 1-3 and are similarly rejected.
Claim(s) 4, 5, 13, and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over TiwarI, Kobayashi, and Utro, as applied above, and in further view of Hogue et al. (USPPN: 2018/0102190; hereinafter Hogue).
As to claim 4, the rejection of claim 1 is incorporated. While Tiwari teaches the second model using one or more visual attributes (see rejection above) and Utro teaches retrieving, by the processor, patient attributes of the patient from a database (e.g., see Fig. 1, [0027], [0037] teaching retrieving a patient profile), Tiwari-Utro fail to teach wherein executing the neural network comprises: executing, by the processor, the second model using the one or more visual attributes and the patient attributes as input.
However, in the same field of endeavor of medical diagnosis systems, Hogue teaches retrieving, by the processor, patient attributes of the patient from a database; and executing, by the processor, the neural network using the one or more visual attributes and the patient attributes as input (e.g., see [0081], [0094] teaching a machine learning model using scientific and clinical knowledge as inputs, including patient demographic details and genetic alterations in tumor data, for providing a treatment plan). Accordingly, it would have been obvious to modify Tiwari in view of Hogue with a reasonable expectation of success. One would have been motivated to make the modification in order to improve upon healthcare management by increasing customization based on personal information and treatment options (e.g., see [0003] of Hogue).
As to claim 5, the rejection of claim 4 is incorporated. Tiwari-Kobayashi-Utro-Hogue further teach wherein executing the neural network comprises: determining, by the processor, a confidence score for the genetic mutation based on the one or more visual attributes and the patient attributes; and selecting, by the processor, the genetic mutation based on the confidence score (e.g., see [0038]-[0039] of Tiwari wherein a probability score can further be provided based on the extracted features. See also [0037] of Utro and [0081] of Hogue teaching further using patient attributes for customized treatment plans).
As to claims 13 and 14, the claims are directed to the system implementing the method of claims 4 and 5 and are similarly rejected.
Claim(s) 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tiwari et al. (USPPN: 2022/0156935; hereinafter Tiwari) in further view of Kobayashi et al. (USPPN: 2021/0263933; hereinafter Kobayashi), and Utro et al. (USPPN: 2021/0012861; hereinafter Utro),
As to claim 18, Tiwari teaches A method (e.g., see Abstract) comprising:
Identifying, by the processor, one or more features from an image of a tumor of the patient (e.g., see Fig. 2, [0018], [0026] identifying features of the imaging scan of the tumor);
predicting, by the processor, using a model, a genetic mutation of the patient according to the one or more features (e.g., see Fig. 1, [0020]-[0021], [0032] wherein a machine learning model predicts a gene mutation status based on the features extract from the imaging tumor).
While Tiwari teaches the method can be used to facilitate monitoring and treatment of medical conditions and further teaches training the machine learning models using clinical data, Tiwari fails to explicitly teach receiving clinical information of a patient; generating, by a processor, a nodal graph data structure comprising a plurality of nodes connected by edges indicating relationships between the nodes, the plurality of nodes corresponding to one or more genetic mutations, one or more molecular alterations, and one or more therapy protocols, nodes for the one or more molecular alterations and for the one or more therapy protocols each at the end of the nodal graph data structure; generating, by the processor, a search query for the nodal graph data structure constrained by an identification of the molecular alteration and the genetic mutation, the identification of the molecular alteration restricting paths of the nodal graph data structure that can be traversed based on execution of the search query; executing, by a processor, the search query on the nodal graph data structure to traverse a third set of edges of the nodal graph data structure by: identifying the node corresponding to the genetic mutation based on the identification of the genetic mutation in the constrained search query; identifying a second set of edges connected with the node corresponding to the genetic mutation and having a node corresponding to a first therapy protocol at a second end of the nodal graph data structure and a third set of edges connected with the node corresponding to the genetic mutation and having a node corresponding to a second therapy protocol at a third end of the nodal graph data structure; selecting and traversing the third set of edges instead of the second set of edges based on the identification of the molecular alteration in the constrained search query restricting traversal of the second set of edges; and identifying the node corresponding to the second therapy protocol associated with the tumor at the third end of the nodal graph data structure based on the traversal of the third set of edges; and aggregating, by a processor, the patient into a list of patients corresponding to the second therapy protocol based on the identification of the nod corresponding to the second therapy protocol associated with the tumor.
However, in the same field of endeavor of searching for relevant data, Kobayashi teaches generating, by a processor, a nodal graph data structure comprising a plurality of nodes connected by edges indicating relationships between the nodes, the plurality of nodes corresponding to one or more [genetic mutations], one or more [molecular alterations], and one or more [therapy protocol]s, nodes for the one or more [molecular alterations] and for the one or more [therapy protocols] each at the end of the nodal graph data structure (e.g., see Figs. 3-7, [0004], [0030]-[0031] wherein a graph is generated comprising a plurality of nodes connected by edges indicating relationships of those nodes);
querying, by a processor, the nodal graph data structure using the [genetic mutation] to traverse a first set of edges of the nodal graph data structure by: identifying a node corresponding to the [genetic mutation] and traversing the first set of edges connected with the node to identify a node for a [molecular alteration of the patient] at a first end of the nodal graph data structure (e.g., see Figs. 3-7, [0037] wherein a search can be performed on the graph structure wherein an end node is identified from following the edges/path of the first node);
generating, by the processor, a search query for the nodal graph data structure constrained by an identification of the [molecular alteration] and the [genetic mutation], the identification of the [molecular alteration] restricting paths of the nodal graph data structure that can be traversed based on execution of the search query (e.g., see [0032], [0033], [0036] wherein a search query is generated for searching the graph structure having constraint data, which prevents search of data that does not satisfy the constraint data);
executing, by a processor, the search query on the nodal graph data structure to traverse a third set of edges of the nodal graph data structure by: identifying the node corresponding to the [genetic mutation] based on the identification of the [genetic mutation] in the constrained search query (e.g., see [0004], [0036]-[0040] wherein a search query is performed on the graph structure based on whether is matches the search criterion of the search query);
identifying a second set of edges connected with the node corresponding to the [genetic mutation] and having a node corresponding to a first [therapy protocol] at a second end of the nodal graph data structure and a third set of edges connected with the node corresponding to the [genetic mutation] and having a node corresponding to a second [therapy protocol] at a third end of the nodal graph data structure (e.g., see Figs. 3-7, [0004], [0030]-[0031] teaching a graph structure having a plurality of nodes connected by edges indicating relationships of those nodes);
selecting and traversing the third set of edges instead of the second set of edges based on the identification of the [molecular alteration] in the constrained search query restricting traversal of the second set of edges (e.g., see [0032], [0037], [0039], [0041] wherein nodes not included in the search query constraint rules are excluded in the search result); and
identifying the node corresponding to the second [therapy protocol] associated with the [tumor] at the third end of the nodal graph data structure based on the traversal of the third set of edges (e.g., see [0037], [0038] wherein the search result based on the search query is provided);
and aggregating, by a processor, the patient into a list of patients [corresponding to the second therapy protocol] based on the identification of the node corresponding to the [second therapy protocol associated with the tumor] (e.g., see Fig. 14, [0147] wherein the search result of the remaining nodes meeting the search criteria, which includes subject information, are output and/or saved). Accordingly, it would have been obvious to modify Tiwari in view of Kobayashi in order to quickly search graph data even with an increase scale of the graph (e.g., see [0026] of Kobayashi).
While Kobayashi teaches a nodal graph structure in which the nodes are searched and/or excluded based on the data in the search query, Kobayashi fails to teach the nodes corresponding to genetic mutation and therapy protocols and the search query constrained by an identification of genetic mutation. It is further noted that the claim language of nodes corresponding to “genetic mutation” and “therapy protocol” as well as the search constraint being a “molecular alteration” are interpreted as nonfunctional descriptive information as they are not functionally required in the claimed method. See MPEP 2111.05. The function described in the claimed method would be performed the same regardless of whether the claimed type of nodes and/or search query constraint existed. Therefore, Kobayashi teaches graphical node searching of the claimed limitation. Furthermore, it would have been obvious to substitute any type of node data to correspond to any type of information as a simple substitution. As such, it would have been obvious at the time of filing to substitute the node information including gender and age of the prior art with genetic mutation and therapy protocols because the results would have been predictable for easily identifying desired data). See KSR Int’l v. Teleflex Inc., 127 S. Ct. 1727, 1740-41, 82 USPQ2d 1385, 1396 (2007); and MPEP 2143.
Nonetheless, for the purposes of compact prosecution, and in the same field of endeavor of healthcare management, Utro teaches genetic mutation and therapy protocol (e.g., see Figs. 1, 2, [0032]-[0034] wherein a plurality of nodes and edges can be identified for identifying those nodes which correspond to a genomic alteration and further identifying corresponding edges and nodes, wherein a treatment is identified by the mutations). Utro additionally teaches receiving clinical information of a patient (e.g., see [0034], [0037] wherein patient profile data is received).
Accordingly, it would have been obvious to modify Tiwari in view of Utro with a reasonable expectation of success. One would have been motivated to make the modification in order to predict drug resistance thereby improving treatment options (e.g., see [0003] of Utro).
As to claim 19, the rejection of claim 18 is incorporated. Tiwari-Utro further teaches predicting, by the processor, using the model, a second genetic mutation according to the one or more features (e.g., see [0019] of Tiwari wherein the model predicts against at least two mutations); and identifying, by the processor, a second therapy protocol according to a combination of the first genetic mutation and the second genetic mutation and the clinical information (e.g., see [0034] of Utro teaching identifying a treatment for the patient based on a plurality of data including genetic mutations and clinical information and further identifying a different or combination treatment). Accordingly, it would have been obvious to modify Tiwari-Hogue in view of Utro with a reasonable expectation of success. One would have been motivated to make the modification in order to predict drug resistance thereby improving treatment options (e.g., see [0003] of Utro).
As to claim 20, the rejection of claim 18 is incorporated. Tiwari further teaches predicting, by the processor, using the model, more than two genetic mutations of the patient according to the one or more features (e.g., see [0019], [0052] wherein the model predicts against at least two mutations and can be used for specific gene mutations (i.e., more than two)); and determining, by the processor, chemotherapy as the second therapy protocol according to more than two genetic mutations associated with the tumor of the patient (e.g., see [0017]-[0019] [0034] of Utro teaching identifying a treatment for the patient based on a plurality of data including genetic mutations and clinical information and further identifying a different or combination treatment, wherein a treatment includes chemotherapy). Accordingly, it would have been obvious to modify Tiwari in view of Utro with a reasonable expectation of success. One would have been motivated to make the modification in order to predict drug resistance thereby improving treatment options (e.g., see [0003] of Utro).
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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/STELLA HIGGS/Primary Examiner, Art Unit 3681
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