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 .
DETAILED ACTION
In the response filed on 16 June 2026 the following has occurred: claims 1-4 and 8-14 have been amended; claims 5 and 6 have been canceled; claims 15-20 are newly added.
Now claims 1-4 and 7-20 are pending.
Information Disclosure Statement
The Information Disclosure Statement(s) filed on 27 January 2026, has been considered by the Examiner.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
a statistical data acquisition unit in claims 11
a degree of contribution calculating unit in claims 12
a therapeutic action searching unit in claims 13
a therapy target searching unit in claims 14
The various units are being read in view of Applicant’s specification paragraphs [0031]-[0034] as software implemented by a processor (i.e., a generic off the shelf CPU).
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim(s) 4 is rejected for lack of adequate written description.
Claim(s) 4 is rejected for lack of adequate written description. The claims recite functional steps for which the Applicant has not adequately described the steps in sufficient detail for one of ordinary skill in the art to conclude that the Applicant had possession of the invention. This is a new matter rejection.
Claim(s) 4 recites applying a stochastic correction. The Applicant has provided no disclosure of the word “stochastic”.
The specification does not recite the word “stochastic” anywhere in the disclosure.
This is inadequate for a person of ordinary skill in the art at the time of the invention (or filing) to conclude that the Applicant had possession of the claimed “applying a stochastic correction”. This is a new matter rejection.
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 1-4 and 7-20 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 and 8-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite method and system for calculation or searching for data using statistical analysis of data. The limitations of:
Claim 1, which is representative of claim 11
[…] input an input data set to a [… model …], the [… model …] to predict and output, using learning data including learning attribute information representing an attribute of a past therapy target learning therapy information representing a content of a therapeutic action with respect to the past therapy target, and a learning therapy result representing a result of the therapeutic action with respect to the past therapy target, from attribute information representing an attribute of a therapy target and therapy information representing a content of a therapeutic action with respect to the therapy target, a result of the therapeutic action with respect to the therapy target, wherein the input data set includes a plurality of input data elements including input attribute information representing an attribute of a therapy target and input therapy information representing a content of a therapeutic action with respect to the therapy target, the input data elements of the input data set having mutually different input attribute information and the input data elements of the input data set having identical input therapy information, […] thereby acquiring statistical data comprising a distribution of prediction results across the plurality of output data elements of a therapeutic action indicated by the input therapy information, […]: generate input attribute information of a plurality of input data elements by [… using math for …] values of a plurality of data items in accordance with at least one predetermined distribution; construct the plurality of input data elements such that the input data elements include mutually different input attribute information and identical input therapy information: input the plurality of input data elements […] to obtain a plurality of output data elements corresponding respectively to the plurality of input data elements: and
generate statistical data as the distribution of prediction results across the plurality of output data elements corresponding to the predetermined distribution, wherein [… using …] virtual learning data generated based on statistical information regarding a therapeutic action performed in the past, the virtual learning data being generated to conform to a distribution regarding the therapeutic action.
Claim 8, which is representative of claim 12
[…] input, to a [… model …] to predict and output using learning data including learning attribute information representing an attribute of a past therapy target, learning therapy information representing a content of a therapeutic action with respect to the past therapy target, and a learning therapy result representing a result of the therapeutic action with respect to the past therapy target, from attribute information representing an attribute of a therapy target and therapy information representing a content of a therapeutic action with respect to the therapy target, a result of the therapeutic action with respect to the therapy target, first input data including first input attribute information representing an attribute of a therapy target and first input therapy information representing a content of a therapeutic action with respect to the therapy target to thereby acquire a first prediction result, and generate second input data by modifying only a single data item of the first input attribute information or the first input therapy information while maintaining remaining data items constant; input, to the [… model …], second input data, to thereby acquire a second prediction result, compute a contribution value for the single modified data item based on a difference
between the first prediction result and the second prediction result; and
repeat, for each data item, the generating of the second input data, the inputting of the second input data, and the computing of the contribution value to generate a normalized contribution distribution across data items.
Claim 9, which is representative of claim 13
[…] generate a plurality of input data elements, each input data element including input attribute information representing the predetermined therapy target and input therapy information representing a content of a therapeutic action, the plurality of input data elements including identical input attribute information and mutually different input therapy information; input the plurality of input data elements to the [… model …] to obtain a plurality of prediction results corresponding to the respective input data elements; and evaluate and rank the plurality of therapeutic actions based on an aggregate statistical value derived from a distribution of the plurality of prediction results across the plurality of input data elements and select a therapeutic action suitable for the predetermined therapy target, the prediction results having been acquired by inputting a plurality of input data elements to a [… model …] to predict and output, using learning data including learning attribute information representing an attribute of a past therapy target, learning therapy information representing a content of a therapeutic action with respect to the past therapy target, and a learning therapy result representing a result of the therapeutic action with respect to the past therapy target, from attribute information representing an attribute of a therapy target and therapy information representing a content of a therapeutic action with respect to the therapy target, a result of the therapeutic action with respect to the therapy target, wherein each of the input data elements includes input attribute information representing an attribute of a therapy target and input therapy information representing a content of a therapeutic action with respect to the therapy target, the input data elements including the input therapy information elements that are mutually different.
Claim 10, which is representative of claim 14
[…] generate a plurality of input data elements, each input data element including input attribute information representing a therapy target and input therapy information representing the predetermined therapeutic action, the plurality of input data elements including identical input therapy information and mutually different input attribute information; input the plurality of input data elements to a [… model …] to obtain a plurality of prediction results corresponding to the respective input data elements; evaluate the plurality of prediction results by computing an aggregate statistical value for each therapy target based on corresponding prediction results; and rank the plurality of therapy targets based on the aggregate statistical values and select a therapy target suitable for the predetermined therapeutic action,, the prediction results having been acquired by inputting a plurality of input data elements to a [… model …], to predict and output, using learning data including learning attribute information representing an attribute of a past therapy target, learning therapy information representing a content of a therapeutic action with respect to the past therapy target, and a learning therapy result representing a result of the therapeutic action with respect to the past therapy target, from attribute information representing an attribute of the therapy target and therapy information representing a content of a therapeutic action with respect to the therapy target, a result of the therapeutic action with respect to the therapy target, wherein each of the input data elements includes input attribute information representing an attribute of a therapy target and input therapy information representing a content of a therapeutic action with respect to the therapy target, the input data elements including the input attribute information elements that are mutually different.
, as drafted, is a system, which under its broadest reasonable interpretation, covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions) via human interaction with generic computer components. That is, by a human user interacting with a processor implementing various units, the claimed invention amounts to managing personal behavior or interaction between people, the Examiner notes as stated in 2106.04(a)(2), “certain activity between a person and a computer… may fall within the “certain methods of organizing human activity” grouping”. For example, via human interaction with a processor implementing various units, the claim encompasses collection of data, organization of the collected data into a model, use of the collected data to produce a result, and providing of the result to a human user for a human user to use in making treatment determinations to organize their treatment workflow. If a claim limitation, under its broadest reasonable interpretation, covers managing personal 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.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of various units, which implements the abstract idea. The a processor implementing various units are recited at a high-level of generality (i.e., a general-purpose computers/ computer components implementing generic computer functions; see Applicant’s Specification paragraphs [0031]-[0034]) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim recites the additional elements of “sampling” and “a learner, the learner having been trained to predict and output” to implement the abstract idea. The “sampling” steps are recited at a high-level of generality (i.e., using math) and amounts to generally linking the abstract idea to a particular technological environment. The “a learner, the learner having been trained to predict and output” steps are recited at a high-level of generality (i.e., using and training in a generic manner a generic off-the shelf model) and amounts to generally linking the abstract idea to a particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a processor implementing various units to perform the noted steps amounts to no more than mere instructions to apply the exception using generic hardware components. Mere instructions to apply an exception using a generic hardware component cannot provide an inventive concept (“significantly more”).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “sampling” and “a learner, the learner having been trained to predict and output” were considered generally linking the abstract idea to particular technological environment and/or extra-solution activity. The “sampling” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Hazard (20210012246): paragraph [0021]; Anderson (20160253473): paragraphs [0127]-[0132]; Itu (20190139641): paragraphs [0052], [0066]; use of sampling is well-understood, routine and conventional. The “a learner, the learner having been trained to predict and output” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Shrager (20200411199): see below but at least paragraph [0013]; Hazard (20210012246): paragraph [0015]; Mitsumori (20210118568): paragraph [0015]; training and use of a machine learning model is well-understood, routine and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claims 2-4, 7 and 15-20 are similarly rejected because either further define the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible.
Claims 2 further describe use of population of interest, however does not recite any additional elements are therefore cannot provide a practical application and/or significantly more.
Claims 3 and 7 recites the additional element of using an iterative process to train a model and re-training a model, however this is recited at a high-level of generality (i.e., using an iterative process and data that has been re-labeled) and amounts to generally linking the abstract idea to a particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of iterative process to train a model and re-training a model, was considered generally linking the abstract idea to particular technological environment. This has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Hazard (20210012246): paragraph [0022]; Itu (20190139641): paragraph [0023]; Kasthurirarthne (20200312457): paragraphs [0010]-[0012]; using an iterative process to update models with new data is well-understood routine and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claim 4, describes mathematical concepts (i.e., an abstract idea), however does not recite any additional elements are therefore cannot provide a practical application and/or significantly more.
Claim 15-16 and 19 recite computation of a contribution value, however does not recite any additional elements are therefore cannot provide a practical application and/or significantly more.
Claim 17-18 and 20 recite ranking of data, however does not recite any additional elements are therefore cannot provide a practical application and/or significantly more.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-4, 7, 11 and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 20200411199 (hereafter “Shrager”), in view of U.S. Patent Pub. No. 20210118568 (hereafter “Mitsumori”), in further view of U.S. Patent Pub. No. 20210012246 (hereafter “Hazard”).
Regarding (Currently Amended) claim 1, Shrager teaches a statistical data acquisition apparatus (Shrager: Figures 1, 18-23, paragraph [0085], “One or more sets of training data may be generated and provided to a decision engine comprising one or more algorithms for making predictions… statistical methods and methods based on machine learning techniques. Statistical methods include penalized logistic regression, prediction analysis of microarrays (PAM), methods based on shrunken centroids, support vector machine analysis, and regularized linear discriminant analysis”) comprising:
a processor configured to execute instructions to function as a statistical data acquisition unit configured to input an input data set to a learner (Shrager: Figures 1, 18-23, paragraph [0013], “The AI-based platform uses a combination of expert collective intelligence, AI, and machine learning to dynamically generate and test novel personalized treatment hypotheses”, paragraph [0044], “obtain patient case summaries and generate and/or validate treatment rationales associated with said summaries. Individual patients or their doctors are able to input case information through a clinical case capture tool presenting selectable clinical case templates. The clinical case template may have adaptive parameters that dynamically change according to previously entered parameters to capture the unique set of information for a particular case”, paragraph [0125], “the platforms, media, methods, and applications described herein include a digital processing device, a processor, or use of the same”),
the learner having been trained to predict and output, using learning data including learning attribute information representing an attribute of a past therapy target, learning therapy information representing a content of a therapeutic action with respect to the past therapy target, and a learning therapy result representing a result of the therapeutic action with respect to the past therapy target, from attribute information representing an attribute of a therapy target and therapy information representing a content of a therapeutic action with respect to the therapy target, a result of the therapeutic action with respect to the therapy target (Shrager: Figures 1, 18-23, paragraph [0046], “a treatment option can refer to a specific treatment (e.g., active agent and/or dosing regimen) or mode of treatment (e.g., chemotherapy, surgery)… Examples of targeted therapeutic agents include”, paragraphs [0081]-[0085], “generate models that predict one or more treatment options for a clinical case and/or a cohort comprising at least one clinical case. In some instances, machine learning methods are applied to the generation of such models… Such models can be generated by providing a machine learning algorithm with training data in which the expected output is known in advance, e.g., an output in which it is known that a clinical case having a specific data set (e.g., patient information and treatment information) achieved a particular outcome or a probability in which a particular outcome was achieved within a known group of clinical cases having specific data sets… The training data for the machine learning algorithms can be provided as follows. Clinical cases with known outcomes can be grouped into cohorts based on patient information and/or treatment information… the machine learning algorithm is provided with training data that includes the classification (e.g., treatment option, outcome, etc.), thus enabling the algorithm to “learn” by comparing its output with the actual output to modify and improve the model”, paragraph [0113], “treatment history and outcomes of a cohort of clinical cases for patients diagnosed with glioblastoma”),
wherein the input data set includes a plurality of input data elements including input attribute information representing an attribute of a therapy target and input therapy information representing a content of a therapeutic action with respect to the therapy target (Shrager: Figures 1, 18-23, paragraphs [0044]-0046], “identify a similar patient cohort… a treatment option can refer to a specific treatment (e.g., active agent and/or dosing regimen) or mode of treatment (e.g., chemotherapy, surgery)”, paragraphs [0081]-[0085], “Clinical cases with known outcomes can be grouped into cohorts based on patient information and/or treatment information . For example, patient information can include patient age, gender, cancer type, cancer stage… Each feature space can comprise types of information about a case, such as biomarker expression or genetic mutations… the machine learning algorithm is provided with training data that includes the classification (e.g., treatment option, outcome, etc.), thus enabling the algorithm to “learn” by comparing its output with the actual output to modify and improve the model”. The Examiner notes patients with various differing attributes are grouped into cohorts based on similar therapeutic action applied for training of a model, which teaches what is required under the broadest reasonable interpretation),
the input data elements of the input data set having […] input attribute information and the input data elements of the input data set having identical input therapy information (Shrager: paragraph [0009], “a pre-defined patient cohort”, paragraph [0044], “identify a similar patient cohort”, paragraph [0110], “the first cohort undergoing a specific treatment is experiencing outcomes that are statistically worse than a second cohort”. The Examiner notes cohorts using the same treatment is identical input therapy information under the broadest reasonable interpretation),
the statistical data acquisition unit thereby acquiring statistical data comprising a distribution of prediction results across the plurality of output data elements of a therapeutic action indicated by the input therapy information (Shrager: Figures 1, 18-23, paragraph [0081], “predict one or more treatment options for a clinical case and/or a cohort comprising at least one clinical case”, paragraphs [0085]-[0088], “An algorithm may utilize a predictive model such as a neural network, a decision tree, a support vector machine, or other applicable model. Using the training data, an algorithm can form a classifier for classifying the case according to relevant features… The decision engine 107 can learn using information from the knowledge base 106 (e.g., using knowledge base data as the prior for determining a posterior probability distribution… calculate the posterior probabilities (e.g., of one or more treatment outcomes”, paragraph [0179], “the system or platform may provide its recommendations and/or calculated rankings to the treating physician”),
wherein the statistical data acquisition unit is further configured to: generate input attribute information of a plurality of input data elements […] values of a plurality of data items in accordance with at least one predetermined distribution (Shrager: paragraphs [0011]-[0013], “a virtual trial may be created by interpreting multiple individual treatments or studies that were previously unrelated… dynamically generate and test novel personalized treatment hypotheses”, paragraph [0069], “helping generate treatment hypotheses, deciding on treatment options, and coordinating ongoing clinical cases (e.g., virtual trials)”, paragraphs [0085]-[0088], “decision engine 107 can learn using information from the knowledge base 106 (e.g., using knowledge base data as the prior for determining a posterior probability distribution)”);
construct the plurality of input data elements such that the input data elements include […] identical input therapy information (Shrager: paragraphs [0011]-[0013], “a virtual trial may be created by interpreting multiple individual treatments or studies that were previously unrelated… dynamically generate and test novel personalized treatment hypotheses”, paragraph [0044], “identify a similar patient cohort”, paragraph [0110], “the first cohort undergoing a specific treatment is experiencing outcomes that are statistically worse than a second cohort”. The Examiner notes that “such that the input data elements include” is an intended use of the construction of input that is not required to occur. This feature has been fully considered by the Examiner; however, the limitation does not provide patentable distinction over the cited prior art because it is an intended use or result of the construction of input);
input the plurality of input data elements to the trained learner to obtain a plurality of output data elements corresponding respectively to the plurality of input data elements; and generate statistical data as the distribution of prediction results across the plurality of output data elements corresponding to the predetermined distribution (Shrager: Figures 1, 18-23, paragraph [0081], “predict one or more treatment options for a clinical case and/or a cohort comprising at least one clinical case”, paragraphs [0085]-[0089], “An algorithm may utilize a predictive model such as a neural network, a decision tree, a support vector machine, or other applicable model. Using the training data, an algorithm can form a classifier for classifying the case according to relevant features… The decision engine 107 can learn using information from the knowledge base 106 (e.g., using knowledge base data as the prior for determining a posterior probability distribution… calculate the posterior probabilities (e.g., of one or more treatment outcomes… Artificial neural networks are typically organized in layers which comprise an input layer, an output layer, and at least one hidden layer, wherein each layer comprises one or more neurons or nodes… a node receives input from the neurons in the preceding layer, changes its internal state (activation) based on the value of the received input, and generates an output based on the input and activation that is then sent towards the node in the subsequent layer”, paragraph [0179], “the system or platform may provide its recommendations and/or calculated rankings to the treating physician”), […].
Shrager may not explicitly teach (underlined below for clarity):
the input data elements of the input data set having mutually different input attribute information and the input data elements of the input data set having identical input therapy information, […]; construct the plurality of input data elements such that the input data elements include mutually different input attribute information and identical input therapy information;
Mitsumori teaches the input data elements of the input data set having mutually different input attribute information and the input data elements of the input data set having identical input therapy information, […]; construct the plurality of input data elements such that the input data elements include mutually different input attribute information and identical input therapy information (Mitsumori: paragraph [0049], “the first cohort undergoing a specific treatment is experiencing outcomes that are statistically worse than a second cohort having the same clinical profile but using a different treatment”).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the invention was made to combine the noted features of Mitsumori within teaching of Shrager since the combination of the two references is merely simple substitution of one known element for another producing a predictable result (KSR rationale B). Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself—that is, in the substitution of the mutually different features as taught by Mitsumori for the input features as taught by Shrager. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Shrager and Mitsumori may not explicitly teach (underlined below for clarity):
wherein the statistical data acquisition unit is further configured to: generate input attribute information of a plurality of input data elements by sampling values of a plurality of data items in accordance with at least one predetermined distribution;
wherein the learner is configured to be trained with virtual learning data generated based on statistical information regarding a therapeutic action performed in the past,
the virtual learning data being generated to conform to a distribution regarding the therapeutic action.
Hazard teaches wherein the statistical data acquisition unit is further configured to: generate input attribute information of a plurality of input data elements by sampling values of a plurality of data items in accordance with at least one predetermined distribution (Hazard: paragraph [0004], “require the right sampling of training data… acquiring data such that the sampling process must be very selective”, paragraph [0032], “used as a sampling process to obtain observations about unknown parts of the computer-based reasoning model and to update the model based on the new information obtained”);
wherein the learner is configured to be trained with virtual learning data generated based on statistical information regarding a therapeutic action performed in the past (Hazard: Figures 1, 4, paragraph [0015], “use existing training data and, optionally, target surprisal to create synthetic data. In some embodiments, conditions may also be applied to the creation of the synthetic data in order to ensure that training data meeting specific conditions is created”, paragraph [0047], “synthetic data may be requested to direct sampling via a reinforcement learning process”, paragraph [0100], “the techniques may be used to create synthetic data that replicates users, devices, etc.”),
the virtual learning data being generated to conform to a distribution regarding the therapeutic action (Hazard: Figures 1, 4, paragraph [0015], “use existing training data and, optionally, target surprisal to create synthetic data. In some embodiments, conditions may also be applied to the creation of the synthetic data in order to ensure that training data meeting specific conditions is created”, paragraph [0021], “the feature value may be sampled based on a uniform distribution, truncated normal, or any other bounded parametric or nonparametric distribution, between the feature bounds”, paragraph [0100], “the techniques may be used to create synthetic data that replicates users, devices, etc.”, paragraph [0248], “executed by processor 304”).
One of ordinary skill in the art before the effective filing date would have found it obvious to include sampling using a predetermined distribution and virtual data as taught by Hazard with the training and use of models for statistical determinations as taught by Shrager and Mitsumori with the motivation of “improving the quality of the model… improve the breadth of its observations” (Hazard: paragraph [0162]).
Regarding (Currently Amended) claim 2, Shrager, Mitsumori and Hazard teach the limitations of claim 1, and further teach wherein the predetermined distribution corresponds to a statistical distribution of attributes of therapy targets in a population of interest (Shrager: paragraph [0009], “a pre-defined patient cohort”, paragraph [0087], “a posterior probability distribution”, paragraph [0101], “inclusion/exclusion criteria of a trial”; Mitsumori: paragraph [0030], “acquire medical data that is present in a distributed manner”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Currently Amended) claim 3, Shrager, Mitsumori and Hazard teach the limitations of claim 1, and further teach wherein the processor is further configured to execute instructions to function as a distribution adjusting unit configured to: iteratively modify parameters of the predetermined distribution based on a comparison between generated statistical data and target statistical data specified by a user; regenerate input data elements based on the modified distribution and re-input the regenerated input data elements to the learner; and repeat the modifying of the predetermined distribution, the regenerating of the input data elements, and the re-inputting of the regenerated input data elements until the generated statistical data satisfies a convergence condition relative to the target statistical data (Shrager: paragraph [0084], “enabling the algorithm to “learn” by comparing its output with the actual output to modify and improve the model”, paragraphs [0090]-[0094], “the errors from the initial classification of the first record are fed back into the network, and are used to modify the network's algorithm in an iterative process… allows a comparison to be made between the neural network's calculated values for the output nodes to the correct or known values. Accordingly, an error may be calculated for the output nodes and then are used to adjust the weights in the hidden layers so that the output values may converge towards the correct values as the network is trained through successive rounds using the training data. The neural network uses an iterative learning process”, paragraph [0101], “process can iterate until it converges (e.g., no new options, rationales, or rankings)”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Currently Amended) claim 4, Shrager, Mitsumori and Hazard teach the limitations of claim 1, and further teach the statistical data acquisition unit is configured to: compute an output error distribution of the trained learner using validation data distinct from the learning data; and, correct each output data element by applying a stochastic correction based on the output error distribution (Shrager: paragraph [0086], “The classifier can be tested using data that was not used for training to evaluate its predictive ability. The predictive ability of the classifier can be explained using various metrics. These metrics include accuracy, specificity, sensitivity, positive predictive value, negative predictive value, which are determined for a classifier”, paragraphs [0090]-[0094], “the errors from the initial classification of the first record are fed back into the network, and are used to modify the network's algorithm in an iterative process… an error may be calculated for the output nodes… Errors are then propagated back through the system”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Original) claim 7, Shrager, Mitsumori and Hazard teach the limitations of claim 1, and further teach the learner is configured to be trained with a first learning data set, and is thereafter re-trained with a second learning data set that is different from the first learning data set (Shrager: paragraph [0044], “the updated knowledge base can be used to further train and update the one or more algorithms”, paragraph [0110], “The classifier can continuously update based on new data (e.g., administered treatment(s) and outcome or result of the treatment(s)) and re-evaluate the ongoing clinical case. Thus, the decision engine may dynamically or continuously monitor a clinical case over time and recommend a change to the existing treatment options or a new treatment based upon the updated classifier when the ranking or prioritization of the treatment options changes”).
The motivation to combine is the same as in claim 1, incorporated herein.
REGARDING CLAIM(S) 11 and 19
Claim(s) 11 and 19 is/are analogous to Claim(s) 1 and 15, thus Claim(s) 11 and 19 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 1 and 15.
Regarding (New) claim 15, Shrager, Mitsumori and Hazard teach the limitations of claim 1, and further teach function as a degree of contribution calculating unit (Hazard: paragraphs [0121]-[0125], “feature prediction contribution is determined as a conviction score. Various embodiments of determining feature prediction contribution are given herein. In some embodiments, feature prediction contribution can be used to flag what features are contributing most (or above a threshold amount) to a suggestion”, paragraph [0251], “hard-wired circuitry may be used in place of or in combination with software instructions”) configured to:
select, from the plurality of input data elements, first input data including first input attribute information and first input therapy information, and input the first input data to the learner to thereby acquire a first prediction result (Shrager: Figures 1, 18-23, paragraph [0046], “a treatment option can refer to a specific treatment (e.g., active agent and/or dosing regimen) or mode of treatment (e.g., chemotherapy, surgery)… Examples of targeted therapeutic agents include”, paragraphs [0081]-[0085], “generate models that predict one or more treatment options for a clinical case and/or a cohort comprising at least one clinical case. In some instances, machine learning methods are applied to the generation of such models… Such models can be generated by providing a machine learning algorithm with training data in which the expected output is known in advance, e.g., an output in which it is known that a clinical case having a specific data set (e.g., patient information and treatment information) achieved a particular outcome or a probability in which a particular outcome was achieved within a known group of clinical cases having specific data sets… The training data for the machine learning algorithms can be provided as follows. Clinical cases with known outcomes can be grouped into cohorts based on patient information and/or treatment information… the machine learning algorithm is provided with training data that includes the classification (e.g., treatment option, outcome, etc.), thus enabling the algorithm to “learn” by comparing its output with the actual output to modify and improve the model”, paragraph [0113], “treatment history and outcomes of a cohort of clinical cases for patients diagnosed with glioblastoma”);
generate second input data by modifying only a single data item of the first input attribute information or the first input therapy information while maintaining remaining data items constant; input the second input data to the learner to thereby acquire a second prediction result (Shrager: paragraph [0044], “The clinical case template may have adaptive parameters that dynamically change… the updated knowledge base can be used to further train and update the one or more algorithms”, paragraph [0084], “combining two or more feature spaces in a classifier instead of using a single feature space… enabling the algorithm to “learn” by comparing its output with the actual output to modify and improve the model”, paragraphs [0090]-[0094], “the errors from the initial classification of the first record are fed back into the network, and are used to modify the network's algorithm in an iterative process… an error may be calculated for the output nodes… Errors are then propagated back through the system”, paragraph [0097], “The adaptive clinical case parameters may dynamically change”, paragraph [0110], “The classifier can continuously update based on new data (e.g., administered treatment(s) and outcome or result of the treatment(s)) and re-evaluate the ongoing clinical case. Thus, the decision engine may dynamically or continuously monitor a clinical case over time and recommend a change to the existing treatment options or a new treatment based upon the updated classifier when the ranking or prioritization of the treatment options changes”, paragraph [0117], “insight provided by the biomarkers may be incorporated into a virtual trial by modifying the trial to test new treatments or treatment combinations”; Hazard: paragraph [0022], “the synthetic data case may be modified (e.g., resampled) and retested, or discarded… the generated synthetic data may be compared against at least a portion of the existing training data, and a determination may be made whether to keep the synthetic data case based on the distance of the synthetic data case to one or more elements of in the existing training data”);
and compute a contribution value for the modified data item based on a difference between the first prediction result and the second prediction result (Hazard: paragraph [0022], “the generated synthetic data may be compared against at least a portion of the existing training data, and a determination may be made… each synthetic data case generated using the techniques herein are compared to the existing training data”, paragraphs [0121]-[0125], “feature prediction contribution is determined as a conviction score. Various embodiments of determining feature prediction contribution are given herein. In some embodiments, feature prediction contribution can be used to flag what features are contributing most (or above a threshold amount) to a suggestion. Such information can be useful for either ensuring that certain features are not used for particular decision making and/or ensuring that certain features are used in particular decision making… define a feature information measure, such as familiarity conviction, such that a point's weighted distance contribution affects other points' distance contribution and compared”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (New) claim 16, Shrager, Mitsumori and Hazard teach the limitations of claim 15, and further teach wherein the degree of contribution calculating unit is further configured to repeat, for each data item, the generating of the second input data, the inputting of the second input data, and the computing of the contribution value to generate a normalized contribution distribution across the data items (Shrager: paragraph [0089], “typically normalized to a value between 0 and 1)”; Hazard: paragraph [0021], “a distribution just within the feature bounds (e.g., a uniform, normal, or other distribution within the feature bounds)”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (New) claim 17, Shrager, Mitsumori and Hazard teach the limitations of claim 15, and further teach generate a plurality of additional input data elements including identical input attribute information and mutually different input therapy information (Shrager: paragraphs [0011]-[0013], “a virtual trial may be created by interpreting multiple individual treatments or studies that were previously unrelated… dynamically generate and test novel personalized treatment hypotheses”, paragraph [0044], “identify a similar patient cohort”, paragraph [0110], “the first cohort undergoing a specific treatment is experiencing outcomes that are statistically worse than a second cohort”; Mitsumori: paragraph [0049], “the first cohort undergoing a specific treatment is experiencing outcomes that are statistically worse than a second cohort having the same clinical profile but using a different treatment”);
input the plurality of additional input data elements to the learner to obtain a plurality of additional prediction results (Shrager: paragraph [0044], “The clinical case template may have adaptive parameters that dynamically change… the updated knowledge base can be used to further train and update the one or more algorithms”, paragraph [0084], “combining two or more feature spaces in a classifier instead of using a single feature space… enabling the algorithm to “learn” by comparing its output with the actual output to modify and improve the model”, paragraphs [0090]-[0094], “the errors from the initial classification of the first record are fed back into the network, and are used to modify the network's algorithm in an iterative process… an error may be calculated for the output nodes… Errors are then propagated back through the system”, paragraph [0097], “The adaptive clinical case parameters may dynamically change”, paragraph [0110], “The classifier can continuously update based on new data (e.g., administered treatment(s) and outcome or result of the treatment(s)) and re-evaluate the ongoing clinical case. Thus, the decision engine may dynamically or continuously monitor a clinical case over time and recommend a change to the existing treatment options or a new treatment based upon the updated classifier when the ranking or prioritization of the treatment options changes”, paragraph [0117], “insight provided by the biomarkers may be incorporated into a virtual trial by modifying the trial to test new treatments or treatment combinations”); and
evaluate and rank a plurality of therapeutic actions based on an aggregate statistical value of the plurality of additional prediction results and select a therapeutic action suitable for a predetermined therapy target (Shrager: Figures 1, 18-23, paragraph [0016], “generate predictions such as treatment options (optionally ranked according to predicted efficacy) and/or treatment hypotheses”, paragraph [0081], “predict one or more treatment options for a clinical case and/or a cohort comprising at least one clinical case”, paragraph [0085], “An algorithm may utilize a predictive model such as a neural network, a decision tree, a support vector machine, or other applicable model. Using the training data, an algorithm can form a classifier for classifying the case according to relevant features”, paragraph [0088], “calculate the posterior probabilities (e.g., of one or more treatment outcomes”, paragraph [0101], “select one of a ranked list of treatment options and rationales. The selected treatment and/or outcome data can be captured and used to update the knowledge base,”, paragraph [0179], “the system or platform may provide its recommendations and/or calculated rankings to the treating physician”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (New) claim 18, Shrager, Mitsumori and Hazard teach the limitations of claim 17, and further teach wherein the statistical data acquisition unit is configured to correct each of the plurality of output data elements based on an output error distribution of the learner, and the therapeutic action searching unit is configured to evaluate and rank the plurality of therapeutic actions based on corrected prediction results corresponding to the plurality of additional input data elements (Shrager: Figures 1, 18-23, paragraph [0016], “generate predictions such as treatment options (optionally ranked according to predicted efficacy) and/or treatment hypotheses”, paragraph [0081], “predict one or more treatment options for a clinical case and/or a cohort comprising at least one clinical case”, paragraph [0085], “An algorithm may utilize a predictive model such as a neural network, a decision tree, a support vector machine, or other applicable model. Using the training data, an algorithm can form a classifier for classifying the case according to relevant features”, paragraph [0088], “calculate the posterior probabilities (e.g., of one or more treatment outcomes”, paragraph [0090], “the errors from the initial classification of the first record are fed back into the network, and are used to modify the network's algorithm in an iterative process”, paragraph [0101], “select one of a ranked list of treatment options and rationales. The selected treatment and/or outcome data can be captured and used to update the knowledge base,”, paragraph [0179], “the system or platform may provide its recommendations and/or calculated rankings to the treating physician”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (New) claim 20, Shrager, Mitsumori and Hazard teach the limitations of claim 19, and further repeat, for each data item, the generating of the second input data, the inputting of the second input data, and the computing of the contribution value to generate a normalized contribution distribution across the data items (Shrager: paragraph [0084], “enabling the algorithm to “learn” by comparing its output with the actual output to modify and improve the model”, paragraphs [0090]-[0094], “the errors from the initial classification of the first record are fed back into the network, and are used to modify the network's algorithm in an iterative process… allows a comparison to be made between the neural network's calculated values for the output nodes to the correct or known values. Accordingly, an error may be calculated for the output nodes and then are used to adjust the weights in the hidden layers so that the output values may converge towards the correct values as the network is trained through successive rounds using the training data. The neural network uses an iterative learning process”, paragraph [0101], “process can iterate until it converges (e.g., no new options, rationales, or rankings)”);
compute an output error distribution of the learner using validation data distinct from the learning data and correct output data based on the output error distribution (Shrager: paragraph [0086], “The classifier can be tested using data that was not used for training to evaluate its predictive ability. The predictive ability of the classifier can be explained using various metrics. These metrics include accuracy, specificity, sensitivity, positive predictive value, negative predictive value, which are determined for a classifier”, paragraphs [0090]-[0094], “the errors from the initial classification of the first record are fed back into the network, and are used to modify the network's algorithm in an iterative process… an error may be calculated for the output nodes… Errors are then propagated back through the system”);
generate a plurality of additional input data elements including identical input attribute information and mutually different input therapy information (Shrager: paragraphs [0011]-[0013], “a virtual trial may be created by interpreting multiple individual treatments or studies that were previously unrelated… dynamically generate and test novel personalized treatment hypotheses”, paragraph [0044], “identify a similar patient cohort”, paragraph [0110], “the first cohort undergoing a specific treatment is experiencing outcomes that are statistically worse than a second cohort”; Mitsumori: paragraph [0049], “the first cohort undergoing a specific treatment is experiencing outcomes that are statistically worse than a second cohort having the same clinical profile but using a different treatment”);
input the plurality of additional input data elements to the learner to obtain a plurality of additional prediction results (Shrager: paragraph [0044], “The clinical case template may have adaptive parameters that dynamically change… the updated knowledge base can be used to further train and update the one or more algorithms”, paragraph [0084], “combining two or more feature spaces in a classifier instead of using a single feature space… enabling the algorithm to “learn” by comparing its output with the actual output to modify and improve the model”, paragraphs [0090]-[0094], “the errors from the initial classification of the first record are fed back into the network, and are used to modify the network's algorithm in an iterative process… an error may be calculated for the output nodes… Errors are then propagated back through the system”, paragraph [0097], “The adaptive clinical case parameters may dynamically change”, paragraph [0110], “The classifier can continuously update based on new data (e.g., administered treatment(s) and outcome or result of the treatment(s)) and re-evaluate the ongoing clinical case. Thus, the decision engine may dynamically or continuously monitor a clinical case over time and recommend a change to the existing treatment options or a new treatment based upon the updated classifier when the ranking or prioritization of the treatment options changes”, paragraph [0117], “insight provided by the biomarkers may be incorporated into a virtual trial by modifying the trial to test new treatments or treatment combinations”); and
evaluate and rank a plurality of therapeutic actions based on corrected prediction results to select a therapeutic action suitable for a predetermined therapy target (Shrager: Figures 1, 18-23, paragraph [0016], “generate predictions such as treatment options (optionally ranked according to predicted efficacy) and/or treatment hypotheses”, paragraph [0081], “predict one or more treatment options for a clinical case and/or a cohort comprising at least one clinical case”, paragraph [0085], “An algorithm may utilize a predictive model such as a neural network, a decision tree, a support vector machine, or other applicable model. Using the training data, an algorithm can form a classifier for classifying the case according to relevant features”, paragraph [0088], “calculate the posterior probabilities (e.g., of one or more treatment outcomes”, paragraph [0101], “select one of a ranked list of treatment options and rationales. The selected treatment and/or outcome data can be captured and used to update the knowledge base,”, paragraph [0179], “the system or platform may provide its recommendations and/or calculated rankings to the treating physician”).
The motivation to combine is the same as in claim 1, incorporated herein.
Claim(s) 8 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 20200411199 (hereafter “Shrager”) and U.S. Patent Pub. No. 20210012246 (hereafter “Hazard”).
Regarding (Currently Amended) claim 8, Shrager teaches a [… statistical data …] calculating apparatus (Shrager: Figures 1, 18-23, paragraph [0085], “One or more sets of training data may be generated and provided to a decision engine comprising one or more algorithms for making predictions… statistical methods and methods based on machine learning techniques. Statistical methods include penalized logistic regression, prediction analysis of microarrays (PAM), methods based on shrunken centroids, support vector machine analysis, and regularized linear discriminant analysis”), comprising:
a processor configured to execute instructions to function as [… a …] calculating unit configured to: input, to a learner having been trained to predict and output using learning data including learning attribute information representing an attribute of a past therapy target, learning therapy information representing a content of a therapeutic action with respect to the past therapy target, and a learning therapy result representing a result of the therapeutic action with respect to the past therapy target, from attribute information representing an attribute of a therapy target and therapy information representing a content of a therapeutic action with respect to the therapy target, a result of the therapeutic action with respect to the therapy target, input first input data including first input attribute information representing an attribute of a therapy target and first input therapy information representing a content of a therapeutic action with respect to the therapy target to thereby acquire a first prediction result (Shrager: Figures 1, 18-23, paragraph [0046], “a treatment option can refer to a specific treatment (e.g., active agent and/or dosing regimen) or mode of treatment (e.g., chemotherapy, surgery)… Examples of targeted therapeutic agents include”, paragraphs [0081]-[0085], “generate models that predict one or more treatment options for a clinical case and/or a cohort comprising at least one clinical case. In some instances, machine learning methods are applied to the generation of such models… Such models can be generated by providing a machine learning algorithm with training data in which the expected output is known in advance, e.g., an output in which it is known that a clinical case having a specific data set (e.g., patient information and treatment information) achieved a particular outcome or a probability in which a particular outcome was achieved within a known group of clinical cases having specific data sets… The training data for the machine learning algorithms can be provided as follows. Clinical cases with known outcomes can be grouped into cohorts based on patient information and/or treatment information… the machine learning algorithm is provided with training data that includes the classification (e.g., treatment option, outcome, etc.), thus enabling the algorithm to “learn” by comparing its output with the actual output to modify and improve the model”, paragraph [0113], “treatment history and outcomes of a cohort of clinical cases for patients diagnosed with glioblastoma”, paragraph [0125], “the platforms, media, methods, and applications described herein include a digital processing device, a processor, or use of the same”), and
generate second input data by modifying [… a …] data item of the first input attribute information or the first input therapy information while maintaining remaining data items constant; input, to the learner, second input data, to thereby acquire a second prediction result, (Shrager: paragraph [0044], “The clinical case template may have adaptive parameters that dynamically change… the updated knowledge base can be used to further train and update the one or more algorithms”, paragraph [0084], “combining two or more feature spaces in a classifier instead of using a single feature space… enabling the algorithm to “learn” by comparing its output with the actual output to modify and improve the model”, paragraphs [0090]-[0094], “the errors from the initial classification of the first record are fed back into the network, and are used to modify the network's algorithm in an iterative process… an error may be calculated for the output nodes… Errors are then propagated back through the system”, paragraph [0097], “The adaptive clinical case parameters may dynamically change”, paragraph [0110], “The classifier can continuously update based on new data (e.g., administered treatment(s) and outcome or result of the treatment(s)) and re-evaluate the ongoing clinical case. Thus, the decision engine may dynamically or continuously monitor a clinical case over time and recommend a change to the existing treatment options or a new treatment based upon the updated classifier when the ranking or prioritization of the treatment options changes”, paragraph [0117], “insight provided by the biomarkers may be incorporated into a virtual trial by modifying the trial to test new treatments or treatment combinations”),
Shrager may not explicitly teach (underlined below for clarity): a degree of contribution calculating apparatus, comprising: a processor configured to execute instructions to function as a degree of contribution calculating unit configured to […], generate second input data by modifying only a single data item of the first input attribute information or the first input therapy information while maintaining remaining data items constant; input, to the learner, second input data, to thereby acquire a second prediction result, compute a contribution value for the single modified data item based on a difference between the first prediction result and the second prediction result; and repeat, for each data item, the generating of the second input data, the inputting of the second input data, and the computing of the contribution value to generate a normalized contribution distribution across data items.
Hazard teaches a degree of contribution calculating apparatus, comprising: a processor configured to execute instructions to function as a degree of contribution calculating unit configured to […], generate second input data by modifying only a single data item of the first input attribute information or the first input therapy information while maintaining remaining data items constant; input, to the learner, second input data, to thereby acquire a second prediction result, compute a contribution value for the single modified data item based on a difference between the first prediction result and the second prediction result; and repeat, for each data item, the generating of the second input data, the inputting of the second input data, and the computing of the contribution value to generate a normalized contribution distribution across data items (Hazard: paragraph [0022], “the synthetic data case may be modified (e.g., resampled) and retested, or discarded… the generated synthetic data may be compared against at least a portion of the existing training data, and a determination may be made whether to keep the synthetic data case based on the distance of the synthetic data case to one or more elements of in the existing training data”, paragraphs [0121]-[0125], “feature prediction contribution is determined as a conviction score. Various embodiments of determining feature prediction contribution are given herein. In some embodiments, feature prediction contribution can be used to flag what features are contributing most (or above a threshold amount) to a suggestion. Such information can be useful for either ensuring that certain features are not used for particular decision making and/or ensuring that certain features are used in particular decision making… define a feature information measure, such as familiarity conviction, such that a point's weighted distance contribution affects other points' distance contribution and compared”).
One of ordinary skill in the art before the effective filing date would have found it obvious to include degree of contribution determination as taught by Hazard within the statistical determinations as taught by Shrager with the motivation of “improving the quality of the model… the system will improve the breadth of its observations” (Hazard: paragraph [0162]).
REGARDING CLAIM(S) 12
Claim(s) 12 is/are analogous to Claim(s) 8, thus Claim(s) 12 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 8.
Claim(s) 9-10 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 20200411199 (hereafter “Shrager”) and U.S. Patent Pub. No. 20210118568 (hereafter “Mitsumori”).
Regarding (Currently Amended) claim 9, Shrager teaches a therapeutic action searching apparatus (Shrager: Figures 1, 18-23, paragraph [0007], “an artificial intelligence (AI) planning and search problem that requires the coordination of multiple agents—human and computer—to work together to efficiently search the voluminous and high dimensional space of cancer molecular subtypes and treatment combinations”, paragraph [0014], “The decision to try a specific therapy, alone or in combination, is typically… obtained by querying”, paragraph [0042], “efficiently search the high dimensional space of cancer molecular subtypes crossed with treatment combinations”) comprising:
a processor configured to execute instructions to function as a therapeutic action searching unit configured to: generate a plurality of input data elements, each input data element including input attribute information representing the predetermined therapy target and input therapy information representing a content of a therapeutic action, the plurality of input data elements including identical input attribute information […] (Shrager: paragraphs [0011]-[0013], “a virtual trial may be created by interpreting multiple individual treatments or studies that were previously unrelated… dynamically generate and test novel personalized treatment hypotheses”, paragraph [0044], “identify a similar patient cohort”, paragraph [0046], “a treatment option can refer to a specific treatment (e.g., active agent and/or dosing regimen) or mode of treatment (e.g., chemotherapy, surgery)… Examples of targeted therapeutic agents include”, paragraphs [0081]-[0085], “training data for the machine learning algorithms can be provided as follows. Clinical cases with known outcomes can be grouped into cohorts based on patient information and/or treatment information… the machine learning algorithm is provided with training data that includes the classification (e.g., treatment option, outcome, etc.), thus enabling the algorithm to “learn” by comparing its output with the actual output to modify and improve the model”, paragraph [0110], “the first cohort undergoing a specific treatment is experiencing outcomes that are statistically worse than a second cohort”);
input the plurality of input data elements to the learner to obtain a plurality of prediction results corresponding to the respective input data elements (Shrager: paragraph [0044], “The clinical case template may have adaptive parameters that dynamically change… the updated knowledge base can be used to further train and update the one or more algorithms”, paragraph [0084], “combining two or more feature spaces in a classifier instead of using a single feature space… enabling the algorithm to “learn” by comparing its output with the actual output to modify and improve the model”, paragraphs [0090]-[0094], “the errors from the initial classification of the first record are fed back into the network, and are used to modify the network's algorithm in an iterative process… an error may be calculated for the output nodes… Errors are then propagated back through the system”, paragraph [0097], “The adaptive clinical case parameters may dynamically change”, paragraph [0110], “The classifier can continuously update based on new data (e.g., administered treatment(s) and outcome or result of the treatment(s)) and re-evaluate the ongoing clinical case. Thus, the decision engine may dynamically or continuously monitor a clinical case over time and recommend a change to the existing treatment options or a new treatment based upon the updated classifier when the ranking or prioritization of the treatment options changes”, paragraph [0117], “insight provided by the biomarkers may be incorporated into a virtual trial by modifying the trial to test new treatments or treatment combinations”); and
evaluate and rank the plurality of therapeutic actions based on an aggregate statistical value derived from a distribution of the plurality of prediction results across the plurality of input data elements and select a therapeutic action suitable for the predetermined therapy target (Shrager: Figures 1, 18-23, paragraph [0016], “generate predictions such as treatment options (optionally ranked according to predicted efficacy) and/or treatment hypotheses”, paragraph [0081], “predict one or more treatment options for a clinical case and/or a cohort comprising at least one clinical case”, paragraph [0085], “An algorithm may utilize a predictive model such as a neural network, a decision tree, a support vector machine, or other applicable model. Using the training data, an algorithm can form a classifier for classifying the case according to relevant features”, paragraph [0088], “calculate the posterior probabilities (e.g., of one or more treatment outcomes”, paragraph [0101], “select one of a ranked list of treatment options and rationales. The selected treatment and/or outcome data can be captured and used to update the knowledge base,”, paragraph [0179], “the system or platform may provide its recommendations and/or calculated rankings to the treating physician”),
the prediction results having been acquired by inputting a plurality of input data elements to a learner (Shrager: Figures 1, 18-23, paragraph [0046], “a treatment option can refer to a specific treatment (e.g., active agent and/or dosing regimen) or mode of treatment (e.g., chemotherapy, surgery)… Examples of targeted therapeutic agents include”, paragraphs [0081]-[0085], “generate models that predict one or more treatment options for a clinical case and/or a cohort comprising at least one clinical case. In some instances, machine learning methods are applied to the generation of such models… Such models can be generated by providing a machine learning algorithm with training data in which the expected output is known in advance, e.g., an output in which it is known that a clinical case having a specific data set (e.g., patient information and treatment information) achieved a particular outcome or a probability in which a particular outcome was achieved within a known group of clinical cases having specific data sets… The training data for the machine learning algorithms can be provided as follows. Clinical cases with known outcomes can be grouped into cohorts based on patient information and/or treatment information… the machine learning algorithm is provided with training data that includes the classification (e.g., treatment option, outcome, etc.), thus enabling the algorithm to “learn” by comparing its output with the actual output to modify and improve the model”, paragraph [0113], “treatment history and outcomes of a cohort of clinical cases for patients diagnosed with glioblastoma”),
the learner having been trained to predict and output, using learning data including learning attribute information representing an attribute of a past therapy target, learning therapy information representing a content of a therapeutic action with respect to the past therapy target, and a learning therapy result representing a result of the therapeutic action with respect to the past therapy target, from attribute information representing an attribute of a therapy target and therapy information representing a content of a therapeutic action with respect to the therapy target, a result of the therapeutic action with respect to the therapy target, wherein each of the input data elements includes input attribute information representing an attribute of a therapy target and input therapy information representing a content of a therapeutic action with respect to the therapy target, […] (Shrager: Figures 1, 18-23, paragraphs [0044]-0046], “identify a similar patient cohort… a treatment option can refer to a specific treatment (e.g., active agent and/or dosing regimen) or mode of treatment (e.g., chemotherapy, surgery)”, paragraphs [0081]-[0085], “Clinical cases with known outcomes can be grouped into cohorts based on patient information and/or treatment information . For example, patient information can include patient age, gender, cancer type, cancer stage… Each feature space can comprise types of information about a case, such as biomarker expression or genetic mutations… the machine learning algorithm is provided with training data that includes the classification (e.g., treatment option, outcome, etc.), thus enabling the algorithm to “learn” by comparing its output with the actual output to modify and improve the model”. The Examiner notes patients with various differing attributes are grouped into cohorts based on similar therapeutic action applied for training of a model, which teaches what is required under the broadest reasonable interpretation).
Shrager may not explicitly teach (underlined below for clarity):
generate a plurality of input data elements, each input data element including input attribute information representing the predetermined therapy target and input therapy information representing a content of a therapeutic action, the plurality of input data elements including identical input attribute information and mutually different input therapy information; […] the input data elements including the input therapy information elements that are mutually different.
Mitsumori teaches generate a plurality of input data elements, each input data element including input attribute information representing the predetermined therapy target and input therapy information representing a content of a therapeutic action, the plurality of input data elements including identical input attribute information and mutually different input therapy information; […] the input data elements including the input therapy information elements that are mutually different (Mitsumori: paragraph [0049], “the first cohort undergoing a specific treatment is experiencing outcomes that are statistically worse than a second cohort having the same clinical profile but using a different treatment”).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the invention was made to combine the noted features of Mitsumori within teaching of Shrager since the combination of the two references is merely simple substitution of one known element for another producing a predictable result (KSR rationale B). Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself—that is, in the substitution of the mutually different features taught by Mitsumori for the input features as taught by Shrager. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Regarding (Currently Amended) claim 10, Shrager teaches a therapy target searching apparatus (Shrager: Figures 1, 18-23, paragraph [0007], “an artificial intelligence (AI) planning and search problem that requires the coordination of multiple agents—human and computer—to work together to efficiently search the voluminous and high dimensional space of cancer molecular subtypes and treatment combinations”, paragraph [0014], “The decision to try a specific therapy, alone or in combination, is typically… obtained by querying”, paragraph [0042], “efficiently search the high dimensional space of cancer molecular subtypes crossed with treatment combinations”, paragraph [0046], “targeted therapies… Examples of targeted therapeutic agents”, paragraph [0079], “using pathway models”, paragraph [0120], “identifying targets (e.g., molecular drug targets based on biomarker profile)”) comprising:
a processor configured to execute instructions to function as a therapy target searching unit configured to: generate a plurality of input data elements, each input data element including input attribute information representing a therapy target and input therapy information representing the predetermined therapeutic action, the plurality of input data elements including identical input therapy information […] (Shrager: paragraphs [0011]-[0013], “a virtual trial may be created by interpreting multiple individual treatments or studies that were previously unrelated… dynamically generate and test novel personalized treatment hypotheses”, paragraph [0044], “identify a similar patient cohort”, paragraph [0046], “a treatment option can refer to a specific treatment (e.g., active agent and/or dosing regimen) or mode of treatment (e.g., chemotherapy, surgery)… Examples of targeted therapeutic agents include”, paragraphs [0081]-[0085], “training data for the machine learning algorithms can be provided as follows. Clinical cases with known outcomes can be grouped into cohorts based on patient information and/or treatment information… the machine learning algorithm is provided with training data that includes the classification (e.g., treatment option, outcome, etc.), thus enabling the algorithm to “learn” by comparing its output with the actual output to modify and improve the model”, paragraph [0110], “the first cohort undergoing a specific treatment is experiencing outcomes that are statistically worse than a second cohort”);
input the plurality of input data elements to a learner to obtain a plurality of prediction results corresponding to the respective input data elements (Shrager: paragraph [0044], “The clinical case template may have adaptive parameters that dynamically change… the updated knowledge base can be used to further train and update the one or more algorithms”, paragraph [0084], “combining two or more feature spaces in a classifier instead of using a single feature space… enabling the algorithm to “learn” by comparing its output with the actual output to modify and improve the model”, paragraphs [0090]-[0094], “the errors from the initial classification of the first record are fed back into the network, and are used to modify the network's algorithm in an iterative process… an error may be calculated for the output nodes… Errors are then propagated back through the system”, paragraph [0097], “The adaptive clinical case parameters may dynamically change”, paragraph [0110], “The classifier can continuously update based on new data (e.g., administered treatment(s) and outcome or result of the treatment(s)) and re-evaluate the ongoing clinical case. Thus, the decision engine may dynamically or continuously monitor a clinical case over time and recommend a change to the existing treatment options or a new treatment based upon the updated classifier when the ranking or prioritization of the treatment options changes”, paragraph [0117], “insight provided by the biomarkers may be incorporated into a virtual trial by modifying the trial to test new treatments or treatment combinations”);
evaluate the plurality of prediction results by computing an aggregate statistical value for each therapy target based on corresponding prediction results; and rank the plurality of therapy targets based on the aggregate statistical values and select a therapy target suitable for the predetermined therapeutic action (Shrager: Figures 1, 18-23, paragraph [0016], “generate predictions such as treatment options (optionally ranked according to predicted efficacy) and/or treatment hypotheses”, paragraph [0081], “predict one or more treatment options for a clinical case and/or a cohort comprising at least one clinical case”, paragraph [0085], “An algorithm may utilize a predictive model such as a neural network, a decision tree, a support vector machine, or other applicable model. Using the training data, an algorithm can form a classifier for classifying the case according to relevant features”, paragraph [0088], “calculate the posterior probabilities (e.g., of one or more treatment outcomes”, paragraph [0101], “select one of a ranked list of treatment options and rationales. The selected treatment and/or outcome data can be captured and used to update the knowledge base,”, paragraph [0179], “the system or platform may provide its recommendations and/or calculated rankings to the treating physician”),
the prediction results having been acquired by inputting a plurality of input data elements to a learner (Shrager: Figures 1, 18-23, paragraph [0046], “a treatment option can refer to a specific treatment (e.g., active agent and/or dosing regimen) or mode of treatment (e.g., chemotherapy, surgery)… Examples of targeted therapeutic agents include”, paragraphs [0081]-[0085], “generate models that predict one or more treatment options for a clinical case and/or a cohort comprising at least one clinical case. In some instances, machine learning methods are applied to the generation of such models… Such models can be generated by providing a machine learning algorithm with training data in which the expected output is known in advance, e.g., an output in which it is known that a clinical case having a specific data set (e.g., patient information and treatment information) achieved a particular outcome or a probability in which a particular outcome was achieved within a known group of clinical cases having specific data sets… The training data for the machine learning algorithms can be provided as follows. Clinical cases with known outcomes can be grouped into cohorts based on patient information and/or treatment information… the machine learning algorithm is provided with training data that includes the classification (e.g., treatment option, outcome, etc.), thus enabling the algorithm to “learn” by comparing its output with the actual output to modify and improve the model”, paragraph [0113], “treatment history and outcomes of a cohort of clinical cases for patients diagnosed with glioblastoma”, paragraph [0120], “identifying targets (e.g., molecular drug targets based on biomarker profile)”),
the learner having been trained to predict and output, using learning data including learning attribute information representing an attribute of a past therapy target, learning therapy information representing a content of a therapeutic action with respect to the past therapy target, and a learning therapy result representing a result of the therapeutic action with respect to the past therapy target, from attribute information representing an attribute of the therapy target and therapy information representing a content of a therapeutic action with respect to the therapy target, a result of the therapeutic action with respect to the therapy target, wherein each of the input data elements includes input attribute information representing an attribute of a therapy target and input therapy information representing a content of a therapeutic action with respect to the therapy target, […] (Shrager: Figures 1, 18-23, paragraphs [0044]-0046], “identify a similar patient cohort… a treatment option can refer to a specific treatment (e.g., active agent and/or dosing regimen) or mode of treatment (e.g., chemotherapy, surgery)”, paragraphs [0081]-[0085], “Clinical cases with known outcomes can be grouped into cohorts based on patient information and/or treatment information . For example, patient information can include patient age, gender, cancer type, cancer stage… Each feature space can comprise types of information about a case, such as biomarker expression or genetic mutations… the machine learning algorithm is provided with training data that includes the classification (e.g., treatment option, outcome, etc.), thus enabling the algorithm to “learn” by comparing its output with the actual output to modify and improve the model”, paragraph [0120], “identifying targets (e.g., molecular drug targets based on biomarker profile)”. The Examiner notes patients with various differing attributes are grouped into cohorts based on similar therapeutic action applied for training of a model, which teaches what is required under the broadest reasonable interpretation).
Shrager may not explicitly teach (underlined below for clarity): generate a plurality of input data elements, each input data element including input attribute information representing a therapy target and input therapy information representing the predetermined therapeutic action, the plurality of input data elements including identical input therapy information and mutually different input attribute information; […]; the input data elements including the input attribute information elements that are mutually different.
Mitsumori teaches generate a plurality of input data elements, each input data element including input attribute information representing a therapy target and input therapy information representing the predetermined therapeutic action, the plurality of input data elements including identical input therapy information and mutually different input attribute information; […]; the input data elements including the input attribute information elements that are mutually different (Mitsumori: paragraph [0049], “the first cohort undergoing a specific treatment is experiencing outcomes that are statistically worse than a second cohort having the same clinical profile but using a different treatment”).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the invention was made to combine the noted features of Mitsumori within teaching of Shrager since the combination of the two references is merely simple substitution of one known element for another producing a predictable result (KSR rationale B). Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself—that is, in the substitution of the mutually different features taught by Mitsumori for the input features as taught by Shrager. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
REGARDING CLAIM(S) 13-14
Claim(s) 13-14 is/are analogous to Claim(s) 9-10, thus Claim(s) 13-14 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 9-10.
Response to Arguments
Applicant's arguments filed on 16 June 2026 have been fully considered but they are not persuasive. Applicant's arguments will be addressed below in the order in which they appear in the response filed on 16 June 2026.
Rejections under 35 U.S.C. § 101
Regarding the rejection of claims 1-14, the Examiner has considered the Applicant’s arguments but does not find them persuasive. The Examiner has attempted to address all of the arguments presented by the Applicant; however, any arguments inadvertently not addressed are not persuasive for at least the following reasons:
Applicant argues:
The key premise of the Office Action's § 101 analysis is that the claims merely use a generic learner as a tool to perform statistical analysis. That premise no longer fits the amended claims. The amended claims do not simply ask a learner to produce a prediction and then report… the result. Instead, they reconfigure how the computer system generates inputs, how information is propagated through the learner, how outputs are post-processed, and how the resulting information becomes usable by subsequent processor-executed modules. In other words, the amendments are directed to the architecture and operation of the computer-based statistical simulation system itself, not to an abstract request for advice or a treatment recommendation… That is a concrete improvement in computer functionality: the same computational model is transformed into a system that can generate machine-usable output distributions for a controlled population, rather than merely return isolated predictions for individual inputs. The improvement is not only that more data is analyzed; the improvement is that the system's internal data architecture and information flow are changed in a way that makes new forms of computation possible… That is an improvement in the operation of the computer system itself, because it changes the computer from a passive prediction tool into an iterative distribution-optimization engine… That is a concrete improvement in how the system processes and structures information… That integrated workflow is a concrete improvement in computer functionality because it gives the computer system a new composite capability that generic machine-learning tools do not provide by themselves.
The Examiner respectfully disagrees.
It is respectfully submitted, that there are no claimed additional elements that provide a practical application to a technical problem recited in Applicant’s specification and/or an improvement in the functionality of the computer. In particular, only the additional elements are capable of providing a practical application, the labels of data and the mathematical concepts used to organize data are not additional elements capable of providing a practical application, and instead the claimed additional elements amount to generic use of an off-the shelf machine learning model, that is repeatedly trained, which is not sufficient to show an improvement in the performance of the computer, instead the claims at best may improve upon the organization of data for providing a ranking to a human user (i.e., an improved abstract idea), nevertheless an improved abstract idea is still an abstract idea. Additionally, no portions of Applicant’s specification are argued for recitations of a technical problem, and looking to at least paragraph [0004] of Applicant’s specification the language is directed toward manual human activity problems of time, labor and cost, which are not technical problems rooted in computer hardware technology. As none of the claimed additional elements recite a technical solution to a technical problem recited in Applicant’s specification the argument is not persuasive.
Rejections under 35 U.S.C. § 103
Regarding the rejection of claims 1-14, the Examiner has considered the Applicant’s arguments but does not find them persuasive. The Examiner has attempted to address all of the arguments presented by the Applicant; however, any arguments inadvertently not addressed are not persuasive for at least the following reasons:
Applicant argues:
Claims 1 and 11 now require generation of input attribute information by sampling data-item values in accordance with a predetermined distribution, construction of a plurality of input data elements having mutually different input attribute information and identical input therapy information, execution of the trained learner across that constructed plurality, and generation of statistical data as a distribution of prediction results across the resulting output data elements. Shrager and Mitsumori rely on observational/cohort data and do not teach… Claims 8 and 12 now require a specific perturbation-based contribution workflow in which only a single data item is modified while the remaining data items are maintained constant, the resulting modified input is re-input to the learner, a contribution value is computed from the difference between first and second prediction results, and the sequence is repeated for each data item to generate a normalized contribution distribution. Hazard's more general discussion of feature contribution does not teach… Claims 9, 10, 13, and 14 likewise require generation of structured pluralities of input data elements with one dimension fixed and another varied, followed by learner execution across those pluralities and ranking based on aggregate statistical values computed from the resulting prediction results. The cited references do not teach
The Examiner respectfully disagrees.
It is respectfully submitted, that it is the combination of Hazard and Mitsumori within teachings of Shrager that teach the argued limitations, in particular the argued sampling is taught by Hazard see above but at least paragraph [0032], in combination with the distributions taught by Shrager see above but at least paragraphs [0085]-[0088]. The modification of a single data item is taught by the combination of Hazard and Shrager both of which teach modification of specific data items to update training see above but at least Shrager: paragraphs [0044], [0110]; Hazard: paragraph [0022]. And finally, use of mutually different features is taught by Mitsumori since the combination of the two references is merely simple substitution of one known element for another producing a predictable result (KSR rationale B). Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious. Additionally, one of ordinary skill in the art would find it prima facie obvious to include the teachings of Hazard within the teachings of Shrager and Mitsumori with the motivation of “improving the quality of the model… the system will improve the breadth of its observations” (Hazard: paragraph [0162]).
In addition, the Examiner respectfully notes that the cited reference was never applied as a reference under 35 U.S.C. 102 against the pending claims. As such, the Examiner respectfully submits that the issue at hand is not whether the applied prior art specifically teaches the claimed features, per se, but rather, whether or not the prior art, when taken in combination with the knowledge of average skill in the art, would put the artisan in possession of these features. Regarding this issue, it is well established that references are evaluated by what they suggest to one versed in the art, rather than by their specific disclosures, In re Bozek, 163 USPQ 545 (CCPA 1969). The issue of obviousness is not determined by what the references expressly state but by what they would reasonably suggest to one of ordinary skill in the art, as supported by decisions in In re DeLisle 406 Fed 1326, 160 USPQ 806; In re Kell, Terry and Davies 208 USPQ 871; and In re Fine, 837 F.2d 1071, 1074, 5 USPQ 2d 1596, 1598 (Fed. Cir. 1988) (citing In re Lalu, 747 F.2d 703, 705, 223 USPQ 1257, 1258 (Fed. Cir. 1988)). Further, it was determined in In re Lamberti et al, 192 USPQ 278 (CCPA) that:
(i) obviousness does not require absolute predictability;
(ii) non-preferred embodiments of prior art must also be considered; and
(iii) the question is not express teaching of references, but what they would suggest.
According to In re Jacoby, 135 USPQ 317 (CCPA 1962), the skilled artisan is presumed to know something more about the art than only what is disclosed in the applied references. In In re Bode, 193 USPQ 12 (CCPA 1977), every reference relies to some extent on knowledge of persons skilled in the art to complement that which is disclosed therein.
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.
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/A.E.L./Examiner, Art Unit 3684
/RAJESH KHATTAR/Primary Examiner, Art Unit 3684
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