DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-22 are currently pending and under examination herein.
Claims 1-22 are rejected.
Priority
The application claims, filed 07/26/2023, is a National Stage entry of PCT/GB2022/050130, International Filing Date: 01/26/2021. PCT related documentation reviewed.
In this action, all claims 1-22 are examined for an effective filing date of 01/26/2021. In future actions, the effective filing date of one or more claims may change, due to amendments to the claims, or further analysis of the disclosure(s) of the priority application(s).
Information Disclosure Statement
The Information Disclosure Statement, filed 10/16/2023, has been considered. Signed copies of the IDS are included with this Office Action.
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Drawings
The Drawings submitted, filed 07/26/2023, are accepted. The drawings as filed are suitable to the Examiner.
Claim Objection
Claim 14 is objected to under 37 CFR 1.75 as being a substantial duplicate of claim 20. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. (see MPEP § 608.01(m)). Applicant is advised to make claim 14 properly independent, by including all the limitations from claim 1, rather than dependent on claim 1 (see MPEP 2173.05(p)).
Instant claims 4 and 10 are objected to for grammatical errors. Claim 4, which recites “wherein said associated metadata comprising said predicted metadata”, should recite “comprises” rather than comprising. Claim 10 recites “further comprising” which should be “further comprises”.
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 limitations are:
an input module configured to receive one or more sets of entities and associated metadata from a data source;
a processing module configured to predict, based said one or more sets of entities in relation to the data source, the plurality of predictions, wherein the plurality of predictions are ranked in a subset set of predictions;
a computation module configured to compute the set of metrics based on the plurality of prediction and the associated metadata, wherein the computation is performed using one or more pre-trained predictive models; and
an output module configured to present the set of metrics for evaluation in claims 14 and 20
wherein an external/second application module configured to receive the outputted set of metrics in claims 17 and 18, and
the system of claim 20 is configured to: select…generate…selecting… extract…generate…output… in claim 22.
Said modules/system are the generic placeholders, and each respective function is the specialized function.
to receive one or more sets of entities and associated metadata from a data source;
to predict, based said one or more sets of entities in relation to the data source, the plurality of predictions, wherein the plurality of predictions are ranked in a subset set of predictions;
to compute the set of metrics based on the plurality of prediction and the associated metadata, wherein the computation is performed using one or more pre-trained predictive models; and
to present the set of metrics for evaluation in claims 14 and 20
to receive the outputted set of metrics in claims 17 and 18,
to: select…generate…selecting… extract…generate…output… in claim 22
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.
However, the instant specification does not disclose a specific structural embodiment for the system claim to instantiate the one or more modules that may correspond to in FIG 6 [00118-00122: Various functions described herein can be implemented in hardware, software, or any combination thereof. If implemented in software, the functions can be stored on or transmitted over as one or more instructions or code on a computer- readable medium. Computer-readable media may include, for example, computer- readable storage media. Computer-readable storage media may include volatile or non- volatile, removable or non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data]. Therefore, the claim limitation processing circuitry has sufficient structure rooted in the specification written description and further, the claim limitation is indefinite as discussed in the below 112 rejection.
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.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 14-22 are rejected under 35 USC 112 2nd or 112(b) as failing to particularly point out and distinctly claim the invention. Claims 14-22 fail to particularly point out and distinctly claim the subject matter which applicant regards as his invention.
Claim limitations modules/system in claims 14-22 which are indefinite are::
an input module configured to receive one or more sets of entities and associated metadata from a data source;
a processing module configured to predict, based said one or more sets of entities in relation to the data source, the plurality of predictions, wherein the plurality of predictions are ranked in a subset set of predictions;
a computation module configured to compute the set of metrics based on the plurality of prediction and the associated metadata, wherein the computation is performed using one or more pre-trained predictive models; and
an output module configured to present the set of metrics for evaluation in claims 14 and 20
wherein an external/second application module configured to receive the outputted set of metrics in claims 17 and 18, and
the system of claim 20 is configured to: select…generate…selecting… extract…generate…output… in claim 22.
Said claim limitations invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The claims fail to particularly set forth and distinctly claim the actual structures required to receive… predict, compute… present… in claim 20, or to: select…generate…selecting… extract…generate…output… in claim 22, and receive in claims 17 and 18.
The claims fail to set forth how to apply any technical or computer elements required to receive one or more sets of entities and associated metadata from a data source nor to present the set of metrics for evaluation. The claims fail to particularly set forth and distinctly claim the actual algorithms/machine learning models, step-by-step processes or structures required to predict, based said one or more sets of entities in relation nor to compute the set of metrics based on the plurality of prediction and the associated metadata. The claim fails to identify what entity data is relevant, necessary and sufficient for the stated goals. The claim fails to particularly set forth how to incorporate any biological information into a machine learning or correlation calculation, what criteria is needed to determine a “top” predictions/correlation/process, a relative limitation. A reading of the specification provides a variety of possibilities, however no specific model or correlation definition or structure for data handling or computation which is necessary and sufficient is provided. While the claims are read in light of the specification, limitations from the specification cannot be read into the claims.
“For a computer-implemented 35 U.S.C. 112(f) claim limitation, the specification must disclose an algorithm for performing the claimed specific computer function, or else the claim is indefinite under 35 U.S.C. 112(b) (b). See Net MoneyIN, Inc. v. Verisign. Inc., 545 F.3d 1359, 1367 (Fed. Cir. 2008).” (see MPEP 2181.II.B:)
“To claim a means for performing a specific computer-implemented function and then to disclose only a general purpose computer as the structure designed to perform that function amounts to pure functional claiming. Aristocrat, 521 F.3d 1328 at 1333, 86 USPQ2d at 1239.”
“Mere reference to a general purpose computer with appropriate programming without providing an explanation of the appropriate programming, or simply reciting "software" without providing detail about the means to accomplish a specific software function, would not be an adequate disclosure of the corresponding structure to satisfy the requirements of 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Aristocrat, 521 F.3d at 1334, 86 USPQ2d at 1239...”
Therefore, claims 14-22 are indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
B. Claims 14-22 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. This is a WRITTEN DESCRIPTION rejection.
As set forth above, the claims invoke 112 6th paragraph for the cited steps/structure required to receive… predict, compute… present… in claim 20, or to: select…generate…selecting… extract…generate…output… in claim 22. The specification fails to specifically disclose the necessary and sufficient actual algorithms/machine learning models, step-by-step processes or structures required to compute or generate each specialized function, and fails to specifically link those structures to the specialized functions of the claims. Merely stating the desired goal of the step is not a written description of the actual steps required to achieve the goal. While the specification recites a variety of algorithms/machine learning models,, it is the specific linkage of a particular set of steps/structure which are necessary and sufficient to achieve each specialized function that are lacking.
“For a computer-implemented 35 U.S.C. 112(f) claim limitation, the specification must disclose an algorithm for performing the claimed specific computer function, or else the claim is indefinite under 35 U.S.C. 112(b) (b). See Net MoneyIN, Inc. v. Verisign. Inc., 545 F.3d 1359, 1367 (Fed. Cir. 2008).” (see MPEP 2181.II.B:)
“To claim a means for performing a specific computer-implemented function and then to disclose only a general purpose computer as the structure designed to perform that function amounts to pure functional claiming. Aristocrat, 521 F.3d 1328 at 1333, 86 USPQ2d at 1239.”
“Mere reference to a general purpose computer with appropriate programming without providing an explanation of the appropriate programming, or simply reciting "software" without providing detail about the means to accomplish a specific software function, would not be an adequate disclosure of the corresponding structure to satisfy the requirements of 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Aristocrat, 521 F.3d at 1334, 86 USPQ2d at 1239...”
“When a claim containing a computer-implemented 35 U.S.C. 112(f) claim limitation is found to be indefinite under 35 U.S.C. 112(b) for failure to disclose sufficient corresponding structure (e.g., the computer and the algorithm) in the specification that performs the entire claimed function, it will also lack written description under 35 U.S.C. 112(a). See MPEP § 2163.03, subsection VI.”
C. Claims 1-22 are indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. The dependent claims are also rejected because they depend on and/or do not remedy the deficiencies inherited from their parent claims.
The metes and bounds of independent claims 1, 14, and 20 are entirely unclear.
Representative independent claim 1 recites “generating a plurality of predictions” without details on the type of predictions and where are the predictions obtained from. The predictions are not set forth such that one of skill would be apprised as to what predictions and data sources are relevant and how they relate to produce metadata, predicted vs entity. Representative independent claim 1 recites “aggregated from said one or more sets of entities” without details on the nature of the entities, or the active steps for aggregat[ing] a variety of undefined entities to produce said predictions. Representative independent claim 1 recites “using one or more pre-trained predictive models”. The pre-training and machine learning models are not defined by the claim and how the models act upon/aggregate the entities data/metadata to achieve the results as a set of metrics. The specification does not provide the training parameters/weightings/datasets, so one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Further, what metrics specifically are generated for evaluation (intended use language) and what is being evaluated. There are no steps or limitations which indicate the nature of the input/output, nor the predictive models/machine learning model used to produce predictions and how they result in metrics for evaluation, so that one of ordinary skill in the art would be reasonably apprised of the scope of the invention. The minimally necessary and sufficient elements required are not claimed.
Furthermore, the Independent claim 14 recites “an interface device for displaying a set of metrics, the interface device comprising: a memory; at least one processor configured to access the memory and perform operations according to claim 1;” and is indefinite. A single claim which claims both an apparatus and the method steps of using the apparatus is indefinite under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. See In re Katz Interactive Call Processing Patent Litigation, 639 F.3d 1303, 1318, 97 USPQ2d 1737, 1748-49 (Fed. Cir. 2011). In Katz, a claim directed to "[a] system with an interface means for providing automated voice messages…to certain of said individual callers, wherein said certain of said individual callers digitally enter data" was determined to be indefinite because the italicized claim limitation is not directed to the system, but rather to actions of the individual callers, which creates confusion as to when direct infringement occurs. Katz, 639 F.3d at 1318, 97 USPQ2d at 1749 (citing IPXL Holdings v. Amazon.com, Inc., 430 F.3d 1377, 1384, 77 USPQ2d 1140, 1145 (Fed. Cir. 2005), in which a system claim that recited "an input means" and required a user to use the input means was found to be indefinite because it was unclear "whether infringement … occurs when one creates a system that allows the user [to use the input means], or whether infringement occurs when the user actually uses the input means."); Ex parte Lyell, 17 USPQ2d 1548 (Bd. Pat. App. & Inter. 1990) (claim directed to an automatic transmission workstand and the method of using it held ambiguous and properly rejected under 35 U.S.C. 112, second paragraph). This applies equally to dependent claim 17. Dependent claim 17 recites “the interface device of claim 16, in response to receiving said one or more inputs and following the output of the set of metrics” and is indefinite for claiming both an apparatus and the method steps of using the apparatus.
Dependent claim 2 recites the relative claim limitation “top” to describe top predictions/correlation/process/pathways, is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The term fails to particularly set forth criteria (percentile, cutoff, threshold) that are needed to determine a “top” data element/feature. It is entirely unclear how to determine what relation the entities indicate or how to rank the predictions given the information provided. This applies equally to claims 3, 9, 14, 15, and 21. Further still, there is insufficient information about the metadata: what is the metadata and how is it generated from the predictions.
The metes and bounds of claim 5 limitation “a biomedical context” are indefinite for whether this context relates only to the models training datasets and whether the subsequent entities and predictions also biomedical. If they are not, can the models work, for example on chemical compound entities.
The metes and bounds of claim 7 limitation “enrichment” are indefinite, with respect to biomedical data and how the data gets enriched to “using data further extracted from one or more sources.”
The metes and bounds of claims 9, 15, and 21 limitation “overlap/correlation” of the predictions are indefinite for what standard or criteria one with skill in the art determines a relevant degree of overlap/correlate. Further, claims 9, 15, and 21 set forth the target metrics, but claim 1 does not provide the entities, models, or biomedical context for producing the metrics and any potential summary of relationships or reduction to practice statement, which are themselves indefinite limitations. Further, sets forth the target metrics, but claim 1 does not provide the entities, models, or biomedical context for producing the metrics and any potential summary of relationships or reduction to practice statement, which are themselves indefinite limitations. Claim 1 does not recite ligandable drug target families, gene data, pathway enrichment, etc. This applies equally to claims 14 and 21.
Claims 9, 15, and 21 recite the term “ligandable” which is indefinite. The specification does not provide a definition, so one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
. Claim 11 recites an “automated system” which is indefinite without active steps for said system (is this the same as the system in claim 14 or 20) to act on the data to achieve the desired result.
The metes and bounds of claim 13 limitation “evaluating the entities” are indefinite, as to how one of ordinary skill in the art would evaluate the data and how it relates to “the outputted metrics” and for what purpose.
The metes and bounds of claims 9, 14, and 15 exemplary options limitation “model literature option” are indefinite, as to whether claim 1 provides the information necessary to get results that might be utilized/displayed using said options.
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 1-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more.
The instant rejection reflects the framework as outlined in the MPEP at 2106.04:
Framework with which to Evaluate Subject Matter Eligibility:
(1) Are the claims directed to a process, machine, manufacture, or composition of matter;
(2A) Prong One: Do the claims recite a judicially recognized exception, i.e. a law of nature, a natural phenomenon, or an abstract idea;
Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and
(2B) If the claims do not integrate the judicial exception, do the claims provide an inventive concept.
Framework Analysis as Pertains to the Instant Claims:
With respect to step (1): yes, claims 1-19 are directed to a method and a device, to compare biomedical relevance of different input for a predictive machine-learning model, therefore the answer is "yes".
Claims 20-22 are rejected under 35 U.S.C. 101 because the claimed invention is not directed to statutory subject matter. A system claim needs to recite the physical structures that make up the system, e.g., a processor, a memory, an input device, an output device, etc. for a computer-based system. In the instant application, claim 20 include various modules which are software (i.e., functional data per se). Thus, since no physical structures are included in the system claims which would enable a practitioner to make and use the system, the system claim is considered to be non-statutory. The instant claims read on transitory signals comprising software (MPEP 2106):
For example, in Mentor Graphics v. EVE-USA, Inc., 851 F.3d 1275, 112 USPQ2d 1120 (Fed. Cir. 2017), claim interpretation was crucial to the court’s determination that claims to a "machine-readable medium" were not to a statutory category. In Mentor Graphics, the court interpreted the claims in light of the specification, which expressly defined the medium as encompassing "any data storage device" including random-access memory and carrier waves. Although random-access memory and magnetic tape are statutory media, carrier waves are not because they are signals similar to the transitory, propagating signals held to be non-statutory in Nuijten. 851 F.3d at 1294, 112 USPQ2d at 1133 (citing In re Nuijten, 500 F.3d 1346, 84 USPQ2d 1495 (Fed. Cir. 2007).
Accordingly, because the BRI of the claims covered both subject matter that falls within a statutory category (the random-access memory), as well as subject matter that does not (the carrier waves), the claims as a whole were not to a statutory category. For compact prosecution, instant claims will be examined as if the minimally sufficient system components and its non-transitory equivalents (FIG 6, [00126: "system" are intended to encompass computer-readable data storage that is configured with computer-executable instructions that cause certain functionality to be performed when executed by a processor]) are recited.
With respect to step (2A)(1), the claims recite abstract ideas. To determine if the claims recite any concepts that equate to an abstract idea, law of nature, or natural phenomenon, MPEP at 2106.03 teaches abstract ideas include mathematical concepts (mathematical formulas or equations, mathematical relationships, and mathematical calculations), certain methods of organizing human activity, and mental processes (including procedures for collecting, observing, evaluating, and organizing information (see MPEP 2106.04(a)(2)). Laws of nature or natural phenomena include naturally occurring principles/ relations and nature-based products that are naturally occurring or that do not have markedly different characteristics compared to what occurs in nature (MPEP2106(b)). In the instant application, the claims recite the following limitations that equate to an abstract idea with mental steps.
Claims 1, 20, and 22 are independent, and all recite nearly identical limitations. Primarily claim 1 is referred to here for clarity.
With respect to the instant claims, under the step (2A)(1) evaluation, the claims are found to direct to abstract ideas that fall into the grouping of abstract ideas: in particular mental steps for identify… determine…simulate… bioprocess/bioreactor in order to determine overall risk and in mathematical concepts for calculate viable cell density …
The claims directing to judicial exceptions are as follows:
Mental processes:
Claims 1, 14, 20 and 22: selecting one or more sets of entities from a data source; selecting a subset of predictions from the plurality of predictions based on said one or more sets of entities in relation to the data source; extracting metadata from the data source associated with the subset of predictions…generating the set of metrics based on the metadata extracted and the subset of predictions…
Claim 2: top predictions ranked in relation to said one or more pre-trained predictive models.
Claim 3: wherein the set of metrics are generated based on said top predictions and associated metadata.
Claim 5: pre-trained predictive models are adapted for a biomedical context.
Claim 6: pre-trained predictive models are trained using biomedical data
Claim 7: wherein said biomedical data is enriched or has undergone a process of enrichment using data further extracted from one or more sources.
Claim 8: selecting said one or more set of entities from the data source that comprises a knowledge graph; and extracting metadata from the knowledge graph, wherein the knowledge graph is configured to encode data related to a biomedical domain or a field corresponding to the biomedical domain.
Claims 11 and 20: outputted set of metrics are evaluated with at least one automated system configured to process or select one or more predictions based on at least one predetermined criterion associated with the outputted set of metrics.
Claim 13: evaluating the entities of the data source based on the outputted set of metrics.
Claim 19: wherein the report publisher is configured to collate and compile the received set of metrics and the associated metrics reference list…
Mathematical concepts:
Claims 1, 12, , wherein the computation is performed using one or more pre-trained predictive models [machine learning models]
Hence, the claims explicitly recite elements that, individually and in combination, constitute abstract ideas.
With respect to step 2A(2): The claims must therefore be examined further to determine whether they integrate that abstract idea into a practical application (MPEP 2106.04(d). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d).III).
With respect to the instant recitations, the claims recite the following additional elements considered for practical application:
Claims 1, 14, 20 and 22: wherein the metadata comprises entity metadata and predicted metadata… outputting the set of metrics for evaluation
Claim 4: said associated metadata comprising said predicted metadata.
Claims 1, 11-12, 14, and 20-22: computer implemented, interface device, processor, memory, system
Claim 9: wherein the set of metrics are based on one or a combination of: at least one overlap between the plurality of predictions, a set top correlations of objects in a database, a set of top processes, at least one correlation of the predictions with metadata associated with database objects, a proportion of the predictions derived from ligandable drug target families, a percentage of processes or pathways found in an enrichment of gene data in a training model and in enriched lists of the plurality of predictions, at least one overlap between pathway enrichment or process enrichment data between the entities, a summary of relationships associated with the predictions to one or more objects in a database, at least one reduction to practice statement of association between the plurality of predictions and a disease context, and at least one connectivity associated with protein-protein interactions.
Claim 10: displaying the set of metrics on an interface.
Claims 1 and 12: wherein said at least one automated system is associated with the predictive machine learning model.
Claims 14-15 and 21: wherein said at least one display option are displayed in relation to the set of metrics…one overlap between a plurality of predictions; a set of top correlations of objects in a database;
a set of top processes; at least one correlation of the predictions with metadata associated with database objects; a proportion of the predictions derived from ligandable drug target families; a percentage of processes or pathways found in an enrichment of gene data in a training model and in enriched lists of the plurality of predictions; at least one overlap between pathway enrichment or process enrichment data between the entities, a summary of relationships associated with the predictions to one or more objects in a database; at least one reduction to practice statement of association between the plurality of predictions and a disease context; and at least one connectivity associated with protein-protein interactions.
Claim 16: wherein the interface device is configured to receive one or more inputs of entities associated with a knowledge graph.
Claim 17: wherein an external application module configured to receive the outputted set of metrics and an associated metrics reference list from said at least one processor of the interface device.
Claim 18: wherein a second application module is configured to receive the outputted set of metrics and the associated metrics reference list for a report publisher.
Claim 19: report publisher …to generate a representative report for visualising the set of metrics as display options on the interface device.
Claims 1, 14, and 20 do not utilize the output the set of metrics for evaluation.in a real world or practical application, only to predict/optimize target data (adapted for a biomedical context). They also do not recite any conventional steps of producing biomedical data only receiving from a data source.
The instant claims 1-22 recite additional elements that are not an abstract idea but are data gathering steps. Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the parameter data needed to carry out the abstract idea. Data gathering does not impose any meaningful limitation on the abstract idea, or how the abstract idea is performed. Data gathering steps are not sufficient to integrate an abstract idea into a practical application. (MPEP 2106.05(g).
Claims 1, 11-12, 14, and 20-22 also recite the additional non-abstract elements: : computer, interface device, processor, memory, system.... The claims do not describe any specific computational steps by which the system or interface device and related parts perform or carry out the abstract idea, nor do they provide any details of how specific structures of the computer such as the system are used to implement these functions. The claims require nothing more than a minimally described, generic interface device or system (FIG 6, [00116: the described embodiments and aspects of the invention the system may be implemented as any form of a computing and/or electronic device. Such a device may comprise one or more processors which may be microprocessors…[00118-122 Various functions described herein can be implemented in hardware, software, or any combination thereof. If implemented in software, the functions can be stored on or transmitted over as one or more instructions or code on a computer- readable medium. Computer-readable media may include, for example, computer- readable storage media. Computer-readable storage media may include volatile or non- volatile, removable or non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data...). Hence, these are mere instructions to apply the abstract idea using a computer, and therefore the claim does not recite integrate that abstract idea into a practical application. (see MPEP 2106.05(f)).
Remaining claims have been analyzed. Dependent claims are directed to further abstract limitations. Further abstract limitations cannot provide a practical application of the judicial exception as they are a part of that exception. Dependent claims are also directed to additional steps of data gathering. Steps of data gathering do not provide a practical application for the judicial exception. Claim 14-22 are directed to additional computer limitations. These further limitations are still generically stated and require no more than a standard computer with a interface screen to perform them.
Claims 2-22 recite additional elements that are not an abstract idea but are data gathering steps. Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect and organize the entity data needed to carry out the abstract idea. Data gathering does not impose any meaningful limitation on the abstract idea, or how the abstract idea is performed. Data gathering steps are not sufficient to integrate an abstract idea into a practical application. (MPEP 2106.05(g).
None of these dependent claims recite additional elements which would integrate a judicial exception into a practical application.
Finally, the (2B) analysis. Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims lack a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they provide significantly more than the judicial exception. (MPEP 2106.05.A i-vi).
With respect to the instant claims, the additional elements of data gathering, instructions, and field of use limitations described above do not rise to the level of significantly more than the judicial exception. As directed in the Berkheimer memorandum of 19 April 2018 and set forth in the MPEP, determinations of whether or not additional elements (or a combination of additional elements) may provide significantly more and/or an inventive concept rest in whether or not the additional elements (or combination of elements) represent well-understood, routine, conventional activity. Said assessment is made by a factual determination stemming from a conclusion that an element (or combination of elements) is widely prevalent or in common use in the relevant industry, which is determined by either a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to the instant recitations, the claims recite the following additional elements considered for inventive concept:
Claims 1, 14, 20 and 22: wherein the metadata comprises entity metadata and predicted metadata… outputting the set of metrics for evaluation
Claim 4: said associated metadata comprising said predicted metadata.
Claims 1, 11-12, 14, and 20-22: computer implemented, interface device, processor, memory, system
Claim 9: wherein the set of metrics are based on one or a combination of: at least one overlap between the plurality of predictions, a set top correlations of objects in a database, a set of top processes, at least one correlation of the predictions with metadata associated with database objects, a proportion of the predictions derived from ligandable drug target families, a percentage of processes or pathways found in an enrichment of gene data in a training model and in enriched lists of the plurality of predictions, at least one overlap between pathway enrichment or process enrichment data between the entities, a summary of relationships associated with the predictions to one or more objects in a database, at least one reduction to practice statement of association between the plurality of predictions and a disease context, and at least one connectivity associated with protein-protein interactions.
Claim 10: displaying the set of metrics on an interface.
Claims 1 and 12: wherein said at least one automated system is associated with the predictive machine learning model.
Claims 14-15 and 21: wherein said at least one display option are displayed in relation to the set of metrics…one overlap between a plurality of predictions; a set of top correlations of objects in a database;
a set of top processes; at least one correlation of the predictions with metadata associated with database objects; a proportion of the predictions derived from ligandable drug target families; a percentage of processes or pathways found in an enrichment of gene data in a training model and in enriched lists of the plurality of predictions; at least one overlap between pathway enrichment or process enrichment data between the entities, a summary of relationships associated with the predictions to one or more objects in a database; at least one reduction to practice statement of association between the plurality of predictions and a disease context; and at least one connectivity associated with protein-protein interactions.
Claim 16: wherein the interface device is configured to receive one or more inputs of entities associated with a knowledge graph.
Claim 17: wherein an external application module configured to receive the outputted set of metrics and an associated metrics reference list from said at least one processor of the interface device.
Claim 18: wherein a second application module is configured to receive the outputted set of metrics and the associated metrics reference list for a report publisher.
Claim 19: report publisher …to generate a representative report for visualising the set of metrics as display options on the interface device.
Said steps that are “in addition” to the recited judicial exception in the instant claims represent those of mere data handling instructions or field of use limitations (inputting/receiving/selecting/generating/ranking/extracting/displaying/outputting/reporting/visualising) to implement in the recited judicial exception and do not impart meaning to said recited judicial exception, such that is applied in a practical manner. Further with respect to the additional elements in the instant claims, these steps direct to mere data gathering and handling to carry out the abstract idea without imposing any meaningful limitation on the abstract idea. Thereby these steps are insignificant extra-solutions activity steps and are insufficient to integrate an abstract idea into a practical application. (MPEP 2106.05(g).
With respect to claim 1: The additional element of data gathering does not rise to the level of significantly more than the judicial exception. Steps of target entity metric sets and metadata sourcing, sorting, aggregating, extracting, or outputting are merely steps of data handling. Claims 1-22 recite additional elements that are not an abstract idea but are data gathering steps. Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data needed to carry out the abstract idea. Data gathering does not impose any meaningful limitation on the abstract idea, or how the abstract idea is performed. Data gathering steps are not sufficient to integrate an abstract idea into a practical application. (MPEP 2106.05(g). Claims 1, 14, and 20 do not utilize the output the set of metrics for evaluation.in an inventive way, only to predict/optimize target data (adapted for a biomedical context). They also do not recite any conventional steps of producing biomedical data only receiving from a data source.
Claims 1, 11-12, 14, and 20-22 also recite the additional non-abstract elements: : computer, interface device, processor, memory, system.... The claims do not describe any specific computational steps by which the system or interface device and related parts perform or carry out the abstract idea, nor do they provide any details of how specific structures of the computer such as the system are used to implement these functions. The claims require nothing more than a minimally described, generic interface device or system (FIG 6, [00116: the described embodiments and aspects of the invention the system may be implemented as any form of a computing and/or electronic device. Such a device may comprise one or more processors which may be microprocessors…[00118-122 Various functions described herein can be implemented in hardware, software, or any combination thereof. If implemented in software, the functions can be stored on or transmitted over as one or more instructions or code on a computer- readable medium. Computer-readable media may include, for example, computer- readable storage media. Computer-readable storage media may include volatile or non- volatile, removable or non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data...). Hence, these are mere instructions to apply the abstract idea using a computer, and therefore the claim does not recite integrate that abstract idea into a practical application. (see MPEP 2106.05(f)).
Remaining claims have been analyzed. Dependent claims are directed to further abstract limitations. Further abstract limitations cannot provide a practical application of the judicial exception as they are a part of that exception. Dependent claims are also directed to additional steps of data gathering. Steps of data gathering do not provide a practical application for the judicial exception. Claim 14-22 are directed to additional computer limitations. These further limitations are still generically stated and require no more than a standard computer with a interface screen to perform them. With respect to independent claims with the computer related elements, a general purpose computer do not rise to the level of significantly more than the judicial exception. The specification does not disclose any system architecture or hardware components. The additional elements are set forth at such a high level of generality that they can be met by a general purpose computer. Therefore, the computer components constitute no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than an abstract idea (see MPEP 2106.05(b)I-III).
For these reasons, the claims, when the limitations are considered individually and as a whole, are rejected under 35 USC § 101 as being directed to non-statutory subject matter (Step 2B: No). As such, claims 1-22 are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
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.
Claims 1-22 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable over
Fokoue-Nkoutche et al. US Publication 20190303535 A1,
in view of Tripathi et al. US Publication 20200090818 A1.
Note: citations from the instant application are italicized in the following section.
The independent claims are drawn to a method, a device, and a system to compare biomedical relevance of different input for a predictive machine-learning model, in order to assess and compare the suitability of the initial entity data elements or which entity data elements produce the most useful results given the predictive machine-learning model.
With respect to claims 1, 14, 20, and 22, the prior art to Fokoue-Nkoutche et al. provides the overall interface device, method, and system FIG 1 and 8 for receiving a representation of an entity pair (paragraph 0036 “Referring now to FIGS. 2 and 3, specific training module 103 receives (step 302) biomedical entity pair 208 from an input source, such as from a user, a database (data source), a knowledge graph (109 FIG 1; [0031]), a remote server, or another source. In the example depicted in FIG. 2, the biomedical entities in the biomedical entity pair 208 are one or more gene sequences, and one or more disease sequences, respectively.”); and using a classifier (predictive machine learning model) to determine a predicted ranking for the entity pair using the one or more first scores and the one or more second scores (paragraphs 0025-0026 and 0028 “Some embodiments of the invention will generally be described in the context of the following three processing phases: a specific neural network training phase (“specific training phase”); a general neural network training phase (“general training phase”); and a neural network inference phase (“inference phase”)” and “The specific training phase generally refers to a set of functions that receive, as their inputs, a biomedical entity pair; process them using various machine learning techniques including those that use a neural network; and generate an output (that represents a likelihood that the two biomedical entities in the biomedical entity pair are associated with one another (the output may also be considered a measure of their association) (predictions aggregated from sets of entities using one or more pre-trained predictive models… outputting the set of metrics for evaluation)… This process is referred to as “specific” because its output is based on a given biomedical entity pair, and because iterative execution of this specific process forms part of the general training phase (along with other processes)”); and providing the predicted ranking to a user as an indication of the strength of evidence for an association between the entities of the entity pair [ 0003, 0028, 0032] “The inference phase generally refers to a set of functions that evaluate a given biomedical entity pair's level of association (whether as a scale or as a binary value) by using the given biomedical entity pair as inputs to the trained neural network (pre-trained predictive models), and by receiving an output of the trained neural network (outputting the set of metrics for evaluation).
However, Fokoue-Nkoutche does not teach extracting metadata from the data source associated with the subset of predictions, wherein the metadata comprises entity metadata and predicted metadata; generating the set of metrics based on the metadata extracted and the subset of predictions.
The prior art to Tripathi teaches a System and Method for Mapping Biomedical Entities and further teaches, “Optionally , the method further comprises tagging the biomedical entities in the predefined class : drug , with one of the tags : approved , investigational , combinational , and potential (extracting metadata from the data source associated with the subset of predictions, wherein the metadata comprises entity metadata and predicted metadata). Furthermore , the processing module is operable to tag the biomedical entities in the predefined class : drug , with one of the tags : approved , investigational , com binational , and potential . Specifically , tagging the biomedical entities in the predefined class drug provides additional information associated with a specific biomedical entity (metadata). Beneficially , such tagging may provide verified information associated with specific drugs . Moreover , tagging the bio medical entities in the predefined class drug with the tag " approved ” indicates that the drug may be well known and frequently used in treatment of disease associated thereto (entity metadata and predicted metadata) [Tripathi at 0063].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to modify Fokoue-Nkoutche with biomedical metadata tagging as taught by Tripathi in order to identify additional beneficial relationships or correlations, among biomedical entities. One of ordinary skill in the art would have been motivated to use data source mining from Fokoue-Nkoutche, with metadata analyses by Tripathi to provide verified supplemental information which may associate features of well-known and well used drugs in treatment of an associated disease [Tripathi at 0063].
Combining these prior art elements would have been obvious because metadata which is not readily part of the biomedical entity predictions can identify additional important features. One of ordinary skill in the art would predict combining Fokoue-Nkoutche and Tripathi with a reasonable expectation of success they both relate to the field of predictive bioinformatics. The invention is therefore prima facie obvious.
With respect to claims 2, Fokoue-Nkoutche teaches “the output represents a measure of association between the biomedical entities of the biomedical entity pair. In this context, the biomedical entity pair under consideration may be new biomedical entities or newly paired biomedical entities, for which a prior association measure is not yet known or observed… analyzed using a classifier (predictive machine learning model) to determine a predicted ranking for the entity pair using the one or more first scores and the one or more second scores [ 0025-0026 and 0028: “ (top predictions ranked in relation to said one or more pre-trained predictive models) [Fokoue-Nkoutche at 0003, 0028, 0032 ].
With respect to claims 3-5, Fokoue-Nkoutche teaches the output represents a measure of association between the biomedical entities of the biomedical entity pair. In this context, the biomedical entity pair under consideration may be new biomedical entities or newly paired biomedical entities, for which a prior association measure is not yet known or observed.”) (top predictions ranked in relation to said one or more pre-trained predictive models) [Fokoue-Nkoutche at 0003, 0028, 0032 ].
However, Fokoue-Nkoutche does not teach said top predictions and associated metadata and said predicted metadata.
The prior art to Tripathi teaches a System and Method for Mapping Biomedical Entities and further teaches, “Optionally , the method further comprises tagging the biomedical entities in the predefined class : drug , with one of the tags : approved , investigational , combinational , and potential (extracting metadata from the data source associated with the subset of predictions, wherein the metadata comprises entity metadata and predicted metadata). Analogous metadata is provided “furthermore , the processing module is operable to tag the biomedical entities in the predefined class : drug , with one of the tags : approved, investigational , combinational , and potential . Specifically , tagging the biomedical entities in the predefined class drug provides additional information associated with a specific biomedical entity (metadata). Beneficially , such tagging may provide verified information associated with specific drugs . Moreover , tagging the bio medical entities in the predefined class drug with the tag " approved ” indicates that the drug may be well known and frequently used in treatment of disease associated thereto” (entity metadata and predicted metadata) [Tripathi at 0063].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to modify Fokoue-Nkoutche with biomedical metadata tagging as taught by Tripathi in order to identify additional beneficial relationships or correlations, among biomedical entities. One of ordinary skill in the art would have been motivated to use data source mining from Fokoue-Nkoutche, with metadata analyses by Tripathi to provide verified supplemental information which may associate features of well-known and well used drugs in treatment of an associated disease [Tripathi at 0063].
Combining these prior art elements would have been obvious because metadata which is not readily part of the biomedical entity predictions can identify additional important features. One of ordinary skill in the art would predict combining Fokoue-Nkoutche and Tripathi with a reasonable expectation of success they both relate to the field of predictive bioinformatics. The invention is therefore prima facie obvious.
With respect to claims 6, Fokoue-Nkoutche provides “The inference phase generally refers to a set of functions that evaluate a given biomedical entity pair's level of association (whether as a scale or as a binary value) by using the given biomedical entity pair as inputs to the trained neural network (models are trained using biomedical data), and by receiving an output of the trained neural network [Fokoue-Nkoutche at 0003, 0028, 0032].
. With respect to claim 7 and 16, Fokoue-Nkoutche provides for receiving a representation of an entity pair (paragraph 0036 “Referring now to FIGS. 2 and 3, specific training module 103 receives (step 302) biomedical entity pair 208 from an input source, such as from a user, a database (data source), a knowledge graph (109 FIG 1; [0031]), a remote server, or another source.”(using data further extracted from one or more sources).
With respect to claims 8, Fokoue-Nkoutche et al. provides the overall interface device, method, and system FIG 1 and 8 for receiving a representation of an entity pair (paragraph 0036 “Referring now to FIGS. 2 and 3, specific training module 103 receives (step 302) biomedical entity pair 208 from an input source, such as from a user, a database (data source), a knowledge graph (109 FIG 1; [0031]), a remote server, or another source. In the example depicted in FIG. 2, the biomedical entities in the biomedical entity pair 208 are one or more gene sequences, and one or more disease sequences, respectively.”) (biomedical domain); and using a classifier (predictive machine learning model) to determine a predicted ranking for the entity pair using the one or more first scores and the one or more second scores [0025-0026 and 0028].
With respect to claims 9, 14-15, and 21, Fokoue-Nkoutche teaches “the output represents a measure of association between the biomedical entities of the biomedical entity pair. In this context, the biomedical entity pair under consideration may be new biomedical entities or newly paired biomedical entities, for which a prior association measure is not yet known or observed… analyzed using a classifier (predictive machine learning model) to determine a predicted ranking for the entity pair using the one or more first scores and the one or more second scores [ 0025-0026 and 0028: “ (the set of metrics are based on one or a combination of: at least one overlap between the plurality of predictions, a set top correlations of objects in a database…) [Fokoue-Nkoutche at 0003, 0028, 0032 ].
With respect to claims 10 and 14, Fokoue-Nkoutche teaches “external devices 14 such as a keyboard, a pointing device, a display 24, etc.; one or more devices that enable a user to interact with computer system/server 12… via Input/Output (I/O) interfaces 22“ [Fokoue-Nkoutche at 0074] (displaying the set of metrics on an interface).
With respect to claims 11, Fokoue-Nkoutche teaches “in a classification scenario, a hyper-parameter threshold δ is selected as classification boundary” [Fokoue-Nkoutche at 0063] (select one or more predictions based on at least one predetermined criterion associated with the outputted set of metrics).
With respect to claims 12, Fokoue-Nkoutche teaches “the processing module is operable to tag the biomedical entities in the predefined class: drug, with one of the tags: approved, investigational, combinational, and potential. Specifically, tagging the biomedical entities in the predefined class drug provides additional information associated with a specific biomedical entity. Beneficially, such tagging may provide verified information associated with specific drugs. Moreover, tagging the biomedical entities in the predefined class drug with the tag “approved” indicates that the drug may be well known and frequently used in treatment of disease associated thereto” [Fokoue-Nkoutche at 0063] (wherein said at least one automated system is associated with the predictive machine learning model).
With respect to claims 12, Fokoue-Nkoutche provides “the output represents a measure of association between the biomedical entities of the biomedical entity pair. In this context, the biomedical entity pair under consideration may be new biomedical entities or newly paired biomedical entities, for which a prior association measure is not yet known or observed… analyzed using a classifier (predictive machine learning model) to determine a predicted ranking for the entity pair using the one or more first scores and the one or more second scores [Fokoue-Nkoutche at 0003, 0028, 0032 ]. (evaluating the entities of the data source based on the outputted set of metric).
With respect to claims 17-18, Fokoue-Nkoutche teaches “computer system/server 12 may also communicate with one or more external devices 14 such as a keyboard, a pointing device, a display 24, etc.; one or more devices that enable a user to interact with computer system/server 12… via Input/Output (I/O) interfaces 22 Still yet, computer system/server 12 can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter 20. As depicted, network adapter 20 communicates with the other components of computer system/server 12 via bus 18“ [Fokoue-Nkoutche at 0074] (one or more inputs and following the output of the set of metrics, wherein an externa/second application module configured to receive the outputted set of metrics and an associated metrics reference list from said at least one processor of the interface device).
With respect to claims 17-18, Fokoue-Nkoutche teaches “FIG. 7 is described in the context of an example, where the input of the model are raw amino acids sequences and molecule chemical structures, and, in terms of output, the model produces interpretations (reports) optimized for visualization, in addition to the DTI predictions themselves” [Fokoue-Nkoutche at 0048] (the report publisher is configured to collate and compile the received set of metrics and the associated metrics reference list to generate a representative report for visualising the set of metrics as display options on the interface device).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
A. Instant claims 1-22 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 2, 10, 14, 15-17, 27, 31 and 34 of US Patent No.12,633,382. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims are generic to ‘382, which is also a computer-implemented method of determining a biological entity pair of interest for drug discovery by electronically mining medical and scientific datasets to determine a ranking indicating a strength of evidence for an association between two biological entities.
Relevant prior art made of record and not relied upon:
Akhondi et al. US Publication 20210004586 A1 Methods, Systems, and Storage Media for Automatically Identifying Relevant Chemical Compounds in Patent Documents. Akhondi discloses methods, systems, and non-transitory media for training a chemical entity recognition system to extract chemical compounds from a patent document and determine a relevance of the chemical compounds to the patent document are disclosed. A method includes obtaining patent documents from patent databases, normalizing each patent document into a unified format, and generating a chemical patent corpus. The chemical patent corpus includes chemical entities, each having relevancy annotations that indicate a relevance to the patent document from which the chemical entity is extracted. The method further includes providing the chemical patent corpus to the chemical entity recognition system, which tags the one or more chemical entities in a corresponding normalized patent document, extracts additional chemical entities, assigns a confidence score to each additional chemical entity, and labels each additional chemical entity as relevant or irrelevant to an associated patent document based on information contained in the chemical patent corpus.
Beltre et al. US Publication 20190304575 A1 Monitoring Clinical Research Performance. Beltre discloses a computer-implemented method, system, and computer program product monitors clinical research performance. One or more metrics of clinical research performance for investigator/provider/research sites across research studies are collected. The metrics include performance area, characteristic of the performance area with one or more attributes, point values for each attribute, and weight value for the characteristic. A performance score is produced for each of the entities based on the one or more metrics. A machine learning model is trained to determine performance scores based on the produced performance score for each of the entities. A request for entities is processed by applying performance scores from the machine learning model and appropriate corresponding data to a predictive model to determine resulting performance scores, rank and/or match for each of the one or more entities for a given protocol and/or assessment trigger. Actions are performed based on the resulting performance scores, rank and/or match.
Zhang US Publication 20130339005 A1 Automated Extraction of Bio-Entity Relationships from Literature. Zhang discloses an automated, standardized and accurate extraction of relationships within text. Automatic extraction of such relationships/information allows the information to be stored in structured form so that it can be easily and accurately retrieved when needed. Such information can be used to build online search engines for highly specific and accurate information retrieval. The current invention discloses a novel approach to extract such information from raw text based on natural language processing (NLP) and graph theoretic algorithm. The novel method can be applied, for example, to extract protein-protein relationships in biomedical literature. The method can be easily extended to extract other biological relationships between biological terms such as proteins, genes, pathways, diseases and drugs. The method can also be applied to other information domains to extract other relationships.
Dandala et al. US Publication 20180032678 A1 Medical Record System
Dandala discloses technical solutions are described for determining a relationship between pairs of entities. An exemplary computer-implemented method includes generating, by a processor, an association score matrix for a pair of entities, the pair including a first entity and a second entity. The computer-implemented method also includes aggregating, by the processor, the association score matrix for the pair of entities into a single vector of association scores for the pair of entities. The computer-implemented method also includes computing, by the processor, a relationship score for the pair of entities based on the single vector of association scores. The computer-implemented method also includes, in response to the relationship score crossing a predetermined threshold, indicating that the first entity and the second entity are related to each other.
Conclusion
No claims are allowed.
E-mail Communications Authorization
Per updated USPTO Internet usage policies, Applicant and/or applicant’s representative is encouraged to authorize the USPTO examiner to discuss any subject matter concerning the above application via Internet e-mail communications. See MPEP 502.03. To approve such communications, Applicant must provide written authorization for e-mail communication by submitting following form via EFS-Web or Central Fax (571-273-8300): PTO/SB/439. Applicants are encouraged to do so as early in prosecution as possible, so as to facilitate communication during examination.
Written authorizations submitted to the Examiner via e-mail are NOT proper. Written authorizations must be submitted via EFS-Web or Central Fax (571-273-8300). A paper copy of e-mail correspondence will be placed in the patent application when appropriate. E-mails from the USPTO are for the sole use of the intended recipient and may contain information subject to the confidentiality requirement set forth in 35 USC § 122. See also MPEP 502.03.
Inquiries
Papers related to this application may be submitted to Technical Center 1600 by facsimile transmission. Papers should be faxed to Technical Center 1600 via the PTO Fax Center. The faxing of such papers must conform to the notices published in the Official Gazette, 1096 OG 30 (November 15, 1988), 1156 OG 61 (November 16, 1993), and 1157 OG 94 (December 28, 1993) (See 37 CFR § 1.6(d)). The Central Fax Center Number is (571) 273-8300.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Vy Rossi, whose telephone number is (703) 756-4649. The examiner can normally be reached on Monday-Friday from 8:30AM to 5:30PM ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Olivia Wise, can be reached on (571) 272-2249. Any inquiry of a general nature or relating to the status of this application or proceeding should be directed to (571) 272-0547.
Patent applicants with problems or questions regarding electronic images that can be viewed in the Patent Application Information Retrieval system (PAIR) can now contact the USPTO’s Patent Electronic Business Center (Patent EBC) for assistance. Representatives are available to answer your questions daily from 6 am to midnight (EST). The toll free number is (866) 217-9197. When calling please have your application serial or patent number, the type of document you are having an image problem with, the number of pages and the specific nature of the problem. The Patent Electronic Business Center will notify applicants of the resolution of the problem within 5-7 business days. Applicants can also check PAIR to confirm that the problem has been corrected. The USPTO’s Patent Electronic Business Center is a complete service center supporting all patent businesses on the Internet. The USPTO’s PAIR system provides Internet-based access to patent application status and history information. It also enables applicants to view the scanned images of their own application file folder(s) as well as general patent information available to the public.
/VR/
Examiner
Art Unit 1685
/MARY K ZEMAN/Primary Examiner, Art Unit 1686
