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 . 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.
DETAILED ACTION
The following NON-FINAL Office action is in response to application 18464785 filed on 09/11/2023 and the Applicant’s election dated 08/14/2025.
Status of Claims
- Claims 1-20 are currently pending of which:
= Claims 17-20 are withdrawn from consideration as directed to a non-elected invention.
= Claims 1-16 are elected, currently under examination and have been rejected as follows.
Election/Restrictions
Claims 17-20 are withdrawn from further consideration as drawn to a nonelected invention. Applicant’s election with traverse of Invention Group I representing Claims 1-16 in the reply filed on 08/14/2025 is acknowledged. Applicant timely traversed the restriction (election) requirement in the reply filed on 08/14/2025 and is acknowledged. The traversal is on grounds that Claims 1-20 are all classified under parent hierarchy of G06Q10 and there is no persuasive indication in the Office Action that the subject matter of any of these claims falls into a (A) separate classification, (B) status in the art, or (C) field of search [bolded emphasis added]. Examiner fully considered the Applicant’s argument but respectfully disagrees by reminding that burden can be established by any of (A), (B) or (C) as admitted by Applicant above. While the Applicant provides a brief rebuttal by reliance on (A), the Applicant is mute with respect to points (B) or (C). Thus, the Examiner submits that given the uncontested points (B) or (C), the restriction requirement is proper. Examiner continues to rely on MPEP 808: Reasons for Insisting Upon Restriction, MPEP 808.02: Establishing Burden: Where the inventions as claimed are shown to be independent or distinct under the criteria of MPEP § 806.05(c) - § 806.06, the examiner, in order to establish reasons for insisting upon restriction, must explain why there would be a serious burden on the examiner if restriction is not required. Thus, the Examiner must show by appropriate explanation one of the following:
(A) Separate classification thereof: This shows that each invention has attained recognition in the art as a separate subject for inventive effort, and also a separate field of search. Patents need not be cited to show separate classification.
(B) A separate status in the art when they are classifiable together: Even though they are classified together, each invention can be shown to have formed a separate subject for inventive effort when the examiner can show a recognition of separate inventive effort by inventors. Separate status in the art may be shown by citing patents which are evidence of such separate status, and also of a separate field of search.
(C) A different field of search: Where it is necessary to search for one of the inventions in a manner that is not likely to result in finding art pertinent to the other invention(s) (e.g., searching different classes/subclasses or electronic resources, or employing different search queries, a different field of search is shown, even though the two are classified together. The indicated different field of search must in fact be pertinent to the type of subject matter covered by the claims. Patents need not be cited to show different field of search.
As per (A), Applicant merely argues the claims are all classified in the broad parent grouping of G06Q10, yet the Applicant is silent on Examiner’s finding of Group I distinctively classified in G06Q10/10 [bolded emphasis added] and of Group II distinctively classified in G06Q10/06375 [bolded emphasis added]. Simply arguing the claims correspond to G06Q10 at the top of the classification hierarchy does not negate the burden of separate classification: G06Q10/10 versus G06Q10/06375 for distinct inventions.
As per (B), the Applicant merely argues there is no persuasive indication in the Office Action that the subject matter of any of these claims have acquired a separate status in the art, without providing any evidence for the Applicant’s allegation. In the absence of such evidence, Examiner resubmits that per (B) the inventions have acquired a separate status in the art due to their recognized divergent subject matter; as demonstrated here by the following recitations:
- at Group I: Hardware and software components: a computing device housing a processor and a memory, the memory storing instructions for execution by the processor; a user input device in electronic communication with the computing device; a monitor in electronic communication with the computing device, the monitor configured to display the user interface; and an electronic communication line connecting the computing device to a server containing a database; with the wherein limitation of Claim 1, is tested per MPEP 2111.04 as not indicative of limiting effect of which of the components above is operable to estimate a likelihood of success for a development of a medical technology product.
* versus *
- at Group II: step to display factors contributing to likelihood of success determination on a graphical chart.
As per (C), Applicant argues there is no persuasive indication in the Action that the inventions require a different field of search, without providing any evidence for such assertion. In the absence of such evidence, Examiner resubmits that per (C), the inventions require a different field of search (for example, searching different classes/subclasses or electronic resources, or employing different search queries):
-> searching different classification: G06Q10/10 vs. G06Q10/06375, and
-> separate keywords and queries of both patentable and non-patentable literature for:
- synonyms for hardware and software components such as: a computing device housing a processor and a memory storing instructions for execution by the processor; monitor in electronic communication with the computing device, the monitor configured to display the user interface; and an electronic communication line connecting the computing device to a server containing a database; at Inventive Group I,
* versus *
- synonyms for graphed factors such as (factor OR cause OR root OR aspect OR reason OR motive OR motivation OR aim OR goal OR objective) WITH ((on OR upon) NEAR3 (graph$4 OR figure OR draw$6 OR animat$6)) at Inventive Group II.
In conclusion, the Examiner not only provided a single one, but in effect, a preponderance of evidence (A), (B), (C), each or all, demonstrating the restriction/election requirement is proper.
Accordingly, the requirement is still deemed proper and is therefore made FINAL.
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 1-16 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, here abstract idea) without significantly more. The claim(s) recite(s) describe or set forth the abstract estimat[ing] a likelihood [or probability] of success for a development of a medical technology product as summarized by the Title of the invention and then mentioned throughout Claims 1-16. This estimat[ing] a likelihood [or probability] of success for a development of a medical technology product is tested per MPEP 2106.04(a)(2) III C. and found to fall within the abstract grouping of computer-aided mental processes. Specifically,
Examiner follows MPEP 2106.04(a)(2) III C to submit: #1 Performing a mental process on a generic computer, # 2. Performing a mental process in a computer environment, and # 3. Using a computer as tool to perform a mental process, do not preclude the claims to recite, describe or set forth the abstract idea.
It then follows that here, when tested per MPEP 2106.04(a)(2) III C # 2 the broad recitation of utiliz[ation] of artificial intelligence, as in “wherein the development of the medical technology product utilizes artificial intelligence” (dependent Claim 3) and “wherein the medical technology product utilizes artificial intelligence” (dependent Claim 4), can be argued to represent, under MPEP 2106.04(a)(2) III C # 2, a computer environment upon which the abstract process is being performed.
It then also follow that here, when similarly tested per MPEP 2106.04(a)(2) III C (#1),(#3), the “computer system” as (#1) generic computer or (#3) computer tool “operable to estimate a likelihood of success for a development of a medical technology product” (independent Claim 1), “operable to estimate the likelihood of success based on a scaling factor” (dependent Claim 13), or “operable to calculate an overall rating” (dependent Claim 15), would similarly not preclude the claims from reciting, describing or setting forth the abstract computer-aided mental processes. The same (#1) generic computer or (#3) computer tool rationale applies to recitations of “wherein the database stores a plurality of files and information necessary for an operation of a probability of success estimator” (dependent Claim 2), and the generic or tool related “user interface” recited as: “wherein the user interface displays the likelihood of success as a percentage” (dependent Claim 5), “wherein the user interface displays a report explaining a plurality of reasons for the likelihood of success” (dependent Claim 6), “wherein the user interface displays an overall process score for an area of interest” (dependent Claim 12), “wherein the user interface displays a thermometer chart for an area of interest” (dependent Claim 16), “wherein the user interface displays a graphical chart comprising four corners, wherein each of the four corners corresponds to one of four areas of interest” (dependent Claim 7), with subsequent recitations of: “wherein a first one of the four areas of interest is a project preparation and planning” (dependent Claim 8), “wherein a second one of the four areas of interest is a data science” (dependent Claim 9), “wherein a third one of the four areas of interest is a model development and optimization” (dependent Claim 10) and “wherein a fourth one of the four areas of interest is a risk, mitigation, and model validation” (dependent Claim 11) merely narrowing the abstract idea to limited application which according to MPEP 2106.04 I ¶3 do not render the claims less abstract and eligible. Separately from the above rationale, it could perhaps also be argued that “the four corners” of dependent Claims 7-1, and the “thermometer chart” (dependent Claim 16), could be tested under the non-functional and/or printed matter test of MPEP 2111.05 ¶1 as carrying limited patentable weight.
In any event, given the preponderance of legal evidence shown above, it is clear that, the currently claimed computer-aided abstract functions are within the practical cognitive capabilities of one of ordinary skills in the art, such as evaluation judgment and observation as enunciated by MPEP 2106.04(a)(2) III ¶2. Specifically, Examiner, as a person of ordinary skills in the art, finds that here, nothing would have precluded a skilled artisan to perform by aid of computer the aforementioned computer-aided evaluating or estimat[ing] [of] “a likelihood of success for a development of a medical technology product” (Claims 1,13) and computer-aided observation or “display” (Claims 5-7,12,16). This finding is corroborated by MPEP 2106.04(a)(2) III. A., 5th bullet point1, stating that the combination of collecting information, analyzing it, and displaying certain results of the collection and analysis, still falls within the abstract mental processes.
- Here, such computer-aided observation or collecting is set forth as “the database stores a plurality of files and information necessary for an operation of a probability of success estimator” (dependent Claim 2)
- Here, such computer-aided evaluation, judgment or analysis is set forth as: “wherein the computer system is operable to estimate a likelihood of success for a development of a medical technology product” at independent Claim 1, “wherein the computer system is operable to estimate the likelihood of success based on a scaling factor.” at dependent Claim 13, “wherein the scaling factor comprises at least one of a prior experience or a regulatory pathway” at dependent Claim 14, “wherein the computer system is operable to calculate an overall rating” at dependent Claim 15.
- Here, the computer-aided notification or display of certain results of the collection and analysis is set forth by: “wherein the user interface displays the likelihood of success as a percentage” at dependent Claim 5, “wherein the user interface displays a report explaining a plurality of reasons for the likelihood of success” at dependent Claim 6, “wherein the user interface displays an overall process score for an area of interest” at dependent Claim 12, “wherein the user interface displays a thermometer chart for an area of interest” at dependent Claim 16, “wherein the user interface displays a graphical chart comprising four corners, wherein each of the four corners corresponds to one of four areas of interest” at dependent Claim 7 and further abstractly narrowed at dependent Claims 8-11.
Thus, here, there is a preponderance of legal evidence, showing that the claims recite, or at a minimum describe or set forth the abstract exception, with the computer implementation of the abstract processes identified above, arguable under MPEP 2106.04(a)(2) III C, #1,#2, #3, as tools or computer environments to aid in the execution of the abstract exception. Step 2A prong one. Next, in an abundance of caution the Examiner will next more granularly test such computer components below.
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This judicial exception is not integrated into a practical application because per Step 2A prong two, because the individual or combination of the additional, computer-based elements is/are found to merely apply the abstract idea identified above [MPEP 2106.05(f)] and/or narrow it to a field of use or technological environment [MPEP 2106.05(h)]. For example, here, the “user interface”, “monitor configured to display the user interface”, “database” etc., even if considered as additional computer-based elements rather than computer aids, they would still merely apply the aforementioned abstract or existing processes, which according to MPEP 2106.05(f)(2), would merely represent an invocation of machinery such as computers that would not integrate the aforementioned abstract exception into a practical application.
For example when testing recitation of “wherein the database stores a plurality of files and information necessary for an operation of a probability of success estimator” (dependent claim 2), per MPEP 2106.05(f)(2) ¶12, the Examiner finds that use of a computer or other machinery in its ordinary capacity for performing economic or other tasks (e.g., to receive, store, or transmit data) does not integrate a judicial exception into a practical application.
Similarly, when testing per MPEP 2106.05(f)(2) v3, the recitations of: “wherein the user interface displays the likelihood of success as a percentage” at dependent Claim 5, “wherein the user interface displays a report explaining a plurality of reasons for the likelihood of success” at dependent Claim 6, “wherein the user interface displays an overall process score for an area of interest” at dependent Claim 12, “wherein the user interface displays a thermometer chart for an area of interest” at dependent Claim 16, “wherein the user interface displays a graphical chart comprising four corners, wherein each of the four corners corresponds to one of four areas of interest” at dependent Claim 7 and further abstractly narrowed at dependent Claims 8-11, the Examiner finds that requiring the use of software [here “user interface”] to tailor information and provide it to the user on a generic computes, represents a mere invocation of computers or machinery as a tool to perform an existing process, which again is an example of application the abstract idea that does not integrate it into a practical application. Also, the broad recitation of utiliz[ation] of artificial intelligence, as in “wherein the development of the medical technology product utilizes artificial intelligence” (dependent Claim 3) and “wherein the medical technology product utilizes artificial intelligence” (dependent Claim 4), when more granularly tested per MPEP 2106.05(h), could be argued as examples of narrowing the abstract exception, as identified above, to a field of use or technological environment, which again would not integrate the abstract exception into a practical application. Step 2A prong two.
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The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, Examiner follows MPEP 2106.05(d) II guidelines and carries over the above findings tested per MPEP 2106.05 (f) and/or (h) to submit that as shown above, the additional computer-based elements, merely apply the already recited abstract idea [MPEP 2106.05(f)] and/or narrow the abstract exception to a field of use or technological environment [MPEP 2106.05(h)].
For these same reasons, said computer-based additional elements also do not provide significantly more than the abstract idea itself in light of MPEP 2106.05(f) and/or (h) as sufficient option(s) for evidence without even having to rely on the well understood routine and conventional test of MPEP 2106.05(d).
Yet, assuming arguendo that additional evidence would now be required at Step 2B to demonstrate said conventionality, Examiner would further point to MPEP 2106.05(d) I. 2 and rely on Applicant’s own Original Disclosure demonstrating conventionality of the additional computer-based elements as follows:
- Original Specification ¶ [13] 1st sentence, reciting at a high level of generality: Generally, the present disclosure concerns a computer system for estimating the probability of success of a project.
- Original Specification ¶ [14] reciting at high level of generality: “In one embodiment, the system may include a non-transitory computer readable medium or memory, which contains instructions allowing and instructing at least one central processing unit ("CPU" or "processor") to carry out the steps required during estimating a likelihood of success, as described herein. This non-transitory computer readable medium or memory may be housed within a computing device, or may be accessible through an electronic communication system, such as a network. When used herein the term "computing device" means any electronic device having a processor, memory, and a graphical user interface ("GUI") display, including, but not limited to, a cellular phone, a tablet, a laptop, or a desktop. Also, when used herein the term "network" refers to any system of interconnected electronic devices, such as, a cellular communication network or the Internet. Connections in the network may be wired or wireless.
- Original Specification ¶ [58] 1st sentence reciting at high level of generality: …it is apparent that further embodiments could be developed within the spirit and scope of the present disclosure, or the inventive concept thereof.
In conclusion, Claims 1-16 although directed to statutory categories (“system” or machine) they still recite, or at least set forth the abstract idea (Step 2A prong one), with their additional, computer based elements not integrating the abstract idea into a practical application (Step 2A prong two) or providing significantly more than the abstract idea itself (Step 2B). Therefore, the Claims 1-16 are not patent eligible.
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Claim Rejections - 35 USC § 102
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-6 and 12-15 are rejected under 35 U.S.C. 102(a)(1) based upon a public use or sale or other public availability of the invention as disclosed by: Vergetis et al, US 20200321083 A1 hereinafter Vergetis.
Claim 1. Vergetis teaches A computer system comprising:
a computing device housing a processor and a memory, the memory storing instructions for execution by the processor; a user interface facilitating access to the computer system;
(Vergetis ¶ [0112] 2nd sentence: Computer-readable media include magnetic media such as a hard disk drive, optical media such as a Compact Disk (CD) or a Digital Versatile Disk (DVD), a persistent or non-persistent solid-state memory (e.g., Flash memory, Magnetic RAM, etc.), or any other suitable storage media. ¶ [0113] 4th sentence: These advantageous structures may then provide functionality to the storage medium by affecting operations of one or more processors interacting with the information; for example, by increasing the efficiency of computer operations performed by the processor(s) ).
“a user input device in electronic communication with the computing device”;
(Vergetis ¶ [0116] 1st, 4th sentences: A computing device may additionally have components and peripherals, including input devices used for a user interface include keyboards, and pointing devices, such as mice, touch pads, and digitizing tablets. ¶ [0016] 2nd sentence: the interface includes input controls that permit a user to modify parameters of the clinical trial associated with the particular drug, and responsive to the modified parameters, the analytics engine being further adapted to determine, for the particular one of the plurality of drugs, a modified likelihood that the clinical trial associated with the particular drug will be successful, and wherein the interface adapted to identify, to an entity, a measure of the modified likelihood.
“a monitor in electronic communication with the computing device, the monitor configured to display the user interface” (Vergetis ¶ [0060] 4th sentences: End user systems 104 may be, any computer system having a display. ¶ [0116] 3rd sentence: Examples of output devices that can be used to provide a user interface include display screens for visual presentation of output and speakers or other sound generating devices for audible presentation of output); “and”
“an electronic communication line connecting the computing device to a server containing a database”; (Vergetis ¶ [0115] 2nd-3rd sentences: A computing device may be, a server. A network adapter may be any suitable hardware and/or software to enable the computing device to communicate wired and/or wirelessly with the computing device over any suitable computing network. ¶ [0014] the system is provided comprising a database adapted to store a plurality of sets of data relating to clinical trials of a plurality of drugs, an analytics engine adapted to determine, for a particular one of the plurality of drugs, a likelihood that the clinical trial associated with the particular drug and a particular indication will be successful, and an interface adapted to identify, to an entity, a measure of the likelihood. In some embodiments, the analytics engine includes at least one AI-based model optimized to maximize the likelihood. Similarly, ¶ [0061] 4th sentence: the distributed system include databases (e.g. database(s) 108) adapted to store one or more data elements associated with a clinical trial and/or related information that can be used by the system)
- “wherein the computer system is operable to estimate a likelihood of success for a development of a medical technology product”.
(Vergetis ¶ [0072] 6th-10th sentences: The system estimate an average historical likelihood of FDA approval [success] for drugs at start of phase 2 development. In one implementation, the probability is expressed as % likelihood that the result will occur. Further, the interface may be adapted to show the top predictive features by future category and their significance for a selected trial. Specifically, the interface may show certain aspects of a future category such as trial design, trial set up, science, or other feature category along with their strength of predictive features. This value may be expressed, as % strength.
Vergetis Fig.4, ¶ [0073] 1st, 3rd 4th sentence: an interface include controls to adjust the simulation to produce the desired improved probability of FDA approval [or success] value. For example, the number of endpoints, number of patients, number of sites, type of trial, among other information may be used to adjust the simulation in order to improve the probability that a particular clinical trial will be successful (e.g. that FDA approval may be received). For example, at Fig.4, under ORR as primary endpoint marked as Yes, # of primary endpoints of 2, use of biomarkers in inclusion / exclusion criteria set as Yes, and masking/ binding set as yes, and a historic likelihood of the FDA approval for drugs at start of Phase 2 development marked as 15%, the probability of FDA approval increases from a current design of 69% to 74%)
Claim 2. Vergetis teaches all the limitations in claim 1 above. Furthermore,
Vergetis teaches “wherein the database stores a plurality of files and information necessary for an operation of a probability of success estimator” (Vergetis ¶ [0062] 2nd-3rd sentences: an analytics engine may be trained on database elements. The data may be relational, documents, files, or any other type of structured or unstructured data. Specifically, at ¶ [0075] As discussed above, a unique database/data architecture is provided that maps the features/data that are captured across various data sources. It In particular, parameters relating to one or more of clinical trial outcomes, regulatory parameters, company experience information, drug molecule characteristics, gene expression, indication characteristics, and/or clinical trial design characteristics may be captured and stored in a multidimensional database. Such a database may be used to train one or more AI models for the purpose of creating a predictive model for clinical trial outcomes. Specifically, per ¶ [0014]: a system is provided comprising a database adapted to store a plurality of sets of data relating to clinical trials of a plurality of drugs, an analytics engine adapted to determine, for a particular one of the plurality of drugs, a likelihood that the clinical trial associated with the particular drug and a particular indication will be successful. In some embodiments, the analytics engine includes at least one AI-based model optimized to maximize the likelihood).
Claim 3. Vergetis teaches all the limitations in claim 1 above. Furthermore,
Vergetis teaches “wherein the development of the medical technology product utilizes artificial intelligence” (Vergetis ¶ [0007] 2nd sentence: machine learning techniques are applied to solve clinical trial design problems with a focus on de-risking the clinical development process. ¶ [0046] 2nd sentence: the system applies machine learning to optimize parameters of the clinical trial design).
Claim 4. Vergetis teaches all the limitations in claim 1 above. Furthermore,
Vergetis teaches “wherein the medical technology product utilizes artificial intelligence”.
(Vergetis ¶ [0072] 3rd sentence: the drug Gazyva having an active ingredient Obinutuzumab used for Leukemia treatment is evaluated utilizing a trained machine learning model).
Claim 5 Vergetis teaches all the limitations in claim 1 above. Furthermore,
Vergetis teaches “wherein the user interface displays the likelihood of success as a percentage”
(Vergetis ¶ [0072] 6th-10th sentences: The system also estimate an average historical likelihood of FDA approval for drugs at the start of phase 2 development. In one implementation, the probability may be expressed as a percentage likelihood that the result will occur. Further, in some embodiments, the interface may be adapted to show the top predictive features by future category and their significance for a selected trial. More particularly, the interface may show certain aspects of a future category such as trial design, trial set up, science, or other feature category along with their strength of predictive features. This value may be expressed, for example, as a percentage of strength. For example, see Fig. 4 noting the historic likelihood of the FDA approval for drugs at start of Phase 2 development marked as 15%)
Claim 6. Vergetis teaches all the limitations in claim 1 above.
Vergetis further teaches “wherein the user interface displays a report explaining a plurality of reasons for the likelihood of success” (Vergetis ¶ [0072] 6th-10th sentences: The system estimate a likelihood of FDA approval for drugs at the start of phase 2 development. The probability may be expressed as a percentage likelihood that the result will occur. Further, in some embodiments, the interface may be adapted to show the top predictive features [reasons] by future category and their significance for a selected trial. More particularly, the interface may show certain aspects of a future category such as trial design [reason], trial set up [reason], science [reason], or other feature category along with their strength of predictive features [reason]. This value may be expressed, for example, as a percentage of strength.
Vergetis Fig.4, ¶ [0073] 1st-4th sentence: an interface includes controls to adjust the simulation to produce the desired improved probability of FDA approval value. For instance, predictive features may be within a company's control, and therefore what if scenarios may be tested against the model using various different trial design inputs. For example, the number of endpoints [reason], number of patients [reason], number of sites [reason], type of trial [reason], among other information may be used to adjust the simulation to improve the probability that a particular clinical trial will be successful (e.g., that FDA approval may be received). For example at Fig.4, under ORR as primary endpoint marked as Yes, # of primary endpoints of 2, use of biomarkers in inclusion / exclusion criteria [reason] set as Yes, and masking/ binding set as yes, and a historic likelihood of the FDA approval for drugs at start of Phase 2 development marked as 15%, the probability of FDA approval increases from a current design of 69% to a simulation scenario of 74%).
Claim 12. Vergetis teaches all the limitations in claim 1 above. Furthermore,
Vergetis teaches: “wherein the user interface displays an overall process score for an area of interest” (Vergetis ¶ [0067] 3rd sentence: provide analytical information, such as aggregation [or overall] information. For example, at ¶ [0009] 3rd sentence: the system is capable of identifying unique immuno-oncology targets and retrieve relevant drugs and all [or overall] unique drug-indication pairs the system is capable of identifying unique immuno-oncology targets and retrieve relevant drugs and all [or overall] the unique drug-indication pairs. For example, see Fig. 7A noting the interface displays a total of 2536 drugs [ranked or scored] by category, indication, phase development etc. as examples of area of interest. Fig. 7B, Fig.10 noting the interface displays a total of 2536 drugs by geography as another area of interest. Similarly, Fig. 7C noting total trial by phase number or area of interest. Fig. 8A noting a total of 2536 drugs by category: virus, immonomdulator, vaccine, CD3, T-cell, cell therapy. Similarly, Figs. 8B-8D, 11A-11B, 19-21)
Claim 13. Vergetis teaches all the limitations in claim 1 above. Furthermore,
Vergetis teaches “wherein the computer system is operable to estimate the likelihood of success based on a scaling factor” (Vergetis ¶ [0093] 2nd sentence: The predictions of the algorithms are scaled. For example, at ¶ [0072] 4th-6th sentences the interface include calculation of probability of FDA approval for the clinical trial of Gazyva. In this example, the probability may relate to FDA approval estimated at a phase 2 start. The system may also estimate an average historical likelihood of FDA approval for drugs at the start of phase 2 development. Another example at Fig.4, ¶ [0073])
Claim 14. Vergetis teaches all the limitations in claim 13 above. Furthermore,
Vergetis teaches “wherein the scaling factor comprises at least one of a prior experience”
(Vergetis ¶ [0013] 1st sentence: company experience (number of trials the company conducted in the specific therapeutic area and/or indication) is highly indicative as to the probability of success. Similarly, ¶ [0048]. Also, ¶ [0006] 1st-2nd sentences: maps the features/data captured across various data sources. In particular, parameters relating to clinical trial outcomes, regulatory parameters, company experience info. ¶ [0010] 4th sentence: system takes into account historical success rates for different indications to update probability estimates. Similarly, ¶ [0072] 10th sentence: in Fig.3B, a company's clinical development experience may be shown to be a highly predictive feature associated with the approval probability) “or a regulatory pathway” (Vergetis ¶ [0072] 4th-6th sentences: the interface include calculation of a probability of FDA approval for the clinical trial of Gazyva. In this example, the probability may relate to FDA approval estimated at a phase 2 start. The system may also estimate an average historical likelihood of FDA approval for drugs at the start of phase 2 development. Other, example at Fig.4, ¶ [0073])
Claim 15. Vergetis teaches all the limitations in claim 1 above. Furthermore
Vergetis teaches “wherein the computer system is operable to calculate an overall rating”
(Vergetis ¶ [0048] … the system employs a unique methodology for processing by machine learning algorithms variables/features across multiple different categories, which include: Clinical outcomes of the drug reported in previous phases (e.g. overall response rate (ORR)). ¶ [0077] 2nd-3rd sentences: training the model, the system capture (i) if each of those outcomes was measured or not, (ii) the value of the outcome (if it was measured), and (iii) any specific attributes of the value (e.g., if it was measured at 3 months, 6 months, etc.). Some of the information used to train the model may include one or more of the feature categories: overall response rate (ORR), overall survival (OS). Another example at ¶ [0091]. Yet another example at ¶ [0094] 4th-6th sentences: The algorithms may be used to rank the features in terms of their importance/predictive power. In particular, the drugs of a certain portfolio may be ranked based on our predicted PTRS [Probability of Technical and Regulatory Success]. It is appreciated that error ranges vary for each prediction/drug, and bootstrapping may be used to assign a confidence level [or overall rating] to each of our estimates. ¶ [0091] 3rd sentence: For example, good performance can be a ML model with an AUC [area under curve] >0.85; OK/average performance can be a ML model with AUC>0.78, and/or the like. In some embodiments, the best performing algorithms are chosen for making PTRS (Probability of Technical and Regulatory Success) estimates (e.g., pooling the best performing algorithms))
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Rejections under 35 § U.S.C. 103
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 of this title, 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.
Claims 7-11 are rejected under 35 U.S.C. 103 as being unpatentable over:
Vergetis as applied to claim 1 above and in view of
Peter Keeling US 20090132337 A1 hereinafter Keeling. As per,
Claim 7. Vergetis teaches all the limitations in claim 1 above.
Vergetis Figs.3A-B, mid-¶ [0072] teaches areas of interest such as: trial design, trial set up, science, or other feature category along with their strength of predictive features etc. However,
Vergetis does not exactly recite corners as in “wherein the user interface displays a graphical chart comprising four corners, wherein each of the four corners corresponds to one of four areas of interest”.
Keeling however in analogous analysis of therapeutic or medical products teaches or suggests:
“wherein the user interface displays a graphical chart comprising four corners, wherein each of the four corners corresponds to one of four areas of interest” (Keeling ¶ [0024] 1st sentence Referring to Fig.4 below, which is a spider [or corner] diagram which represents the differences between the two drugs in a spatial manner. ¶ [0022] 2nd-5th sentences: The first element is efficacy which has a brand essence which indicates to what extent the product provides clinically significant efficacy. The second element is trustability which gives a brand essence indicator of the extent to which the provider can trust a decision to prescribe the particular drug or brand. The third element is imitability, which has a brand essence indicator of the extent the product can be replaced once adopted. The adverse event (AE) or side effect profile element has a brand essence indicator to the extent to which the provider can minimize AE. Etc. etc.)
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have modified Vergetis’ “system” to have included Keeling teachings in order to have better understood how personalized therapy would have aided the competitive dynamics of an individual therapy brand so as to have optimized timescales and benefits for both the pharmaceutical and diagnostic business models (Keeling ¶ [0006] in view of MPEP 2143 G and/or F). The predictability of such modification would have been further corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Vergetis ¶ [0122] in view of Keeling ¶ [0028].
Further, the claimed invention , could have also been viewed as a mere combination of old elements in a similar field of endeavor dealing with therapeutic or medical products. In such combination each element merely would have performed same analytical, benchmarking and display function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements as evidenced by Vergetis in view of Keeling, the to be combined elements would have fitted together, like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A).
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Keeling Fig.4 in support of rejection arguments or “corners corresponds to one of four areas of interest”
Claim 8 Vergetis / Keeling teaches al the limitations in claim 7 above. Further,
Vergetis teaches/suggests “wherein a first one of the four areas of interest is a project preparation and planning” (Vergetis ¶ [0046] 1st sentence: planning that a drug in clinical development will progress to next phase of development (e.g from Phase 2 to 3), and/or receive FDA approval. ¶ [0097] 5th-7th sentences: Dashboard view also include a trial area that allows the user to view clinical trial activity by monotherapy, combination therapies, category, indication, phase of development, and/or other parameters. The dashboard view also include a who view that includes items relating to who is conducting the trials (e.g., stakeholder such as pharma companies or other industries), a view that shows geography of where clinical trials are being held, and a view that allows the user to drug development over time. Through these dashboard views, user more quickly locate clinical trial info without having to access multiple sources).
Keeling also teaches or suggests: “wherein a first one of the four areas of interest is a project preparation and planning” (Keeling ¶ [0005] the advent of personalized medicine requires parallel development of a diagnostic; and diagnostic market or franchise to enable doctors to choose and monitor specific therapies. To this end at Figs. 2,4 and ¶ [0022] 4th sentence: third element is imitability, which has a brand essence indicator of the extent the product can be replaced once adopted. Figs.2,4,¶ [0023] 3rd-4th sentences: The standard of care potential element has a brand essence relating to the extent to which the therapy is likely to become [or prepared as] a standard of care. The targeted element has a brand essence relating to the extent to which the treatment can be tailored or optimized [or planned] for a particular patient).
Rationales to have modified/combined Vergetis / Keeling were presented above.
Claim 9. Vergetis / Keeling teaches all the limitations in claim 7 above.
Vergetis teaches /suggests “wherein a second one of the four areas of interest is a data science”
(Vergetis ¶ [0072] 9th sentence: The interface show certain aspects such as science)
Keeling also teaches/suggest “wherein a second one of the four areas of interest is a data science”
(Keeling Fig.4 and ¶ [0022] 7th sentence: evidence [or science] based positioning elements the brand essence relates to the drug offering an opportunity for the right product rather than the prescription. ¶ [0025] 9th sentence: Fig.6 c shows the elements where drug B scores better than drug A and include consistent outcome, uniqueness, ethicality, standard of care, evidence-based, imitability, and targeting).
Rationales to have modified/combined Vergetis / Keeling were presented above.
Claim 10 Vergetis / Keeling teaches all the limitations in claim 7 above.
Vergetis teaches/suggests “wherein a third one of the four areas of interest is a model development and optimization” (Vergetis ¶ [0075] 3rd sentence: train AI models for the purpose of creating a predictive model for clinical trial outcomes. Figs. 4-5 and ¶ [0093] A combined model may be then created by using top algorithms found from previous step and that uses also different types of ML methods. The predictions of the algorithms are scaled, and sample predictions are made for trials that are still under development. Outcome probabilities are modeled for trials with changed characteristics than the original ones to optimize trial design. Also, the system outputs a comparison list between different drugs by outcome probability. Figs. 2C, 3A,4 and ¶ [0094] 1st sentence: There are a number of predictive signals and their respective strength, that the system determine, from the ML model which drive of probability that a drug in clinical development will receive FDA approval).
Keeling also teaches/suggest “wherein a third one of the four areas of interest is a model development and optimization” (Keeling ¶ [0023] 3rd -4th sentences: The standard of care potential element has a brand essence relating to the extent to which the therapy is likely to become [or be developed] a standard [or model] of care. The targeted element has a brand essence relating to the extent to which the treatment can be tailored or optimized for a particular patient)
Rationales to have modified/combined Vergetis / Keeling were presented above.
Claim 11 Vergetis / Keeling teaches all the limitations in claim 7 above.
Vergetis teaches or suggests “wherein a fourth one of the four areas of interest is a risk, mitigation, and model validation” (Vergetis ¶ [0005] 2nd sentence: the clinical trial process is expensive in both time and money and is wrought with risk. ¶ [0007] 2nd-5th sentences: de-risking [or mitigating of risk] the clinical development process. In some embodiments, a computer-based system is provided that is configured to determine, in real time, estimates for a probability of regulatory approval for drugs. For instance, the system may be adapted to provide a probability estimate for drugs that are in a particular phase or juncture of a clinical trial. For example, the system may be adapted to determine a probability at Phase 1, Phase 2, or Phase 3 or any intermediary point of a clinical trial. ¶ [0089] 2nd sentence: the training set 282 is split (e.g, through cross-validation) in order to train and/or select the best model(s))
Keeling also teaches or suggests: “wherein a fourth one of the four areas of interest is a risk” (Keeling Fig.4, ¶ [0022] 2nd sentence: The first element is efficacy which has a brand essence which indicates to what extent the product provides clinically significant efficacy. Fig.4, ¶ [0022] 5th sentence: the adverse [or risk] event (AE) or side effect profile element has a brand essence indicator to the extent to which the provider minimize the [risky or adverse event] AE), “mitigation” (Keeling Fig.4, ¶ [0022] 5th sentence: the adverse event (AE) or side effect profile element has a brand essence indicator to the extent to which the provider minimize [or mitigate] the [adverse event] AE. Also Fig.4, ¶ [0022] 6th sentence: For the convenience element the brand essence relates to the efficiency and ease with which a patient can adhere to the therapy), “and model validation” (Keeling Fig.4, ¶ [0023] 8th sentence: The consistent outcome positioning element has a brand essence which relates to the extent to which a test will give a consistent [or validation] answer for each unique patient)
Rationales to have modified/combined Vergetis / Keeling were presented above.
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Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over
Vergetis as applied to claim 1 above, and in view of
Goodgame et al, US20130346093A1 hereinafter Goodgame. As per,
Claim 16. Vergetis teaches all the limitations in claim 1 above.
Vergetis does not explicitly recite: “wherein the user interface displays a thermometer chart for an area of interest”. However,
Goodgame in analogues evaluating pharmaceutical products using clinical trials teaches/suggests
- “wherein the user interface displays a thermometer chart for an area of interest” (Goodgame Fig.2 and ¶ [0043] 4th sentence: Patient thermometer bar chart D shows how many patients would be available in the patient population after applying the selected patient criteria).
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention,