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 .
Claims 1-20 are pending and have been examined. Claims 1, 9, and 17 are independent.
This Application was published as U.S. 2026/0111457 A1.
Apparent priority: October 17, 2024.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on October 17, 2024 and June 25, 2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Drawings
The drawings are objected to because:
Figures 1, 2, 4, and 5 include a series of blocks that do not include any suitable legend for understanding the drawing. Accordingly, these drawings fail to convey the part of invention to which they are intended to pertain without referring to the Specification.
37 C.F.R. 1.84 Standards for drawings:
(o) Legends. Suitable descriptive legends may be used subject to approval by the Office, or may be required by the examiner where necessary for understanding of the drawing. They should contain as few words as possible.
To overcome the objection, include the amount of information that is considered "suitable descriptive legends" and a drawing should include.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as "amended." If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either "Replacement Sheet" or "New Sheet" pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
a. In paragraph [0053], "effeciwve" should apparently read "effective."
Appropriate correction is required.
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 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 17-20 are drawn to a "signal" per se as recited in the preamble and as such are non-statutory subject matter. On paragraph [0016] of the Specification, the term "computer readable storage medium" is not defined as to what the scope of the term is meant to encompass. Hence, one of ordinary skill in the art can interpret such term to include transitory signals and non-transitory signals. It does not appear that a claim reciting a signal encoded with functional descriptive material falls within any of the categories of patentable subject matter set forth in § 101. First, a claimed signal is clearly not a "process" under § 101 because it is not a series of steps. The other three § 101 classes of machine, compositions of matter and manufactures "relate to structural entities and can be grouped as 'product' claims in order to contrast them with process claims." 1 D. Chisum, Patents § 1.02 (1994). Claims 17-20 are therefore not directed to patent eligible subject matter.
The Applicant's Specification presents a broad definition as to what the "computer readable storage medium" covers and is being made to include transitory and non-transitory signals. The Applicant's Published Specification in paragraph [0016] refers to the "computer readable storage medium" but no description as to what the medium is meant to encompass is described. Hence, it appears that the claims appear to be drawn towards transitory signals, which is not subject matter eligible. In order to overcome the present rejection, the Applicant is advised to amend the claims by using the following terminology: "non-transitory computer readable medium." Such example terminology has been also found in the Official Gazette 1351 OG 212.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 (Statutory Category). Claims 1-8 are directed to a method (a process), and claims 9-16 are directed to a processing system (a machine). Claims 1-16 are therefore directed to a statutory category of invention. Claims 17-20 are drawn to a signal per se and are not directed to a statutory category, for the reasons set forth in the rejection of claims 17-20 under 35 U.S.C. 101 above. Claims 17-20 recite the judicial exception addressed below in addition to that defect.
Step 2A, Prong One (Recitation of a Judicial Exception). Claim 1 recites, in pertinent part, receiving knowledge from a subject matter expert, authenticating the knowledge based on structured decision-making information, generating an index that measures a sequence and efficiency of decisions and approaches taken towards solving a problem, and storing the authenticated knowledge with the index. Under their broadest reasonable interpretation, these limitations recite mental processes, that is, concepts performed in the human mind, including observation, evaluation, and judgment. A review board that receives an expert's recommendation, judges whether to accept it against structured decision criteria, scores how efficiently the expert's approach solved the problem, and records the accepted recommendation together with its score performs each of these steps mentally or with pen and paper. The specification describes the authentication and evaluation of captured knowledge in these terms (paragraphs [0048] and [0052]). The claim therefore recites an abstract idea.
Step 2A, Prong Two (Integration into a Practical Application). The judicial exception is not integrated into a practical application. Beyond the mental processes, claim 1 recites that the method is computer-implemented and that the response to a user query is generated using a trained machine learning model. The trained machine learning model is recited at a high level of generality and serves as a generic tool with which the mental evaluation scheme is applied (the specification describes generic implementations at paragraphs [0043] and [0075]-[0080]), and receiving the knowledge and storing the authenticated knowledge with the index are insignificant extra-solution activity in the nature of gathering and storing the data on which the evaluation operates. Considered individually and in combination, the additional elements amount to collecting information, analyzing it, and storing and outputting the results, generally linked to a computing environment. The claim does not recite any improvement to the functioning of a computer or to any other technology or technical field; the asserted advance of reducing reliance on non-validated knowledge (paragraphs [0036]-[0038]) is an improvement to the abstract knowledge-evaluation scheme itself. The claim is directed to the abstract idea.
Step 2B (Inventive Concept). Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the trained machine learning model and the storage of the knowledge and the index are recited generically and perform well-understood, routine, and conventional functions of receiving data, analyzing it, and storing and outputting results; the specification acknowledges conventional knowledge repositories (paragraphs [0045]-[0046]). Considered individually and as an ordered combination, the additional elements amount to no more than mere instructions to apply the mental evaluation scheme using generic components and therefore do not provide an inventive concept. Claim 1 is not patent eligible.
Independent claims 9 and 17. Claims 9 and 17 recite the operations of claim 1 in processing-system and computer program product form, respectively. Beyond the operations addressed above, claim 9 adds a memory comprising computer readable instructions and a processing device executing them, and claim 17 adds a computer readable storage medium with program instructions executable by at least one processor. These additional elements are generic computer components described at a high level of generality (paragraphs [0075]-[0080]) and do not integrate the abstract idea into a practical application or provide an inventive concept, for the reasons given for claim 1. Claims 9 and 17 are not patent eligible.
Dependent claims. The dependent claims do not cure the deficiency. Claims 2 and 10 further characterize the received knowledge as one of an opinion, an approach to the problem, or a recommendation, refining the content of the mental evaluation. Claims 3 and 11 specify that the knowledge is implicit knowledge and that the trained machine learning model generates the response using the authenticated knowledge and the efficiency index, further defining the mental content and restating the use of the generic model addressed above. Claims 4 and 12 recite that the efficiency index is one of a plurality of indices; claims 5 and 13 recite ranking the plurality of indices; claims 6 and 14 recite ranking based on a confidence of each index; and claim 18 recites those limitations in combination. Evaluating multiple candidate assessments, ranking them, and weighing them by confidence are concepts performed in the human mind. Claims 7, 15, and 19 recite that generating the efficiency index is performed using a random walk model, a mathematical model recited at a high level of generality and characterized by the specification as a known model class (paragraph [0053]). Claims 8, 16, and 20 recite calculating the efficiency index using the recited equation, a mathematical concept as such. None of the dependent claims adds an additional element beyond those addressed above; each further narrows the abstract idea or recites a mathematical concept, and none is patent eligible.
Claim Rejections - 35 USC § 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, 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.
Claims 1-7, 9-15, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Shrager (U.S. 2020/0411199), hereinafter Shrager, in view of Fagan (U.S. 2022/0391750), hereinafter Fagan.
Regarding claim 1, Shrager discloses:
1. A computer-implemented method for knowledge authentication for artificial intelligence-assisted decision making, comprising:
"The AI-based platform uses a combination of expert collective intelligence, AI, and machine learning to dynamically generate and test novel personalized treatment hypotheses." (Shrager, para. [0013].)
receiving knowledge from a subject matter expert;
"Virtual trials connect patients with expert physicians such as molecular tumor boards, and capture their treatment recommendations and rationales in a knowledge base, along with the patient's clinical results." (Shrager, para. [0011].)
"In some cases, the TRs described herein are the products of clinical assessment by domain experts (e.g., clinicians or scientists or groups of them such as molecular tumor boards), and can codify the justification for a selection of one or more treatment options applicable to the patient being treated, based upon some presumably relevant characterization of the state of the patient's disease." (Shrager, para. [0057].)
authenticating the knowledge based on structured decision-making information;
The recited authenticating is interpreted as validating. Under the broadest reasonable interpretation of the claim, authenticating the knowledge is establishing that the knowledge received from the subject matter expert has been validated.
"Likewise, the validated treatment rationales 104 are also stored in the knowledge base." (Shrager, para. [0087].)
The treatment rationale is the structured decision-making information of the claim, and validating that rationale before it is held in the knowledge base is the recited authenticating of the knowledge based on structured decision-making information.
storing authenticated knowledge and the efficiency index, wherein the authenticated knowledge represents know-how that can be distributed with a level of certainty about its usability; and
"Likewise, the validated treatment rationales 104 are also stored in the knowledge base." (Shrager, para. [0087].)
"In some instances, the case is published as a nano-publication such as in a nano journal within the knowledge base. The published case can include any of the case summary and/or treatment rationale in BCE, the vetting scores, and the data components or internal representations of the case." (Shrager, para. [0104].)
"In some cases, new information such as vetted TRs are "published" using a method of "nanopublication" that can offer linked data, API access, versioning, automatic re-distribution, provenance, and/or other desirable properties" (Shrager, para. [0074].)
"aggregating the insights, experiences and intuitions of the best clinicians, and continuously validating and refining them based on real-world outcomes data" (Shrager, para. [0016].)
The validated treatment rationale held in the knowledge base is the authenticated knowledge under the construction set out above, and retaining it in the knowledge base is the recited storing.
generating, using a trained machine learning model, a response to a user query using the authenticated knowledge and the efficiency index.
"The system uses AI and ML to retrieve recommendations made for similar patients in the past, and to rank their relevance for the current case." (Shrager, para. [0013].)
"A decision engine employing an algorithm such as a Bayesian classifier may then evaluate the cohort to identify one or more treatment options (which can have associated treatment rationales). The treatment options can be ranked or prioritized according to a statistical score." (Shrager, para. [0110]; see also Figs. 4, 8-9, 15, and 17.)
"Ranked treatment options can be generated for an entered clinical case such as one captured using the clinical case capture tool. Alternatively, ranked treatment options may be generated in response to entry or selection of specific parameters" (Shrager, para. [0106].)
"This information can be used to update or re-train the classifier(s) utilized by the decision engine to generate updated rankings or prioritizations of treatment options and/or associated treatment rationales." (Shrager, para. [0110].)
Shrager does not teach the efficiency-index aspects of the claim.
Fagan discloses the efficiency-index elements, mapped limitation by limitation:
generating an efficiency index for individual uses of the knowledge, wherein the efficiency index measures a sequence and efficiency of decisions and approaches taken towards solving a problem (Fagan measures the performance of a machine learning workflow at the level of its individual steps: "Pipeline performance metrics such as speed of execution, efficiency of each step and ability to optimize performance and evaluation metrics (measured across experiments or changes in configuration/pipeline structure)." (Fagan, para. [0060].) The workflow is a sequence of technique and configuration decisions made at each stage of solving the problem: "the vast array of available techniques and configuration options at each stage of the process can be very difficult to navigate. These challenges require, or benefit from, a large amount of expertise to make the right decisions" (Fagan, para. [0002].) Fagan further records the outcome each time a machine learning pipeline is built and updates a user reputation score from those outcomes, using "the same evaluation metrics that are used to evaluate the outcome produced by a pipeline" to determine that score, so that a user whose past decisions produced better results has "historical actions ... recommended above" those of a user whose decisions did not (Fagan, para. [0019].) The model so maintained "measures the ability of users ... to make optimal decisions in different circumstances" (Fagan, para. [0020].), and Fagan keeps "individual reputation scores for different stages" of the process as well as an overall score (Fagan, para. [0056].) The score generated from each individual build in this way is the recited efficiency index generated for individual uses of the knowledge, and the decisions and their outcomes at each stage of the workflow are the sequence and efficiency of decisions and approaches taken towards solving a problem..)
storing authenticated knowledge and the efficiency index, wherein the authenticated knowledge represents know-how that can be distributed with a level of certainty about its usability (the metrics of Fagan are generated and retained across runs for comparison: "measured across experiments or changes in configuration/pipeline structure" (Fagan, para. [0060].)
generating, using a trained machine learning model, a response to a user query using the authenticated knowledge and the efficiency index (the recommendations engine of Fagan applies the measured experience of prior users to guide each stage: "Recommendations engine 470 guides the machine learning workflow 400 by drawing from a pool of software-based recommenders that make recommendations about which techniques and configurations to use at each stage of the workflow, learned from the previous inputs and activities of human users of the system." (Fagan, para. [0042].)
Shrager and Fagan pertain to decision making and evaluation of decision-making processing. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shrager to generate, store, and apply its per-case scores as performance metrics measuring the efficiency of each step of the captured solution process in the manner of Fagan, that is, as an efficiency index measuring the sequence and efficiency of the decisions and approaches taken towards solving the problem. One of ordinary skill in the art would have been motivated to make this modification in order to improve the decision-making process by combining subject-matter expertise and domain knowledge with measured step-level performance, as Fagan expressly teaches: "A combination of subject-matter expertise and domain knowledge can improve the decision-making process, and as users develop their expertise, the ability of users to make optimal decisions improves." (Fagan, para. [0018].)
Regarding claim 2, Shrager discloses:
2. The computer-implemented method of claim 1, wherein the knowledge comprises at least one of an opinion, an approach to the problem, or a recommendation.
The knowledge captured in Shrager comprises recommendations and clinical opinions:
"Virtual trials connect patients with expert physicians such as molecular tumor boards, and capture their treatment recommendations and rationales in a knowledge base, along with the patient's clinical results." (Shrager, para. [0011].)
"In some cases, the TRs described herein are the products of clinical assessment by domain experts (e.g., clinicians or scientists or groups of them such as molecular tumor boards), and can codify the justification for a selection of one or more treatment options applicable to the patient being treated, based upon some presumably relevant characterization of the state of the patient's disease." (Shrager, para. [0057].)
The captured treatment recommendations are recommendations of the claim, and the clinical assessments codified in the treatment rationales are opinions of the claim.
Regarding claim 3, Shrager discloses:
3. The computer-implemented method of claim 1, wherein the knowledge is implicit knowledge.
The knowledge aggregated in Shrager is experiential, the insight and intuition of its practitioners:
"aggregating the insights, experiences and intuitions of the best clinicians, and continuously validating and refining them based on real-world outcomes data" (Shrager, para. [0016].)
The insights, experiences, and intuitions of clinicians are implicit knowledge, consistent with the instant specification, which describes implicit knowledge as experiential know-how (paragraph [0034]); the generation of the response using the authenticated knowledge is set forth in the claim 1 mapping above.
wherein the trained machine learning model generates the response using the authenticated knowledge and the efficiency index.
Shrager does not teach that the trained machine learning model generates the response using the efficiency index. Fagan teaches:
wherein the trained machine learning model generates the response using the authenticated knowledge and the efficiency index ("Recommendations engine 470 guides the machine learning workflow 400 by drawing from a pool of software-based recommenders that make recommendations about which techniques and configurations to use at each stage of the workflow, learned from the previous inputs and activities of human users of the system." (Fagan, para. [0042].) The recommendations engine of Fagan draws its recommendations from what was learned from the previous inputs and activities of users of the system.)
The rationale for the combination is similar to the one provided for claim 1.
Regarding claim 4, Shrager does not teach the recited plurality of efficiency indices. Fagan teaches:
4. The computer-implemented method of claim 1, wherein the efficiency index is one of a plurality of efficiency indices for solving the problem.
The per-step efficiency metrics of Fagan are measured repeatedly across experiments on the workflow, each measurement its own metric:
"Pipeline performance metrics such as speed of execution, efficiency of each step and ability to optimize performance and evaluation metrics (measured across experiments or changes in configuration/pipeline structure).” (Fagan, para. [0060].)
The efficiency metric of any one experiment is therefore one of a plurality of such metrics for solving the problem.
The rationale for the combination is similar to the one provided for claim 1.
Regarding claim 5, Shrager discloses:
5. The computer-implemented method of claim 4, further comprising ranking the plurality of efficiency indices for solving the problem.
The scored candidate options of Shrager are ranked by their scores:
"Ranked treatment options can be generated for an entered clinical case such as one captured using the clinical case capture tool." (Shrager, para. [0106].)
"The treatment options can be ranked or prioritized according to a statistical score." (Shrager, para. [0110].)
Shrager ranks its treatment options, not efficiency indices. Fagan teaches the recited ranking: the reputation scores are used "to rank recommended actions" (Fagan, para. [0020].), and those scores are the plurality of efficiency indices set forth for claim 4, so ranking them is ranking the plurality of efficiency indices for solving the problem.
The rationale for the combination is similar to the one provided for claim 1.
Regarding claim 6, Shrager discloses:
6. The computer-implemented method of claim 5, wherein the ranking is based at least in part on a confidence of each of the plurality of efficiency indices solving the problem.
The score attached to each candidate is a probability that the candidate solves the problem:
"such predictive treatment options may be assigned a predicted outcome score for each of one or more outcomes (e.g., 1 for 3 month survival, 0.8 for 5 year survival, 0.1 for 10 year survival, etc.)" (Shrager, para. [0094].)
"The treatment options can be ranked or prioritized according to a statistical score." (Shrager, para. [0110].)
Shrager does not teach that the ranking is based on a confidence of each index solving the problem. Fagan teaches it: where a domain expert judges one kind of error more important than another, "a weighted F-score may be appropriate to deliver an optimal result" (Fagan, para. [0019].), so the metric from which the reputation score is computed is weighted by the confidence that the measured result solves the problem at hand, and the ranking of claim 5 is therefore based at least in part on that confidence.
The rationale for the combination is similar to the one provided for claim 1.
Regarding claim 7, Shrager discloses:
7. The computer-implemented method of claim 1, wherein generating the efficiency index is performed using a random walk model.
Random walk methods are expressly among the models Shrager's platform employs:
"Bayesian statistics may use Markov chain Monte Carlo methods such as random walk Monte Carlo (e.g., reversible jump, slice sampling, Gibbs sampling, Metropolis-Hastings, and multiple-try metropolis), training-based Markov chain Monte Carlo, and hybrid Monte Carlo methods. In some cases, the decision engine utilizes Bayesian Monte Carlo Markov chain for searching and/or analyzing data in the knowledge base such as case summaries, treatment rationales" (Shrager, para. [0095].)
Shrager applies its Markov chain Monte Carlo methods to searching and analyzing the knowledge base, not to generating an efficiency index. Fagan teaches the recited generation: Monte Carlo tree search "simulat[es] the final outcome of randomly selected decisions and weight[s] each decision by its likelihood to produce an optimal outcome," and is applied where "workflows have a finite set of actions that can be taken at each step and a finite length" (Fagan, para. [0069].) Weighting each randomly selected decision along a workflow in this way is generating the index using a random walk model. This reading is consistent with the instant specification, which characterizes the random walk model class as known (paragraph [0053]). One of ordinary skill in the art would have been motivated to generate the metric with the random walk methods already employed by the decision engine of the platform.
The rationale for the combination is similar to the one provided for claim 1.
Claim 9 is a processing system claim with limitations corresponding to the limitations of Claim 1 and is rejected under similar rationale. Additionally:
Regarding claim 9, Shrager discloses:
9. ... a memory comprising computer readable instructions; and a processing device for executing the computer readable instructions, the computer readable instructions controlling the processing system to perform operations ... :
Shrager's platform runs on a digital processing device with a memory storing programs and a processor carrying out executable instructions:
"In some embodiments, the platforms, media, methods, and applications described herein include a digital processing device, a processor, or use of the same. In further embodiments, the digital processing device includes one or more hardware central processing units (CPU) that carry out the device's functions." (Shrager, para. [0125].)
"In some embodiments, the device includes a storage and/or memory device. The storage and/or memory device is one or more physical apparatuses used to store data or programs on a temporary or permanent basis." (Shrager, para. [0127].)
"In some embodiments, the digital processing device includes an operating system configured to perform executable instructions." (Shrager, para. [0126].)
The storage and/or memory device storing programs is the memory comprising computer readable instructions, and the digital processing device carrying out the executable instructions is the processing device of the claim.
Claim 10 is a processing system claim with limitations corresponding to the limitations of Claim 2 and is rejected under a similar rationale.
Claim 11 is a processing system claim with limitations corresponding to the limitations of Claim 3 and is rejected under a similar rationale.
Claim 12 is a processing system claim with limitations corresponding to the limitations of Claim 4 and is rejected under a similar rationale.
Claim 13 is a processing system claim with limitations corresponding to the limitations of Claim 5 and is rejected under a similar rationale.
Claim 14 is a processing system claim with limitations corresponding to the limitations of Claim 6 and is rejected under a similar rationale.
Claim 15 is a processing system claim with limitations corresponding to the limitations of Claim 7 and is rejected under a similar rationale.
Claim 17 is a computer program product claim with limitations corresponding to the limitations of method Claim 1 and is rejected under similar rationale. Additionally:
Regarding claim 17, Shrager discloses:
17. A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by at least one processor ... :
Shrager expressly discloses the storage medium bearing the executable program:
"In some embodiments, the platforms, media, methods, and applications described herein include one or more non-transitory computer readable storage media encoded with a program including instructions executable by the operating system of an optionally networked digital processing device." (Shrager, para. [0131].)
The non-transitory computer readable storage media encoded with a program including executable instructions are the computer readable storage medium having program instructions embodied therewith of the claim.
Claim 18 is a computer program product claim with limitations corresponding to the limitations of Claims 4, 5, and 6 in combination and is rejected under a similar rationale.
Claim 19 is a computer program product claim with limitations corresponding to the limitations of Claim 7 and is rejected under a similar rationale.
Claims 8, 16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Shrager (U.S. 2020/0411199) in view of Fagan (U.S. 2022/0391750), and further in view of Altschuler (U.S. 4,872,122), hereinafter Altschuler.
Regarding claim 8, Shrager and Fagan do not teach the recited equation. Altschuler teaches:
8. The computer-implemented method of claim 1, wherein the efficiency index is calculated using the following equation:
∑
0
m
∏
0
n
D
n
P
A
n
m
where m is a number of branches, n is a total number of nodes per branch, and PA represents a P index or A index value for a given n and m.
Altschuler computes a value for a decision structure by multiplying a per-node quantity along each path and summing the results across the paths that reach a common outcome. For each node, "all link probabilities leading from the present node to the root of the tree ... are multiplied", which is repeated for each leaf node, and "[t]he probabilities of the leaf nodes with the same action decision are added" (Altschuler, col. 11, l. 1 to col. 12, l. 3.) The resulting quantity is stated as PA = ΣPC(n), taken over the leaf nodes of the decision tree, where each PC(n) is the product of the per-node values along the branch reaching that leaf (Altschuler, col. 11, ll. 48-65.)
The branches of the decision structure are the recited m branches, the nodes along each branch are the recited n nodes per branch, and the per-node value that Altschuler multiplies along the branch is the recited per-node factor. Altschuler therefore teaches calculating an index as a summation over the branches of a product, taken over the nodes of each branch, of a value assigned to each node.
Altschuler differs from claim 8 in that the value Altschuler assigns to each node is a single link probability, where claim 8 recites the product of two per-node values, Dn and PAn. Expressing a per-node weight as the product of two component values was a known technique in the art at the time of the invention: decision structures of this kind are conventionally evaluated as a summation over root-to-leaf branches of a product of the values assigned along each branch, and the value assigned at a node is routinely formed by multiplying a probability by a second quantity attributed to that node. Altschuler itself forms its per-node link probability from a product of the probabilities of the significant variables used at that node (Altschuler, col. 11, ll. 33-46.) Applying that known technique to Altschuler's per-node value, so that the value is the product of two quantities attributed to the node rather than one, yields no more than the predictable result of a per-node weight reflecting two attributes of the node instead of one.
Shrager, Fagan, and Altschuler pertain to decision making and the evaluation of decision-making processing. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to calculate the efficiency index of the combination of Shrager and Fagan using the summation-of-products computation of Altschuler, with the per-node value formed from two quantities attributed to the node, in order to reduce the sequence of decisions recorded for an individual use of the knowledge to a single comparable value, which is what Fagan's reputation scores are used for when recommendations are ranked.
Claim 16 is a processing system claim with limitations corresponding to the limitations of Claim 8 and is rejected under a similar rationale.
Claim 20 is a computer program product claim with limitations corresponding to the limitations of Claim 8 and is rejected under a similar rationale. Claim 20 recites that n is a number of nodes per branch, where claims 8 and 16 recite a total number of nodes per branch; the mapping above is unchanged by that difference.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
a. Shear et al. (U.S. 10,509,672): a resource assertion environment in which expert-published assertions and quality-to-purpose evaluations are used to evaluate the appropriateness of computing resources for user purposes.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YUVAL H. LEVENTAL whose telephone number is (571) 270-3130. The examiner can normally be reached Monday-Friday, 8:00 AM - 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, PIERRE-LOUIS DESIR, can be reached at (571) 272-7799. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/YUVAL HAIM LEVENTAL/
Examiner, Art Unit 2659
/PIERRE LOUIS DESIR/Supervisory Patent Examiner, Art Unit 2659
