DETAILED ACTION
This office action is responsive to the above identified application filed 3/1/2023. The application contains claims 1-20, all examined and rejected.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 09/25/2025 and3/1/2023. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim 1 is rejected under 35 USC 101 because the claimed inventions are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
While independent claims 1, 10 and 19 are each directed to a statutory category, it recites a series of steps which appears to be directed to an abstract idea (mental process, mathematical concept).
Claims 1-20 are rejected under 35 U.S.C. § 101 because the instant application is directed to non-patentable subject matter. Specifically, the claims are directed toward at least one judicial exception without reciting additional elements that amount to significantly more than the judicial exception. The rationale for this determination is in accordance with the guidelines of USPTO, applies to all statutory categories, and is explained in detail below.
When considering subject matter eligibility under 35 U.S.C. 101, (1) it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, (2a) it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so (2b), it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include certain methods of organizing human activities; a mental processes; and mathematical concepts, (2019 PEG)
STEP 1.
Per Step 1, the claims are determined to include machine, process, manufacture, as in independent Claim 1, 10, and 19, and in the therefrom dependent claims. Therefore, the claims are directed to a statutory eligibility category.
At step 2A, prong 1, The invention is directed to Mental Process and mathematical concept (see Alice), As such, the claims include an abstract idea. When considering the limitations individually and as a whole the limitations directed to the abstract idea are:
“calculate a parameter relating to a density of a prior distribution at each sample of a set of samples associated with the prior distribution” (Mental process, Mathematical concept);
“estimate, for the plurality of samples, at least one differential entropy of at least one posterior distribution associated with at least one observation based on the parameter relating to the density of the prior distribution at each sample and the likelihood of observation for each sample” (Mental process, Mathematical concept);
“select an action from a plurality of candidate actions each causing one or more observations” (Mental process, observation, evaluation and judgment).
The claim recites additional elements as
“An apparatus for implementing a computing system to predict preferences, comprising: at least one processor device operatively coupled to a memory” (“Using a computer as a tool to perform a mental process”, MPEP 2106.04(a)(2)(III)(C));
transmit, to at least one device associated with at least one person, at least one electronic interaction generated based on the action (insignificant extra-solution activity, MPEP 2106.05(g))
This judicial exception is not integrated into a practical application. The elements are recited at a high level of generality, i.e. a generic computing system performing generic functions including generic processing of data. Accordingly the additional elements do not integrate the abstract into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore the claims are directed to an abstract idea. (2019 Revised Patent Subject Matter Eligibility Guidance ("2019 PEG"). Thus, under Step 2A of the Mayo framework, the Examiner holds that the claims are directed to concepts identified as abstract.
STEP 2B.
Because the claims include one or more abstract ideas, the examiner now proceeds to Step 2B of the analysis, in which the examiner considers if the claims include individually or as an ordered combination limitations that are "significantly more" than the abstract idea itself. This includes analysis as to whether there is an improvement to either the "computer itself," "another technology," the "technical field," or significantly more than what is "well-understood, routine, or conventional" (WURC) in the related arts.
The instant application includes in Claim 1 additional steps to those deemed to be abstract idea(s).
When taken the steps individually, these steps are:
“An apparatus for implementing a computing system to predict preferences, comprising: at least one processor device operatively coupled to a memory” (“Using a computer as a tool to perform a mental process”, MPEP 2106.05(f)(2));
transmit, to at least one device associated with at least one person, at least one electronic interaction generated based on the action (Well-Understood, Routine, Conventional Activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i))
In the instant case, Claim 1 is directed to above mentioned abstract idea. Technical functions such as receiving, and extracting are common and basic functions in computer technology. The individual limitations are recited at a high level and do not provide any specific technology or techniques to perform the functions claimed.
In addition, when the claims are taken as a whole, as an ordered combination, the combination of steps does not add "significantly more" by virtue of considering the steps as a whole, as an ordered combination. The instant application, therefore, still appears only to implement the abstract idea to the particular technological environments using what is well-understood, routine, and conventional in the related arts. The steps are still a combination made to the abstract idea. The additional steps only add to those abstract ideas using well understood and conventional functions, and the claims do not show improved ways of, for example, an unconventional non-routine functions for analyzing model operations or updating the model that could then be pointed to as being "significantly more" than the abstract ideas themselves.
Moreover, Examiner was not able to identify any "unconventional" steps, which, when considered in the ordered combination with the other steps, could have transformed the nature of the abstract idea previously identified. The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is well-understood, routine, and conventional (WURC) in the related arts.
Further, note that the limitations, in the instant claims, are done by the generically
recited computing devices. The limitations are merely instructions to implement the abstract idea on a computing device that is recited in an abstract level and require no more than a generic computing devices to perform generic functions.
Claim 19 recites a system comprising “A computer program product for implementing a computer system to predict preferences, the computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform operations” configured to perform the same method as set forth in claim 1, the added element of “A computer program product for implementing a computer system to predict preferences, the computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform operations” do not transform the judicial exception into a practical application because they are tantamount to a mere instruction to apply the judicial exception to a generic computer. The additional elements are also not sufficient to amount to significantly more than the judicial exception because the action of implementing the method on a general purpose computer with at least one processor and at least one memory is tantamount to a mere instruction to apply the judicial exception to a computer.
Independent claims 10 and 19 are the same analogy and rejected using similar analysis as claim 1.
CONCLUSION
It is therefore determined that the instant application not only represents an abstract idea identified as such based on criteria defined by the Courts and on USPTO examination guidelines, but also lacks the capability to bring about "Improvements to another technology or technical field" (Alice), bring about "Improvements to the functioning of the computer itself" (Alice), "Apply the judicial exception with, or by use of, a particular machine" (Bilski), "Effect a transformation or reduction of a particular article to a different state or thing" (Diehr), "Add a specific limitation other than what is well-understood, routine and conventional in the field" (Mayo), "Add unconventional steps that confine the claim to a particular useful application" (Mayo), or contain "Other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment" (Alice), transformed a traditionally subjective process performed by humans into a mathematically automated process executed on computers (McRO), or limitations directed to improvements in computer related technology, including claims directed to software (Enfish).
The dependent claims, when considered individually and as a whole, likewise do not provide "significantly more" than the abstract idea for similar reasons as the independent claim.
claims 2 disclose “generate a plurality of samples from the prior distribution (Mental process); obtain, for each sample among the plurality of samples, a likelihood of an observation as an output of a likelihood function given the sample (Mental process, Mathematical concept); and eliminate samples from the plurality of samples having a likelihood less than a threshold value to generate the set of samples” (Mental process, Mathematical concept). Claim 2 does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 3 disclose “wherein the distance from each sample to at least one neighboring sample is a distance from each sample to a kth-nearest neighbor, k being a natural number” data description , which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claim 4 disclose “estimate the at least one differential entropy of the at least one posterior distribution by approximating a probability density function of the prior distribution at each sample using a volume of a sphere having a radius equal to the distance” (Mental process, Mathematical concept). Claim 4 does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 5 disclose “estimate the at least one differential entropy of the at least one posterior distribution having Euler’s constant as a constant term” (Mental process, Mathematical concept). Claim 5 does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 6 disclose “estimate the at least one differential entropy of each of a plurality of posterior distributions based on the at least one parameter relating to the density at each sample and a likelihood of transition for each sample from the prior distribution to each posterior distribution, and wherein each likelihood of transition exceeds a threshold likelihood.” (Mental process, Mathematical concept). Claim 6 does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 7 disclose “obtain the at least one observation from a model having an internal state estimated by the prior distribution” (Mental process, Mathematical concept). Claim 7 does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 8 disclose “the model is a behavioral model of at least one person” data description , which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claim 9 disclose the action from the plurality of candidate actions each causing one or more observations is selected based on expected values of the differential entropies estimated for all observations caused by the action (Mental process, Mathematical concept). Claim 9 does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea.
The dependent claims which impose additional limitations also fail to claim patent eligible subject matter because the limitations cannot be considered statutory. The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 1 ; where all claims are directed to the same abstract idea, "addressing each claim of the asserted patents [is] unnecessary." Content Extraction &. Transmission LLC v, Wells Fargo Bank, Natl Ass'n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter. Claims for the other statutory classes are similarly analyzed.
For at least these reasons, the claimed inventions of each of dependent claims 11-18, and 20, are directed or indirect to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more and are rejected under 35 USC 101.
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 USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The 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/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-5, 7-14, and 16-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-16 of U.S. Patent No. US 10,535,012 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because each of the instant claims (the claim being examined) is “generic to a species or sub-genus claimed in a conflicting patent or application, i.e., the entire scope of the reference claim falls within the scope of the examined claim.” See MPEP 804(II)(B)(1).
Instant Application
U.S. Patent No. US 10,535,012 B2 (reference patent)
Claim 1
An apparatus for implementing a computing system to predict preferences, comprising: at least one processor device operatively coupled to a memory and configured to:
calculate a parameter relating to a density of a prior distribution at each sample of a set of samples associated with the prior distribution; and
estimate, for the plurality of samples, at least one differential entropy of at least one posterior distribution associated with at least one observation based on the parameter relating to the density of the prior distribution at each sample and the likelihood of observation for each sample,
select an action from a plurality of candidate actions each causing one or more observations; and transmit, to at least one device associated with at least one person, at least one electronic interaction generated based on the action.
Claim 1
An apparatus to improve operation of a computing system for predicting personal preferences, comprising: a processor operatively coupled to a memory and configured to:
calculate at least one parameter relating to a density of the prior distribution at each sample in the subset, the at least one parameter including a distance from each sample to at least one neighboring sample;
estimate, for each sample in the subset, at least one differential entropy of at least one posterior distribution associated with at least one observation based on the at least one parameter relating to the density of the prior distribution at each sample and the likelihood of observation for each sample, the estimation being performed on samples in the subset; and
transmit, to at least one device associated with the at least one person, at least one electronic interaction generated based on an action, the action being selected based on expected values of the differential entropies estimated for all observations caused by the action.
Claim 2
The apparatus of claim 1,
wherein the at least one processor device is further configured to:
generate a plurality samples from the prior distribution;
obtain, for each sample among the plurality of samples, a likelihood of an observation as an output of a likelihood function given the sample; and
eliminate samples from the plurality of samples having a likelihood less than a threshold value to generate the set of samples.
Claim 1
An apparatus to improve operation of a computing system for predicting personal preferences, comprising: a processor operatively coupled to a memory and configured to:
generate a plurality of samples from a prior distribution, the prior distribution including a distribution of values representing at least one preference of at least one person;
obtain, for each sample among the plurality of samples, a likelihood of an observation as an output of a likelihood function given the sample;
eliminate samples from the plurality of samples having a likelihood of observation less than a threshold value to generate a subset of the plurality of samples;
calculate at least one parameter relating to a density of the prior distribution at each sample in the subset, the at least one parameter including a distance from each sample to at least one neighboring sample;
estimate, for each sample in the subset, at least one differential entropy of at least one posterior distribution associated with at least one observation based on the at least one parameter relating to the density of the prior distribution at each sample and the likelihood of observation for each sample, the estimation being performed on samples in the subset and without sampling the at least one posterior distribution to reduce consumption of resources of the computing system; and
transmit, to at least one device associated with the at least one person, at least one electronic interaction generated based on an action, the action being selected based on expected values of the differential entropies estimated for all observations caused by the action.
Claim 3
The apparatus of claim 1, wherein the distance from each sample to at least one neighboring sample is a distance from each sample to a kth-nearest neighbor, k being a natural number.
Claim 2
The apparatus of claim 1, wherein the processor is further configured to calculate a distance from each sample to a kth-nearest neighbor as the at least one parameter relating to the density at each sample, k being a natural number.
Claim 4
The apparatus of claim 1, wherein the at least one processor device is further configured to estimate the at least one differential entropy of the at least one posterior distribution by approximating a probability density function of the prior distribution at each sample using a volume of a sphere having a radius equal to the distance.
Claim 3
The apparatus of claim 1, wherein the processor is further configured to estimate the at least one differential entropy of the at least one posterior distribution by approximating a probability density function of the prior distribution at each sample using a volume of a sphere having a radius equal to the distance.
Claim 5
The apparatus of claim 1, wherein the at least one processor device is further configured to estimate the at least one differential entropy of the at least one posterior distribution having Euler's constant as a constant term.
Claim 4
The apparatus of claim 1, wherein the processor is further configured to estimate the at least one differential entropy of the at least one posterior distribution having Euler's constant as a constant term.
Claim 7
The apparatus of claim 1, wherein the at least one processor device is further configured to obtain the at least one observation from a model having an internal state estimated by the prior distribution.
Claim 8
The apparatus of claim 7, wherein the processor is further configured to obtain the observation from a model having an internal state estimated by the prior distribution.
Claim 8
The apparatus of claim 7, wherein the model is a behavioral model of at least one person.
Claim 9
The apparatus of claim 8, wherein the model is a behavioral model of the at least one person.
Claim 9
The apparatus of claim 1, wherein the action from the plurality of candidate actions each causing one or more observations is selected based on expected values of the differential entropies estimated for all the observations caused by the action
Claim 1
“...transmit, to at least one device associated with the at least one person, at least one electronic interaction generated based on an action, the action being selected based on expected values of the differential entropies estimated for all observations caused by the action.”
Claim 10
A computer-implemented method for implementing a computer system to predict preferences, comprising:
calculating a parameter relating to a density of a prior distribution at each sample of a set of samples associated with the prior distribution; and
estimating, for the plurality of samples, at least one differential entropy of at least one posterior distribution associated with at least one observation based on the parameter relating to the density of the prior distribution at each sample and the likelihood of observation for each sample
selecting an action from a plurality of candidate actions each causing one or more observations; and transmitting, to at least one device associated with at least one person, at least one electronic interaction generated based on the action..
Claim 13
A computer-implemented method for improving operation of a computing system for predicting personal preferences, comprising:
generating a plurality of samples from a prior distribution, the prior distribution including a distribution of values representing at least one preference of at least one person;
obtaining, for each sample among the plurality of samples, a likelihood of observation as an output of a likelihood function given the sample;
eliminating samples from the plurality of samples having a likelihood of observation less than a threshold value to generate a subset of the plurality of samples;
calculating at least one parameter relating to a density of the prior distribution at each sample in the subset, the at least one parameter including a distance from each sample to at least one neighboring sample;
estimating, for each sample in the subset, at least one-differential entropy of at least one posterior distribution associated with at least one observation based on the at least one parameter relating to the density of the prior distribution at each sample and the likelihood of observation for each sample, the estimation being performed on samples in the subset and without sampling the at least one posterior distribution to reduce consumption of resources of the computing system; and
transmitting, to at least one device associated with the at least one person, at least one electronic interaction generated based on an action, the action being selected based on expected values of the differential entropies estimated for all observations caused by the action.
Claim 11
The method of claim 10, further comprising: generating a plurality samples from the prior distribution; obtaining, for each sample among the plurality of samples, a likelihood of an observation as an output of a likelihood function given the sample; and eliminating samples from the plurality of samples having a likelihood less than a threshold value to generate the set of samples.
Claim 13
A computer-implemented method for improving operation of a computing system for predicting personal preferences, comprising:
generating a plurality of samples from a prior distribution, the prior distribution including a distribution of values representing at least one preference of at least one person;
obtaining, for each sample among the plurality of samples, a likelihood of observation as an output of a likelihood function given the sample;
eliminating samples from the plurality of samples having a likelihood of observation less than a threshold value to generate a subset of the plurality of samples;
calculating at least one parameter relating to a density of the prior distribution at each sample in the subset, the at least one parameter including a distance from each sample to at least one neighboring sample;
estimating, for each sample in the subset, at least one-differential entropy of at least one posterior distribution associated with at least one observation based on the at least one parameter relating to the density of the prior distribution at each sample and the likelihood of observation for each sample, the estimation being performed on samples in the subset and without sampling the at least one posterior distribution to reduce consumption of resources of the computing system; and
transmitting, to at least one device associated with the at least one person, at least one electronic interaction generated based on an action, the action being selected based on expected values of the differential entropies estimated for all observations caused by the action.
Claim 16
The method of claim 10, wherein the at least one processor device is further configured to obtain the at least one observation from a model having an internal state estimated by the prior distribution.
Claim 15
The method of claim 13, further comprising:
obtaining the observation from a behavior model of the person having an internal state estimated by the prior distribution question;
wherein the electronic interaction includes an electronic message for viewing on the at least one device.
Claim 19
A computer program product for implementing a computer system to predict preferences, the computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform operations comprising:
calculating a parameter relating to a density of a prior distribution at each sample of a set of samples associated with the prior distribution, the at least one parameter including a distance from each sample to at least one neighboring sample; and
estimating, for the plurality of samples, at least one differential entropy of at least one posterior distribution associated with at least one observation based on the parameter relating to the density of the prior distribution at each sample and the likelihood of observation for each sample, the estimation being performed without sampling the at least one posterior distribution to reduce consumption of resources of the computing system.
selecting an action from a plurality of candidate actions each causing one or more observations; and transmitting, to at least one device associated with at least one person, at least one electronic interaction generated based on the action.
Claim 16
A computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform operations for improving operation of a computing system for predicting personal preferences, the operations comprising:
generating a plurality of samples from a prior distribution, the prior distribution including a distribution of values representing at least one preference of a person;
obtaining, for each sample among the plurality of samples, a likelihood of observation as an output of a likelihood function given the sample;
eliminating samples from the plurality of samples having a likelihood of observation less than a threshold value to generate a subset of the plurality of samples;
calculating at least one parameter relating to a density of the prior distribution at each sample in the subset, the at least one parameter including a distance from each sample to at least one neighboring sample;
estimating, for each sample in the subset, at least one differential entropy of at least one posterior distribution associated with at least one observation based on the at least one parameter relating to the density of the prior distribution at each sample and the likelihood of observation for each sample, the estimation being performed on samples in the subset and without sampling the at least one posterior distribution to reduce consumption of resources of the computing system; and
transmitting, to at least one device associated with the at least one person, at least one electronic interaction generated based on an action, the action being selected based on expected values of the differential entropies estimated for all observations caused by the action.
Claim 20
The computer program product of claim 19, wherein the action from the plurality of candidate actions each causing one or more observations is selected based on expected values of the differential entropies estimated for all observations caused by the action.
Claim 16
A computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform operations for improving operation of a computing system for predicting personal preferences, the operations comprising:
transmitting, to at least one device associated with the at least one person, at least one electronic interaction generated based on an action, the action being selected based on expected values of the differential entropies estimated for all observations caused by the action.
As indicated in the table above, all the claimed features in instant claim 1 are disclosed in reference claim 1. While the two claims are not identical, instant claim 1 is anticipated by reference claim 1. It is evident from the table that all limitations in instant claim 1 are linguistically comparable to the underlined limitations in reference claim 1 except for the limitation “calculate a parameter relating to a density of a prior distribution at each sample of a set of samples associated with the prior distribution” in instant claim 1, for which explanation is provided below:
Reference claim 1 recites “calculate at least one parameter relating to a density of the prior distribution at each sample in the subset” wherein the “subset” refers to “samples from the plurality of samples having a likelihood of observation less than a threshold value” and the plurality of samples are associated with the prior distribution (see the “generate...” and “obtain...” limitations of reference claim 1). Therefore, reference claim 1 anticipates instant claim 1.
Instant claims 10 and 19 recite analogous limitations as claim 1, and are rejected based on similar rationale as stated above for claim 1 (instant claim 10 compared to reference claim 13; instant claim 19 compared to reference claim 16). Each of the instant dependent claims as noted above is rejected based on the same rationale as the claim from which it depends. Please see table for more information.
Regarding claim 12;
Claim 12 is similar in scope to claim 3; therefore it is rejected under similar rationale.
Regarding claim 13;
Claim 13 is similar in scope to claim 4; therefore it is rejected under similar rationale.
Regarding claim 14;
Claim 14 is similar in scope to claim 5; therefore it is rejected under similar rationale.
Regarding claim 17;
Claim 12 is similar in scope to claim 8; therefore it is rejected under similar rationale.
Regarding claim 18;
Claim 12 is similar in scope to claim 9; therefore it is rejected under similar rationale.
Claims 6 and 15 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 6 of U.S. Patent No. US 10,535,012 B2 in view of Pant et al. (“An information-theoretic approach to assess practical identifiability of parametric dynamical systems”).
Instant Application
U.S. Patent No. US 10,535,012 B2 (reference patent)
Claim 6
The apparatus of claim 1, wherein the at least one processor device is further configured to estimate the at least one differential entropy of each of a plurality of posterior distributions based on the at least one parameter relating to the density at each sample and a likelihood of transition for each sample from the prior distribution to each posterior distribution, and wherein each likelihood of transition exceeds a threshold likelihood.
Claim 6
The apparatus of claim 1, wherein the subset of the plurality of samples are samples having a likelihood of transition exceeding a threshold likelihood.
Claim 15
The method of claim 10, wherein the at least one differential entropy of each of a plurality of posterior distributions is estimated based on the at least one parameter relating to the density at each sample and a likelihood of transition for each sample from the prior distribution to each posterior distribution, and wherein each likelihood of transition exceeds a threshold likelihood.
Claim 6
The apparatus of claim 1, wherein the subset of the plurality of samples are samples having a likelihood of transition exceeding a threshold likelihood.
Regarding instant claim 6, reference claim 6 does not teach “wherein the at least one processor device is further configured to estimate the at least one differential entropy of each of a plurality of posterior distributions based on the at least one parameter relating to the density at each sample and a likelihood of transition for each sample from the prior distribution to each posterior distribution”. However, Pant et al. teaches this limitation in pg. 67 Section 2. One of ordinary skill in the art would modify reference claim 6 with the teachings of Pant et al. One of ordinary skill in the arts would have been motivated to make this modification in order to provide a framework for quantification of information gain measurements that is easily parallelisable which allows for efficient, scalable analysis of large datasets (Pant et al. pg. 66-67 Section 1). This is simply combining prior art elements according to known methods to yield predictable results, and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143). Instant claim 15 recites analogous limitations and is rejected based on the same rationale as instant claim 6.
Claim Rejections - 35 USC § 112
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 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "the likelihood" in line 8-9. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the likelihood” has been interpreted as “a likelihood”.
Claim 10 recites the limitation "the likelihood" in line 7. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the likelihood” has been interpreted as “a likelihood”.
Claim 19 recites the limitation "the likelihood" in line 10. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the likelihood” has been interpreted as “a likelihood”.
Dependent claims inherit the deficiency of the independent claims 1, 10, and 19.
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.
Claims 1, 3, 7-10, 12, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Balakrishnan et al. (US 2012/0143802 A1) in view of Pant et al. (“An information-theoretic approach to assess practical identifiability of parametric dynamical systems”) Disclosed in IDS submitted 9/25/2025.
Regarding Claim 1,
Balakrishnan et al. teaches an apparatus for implementing a computing system to predict preferences, comprising: at least one processor device operatively coupled to a memory and configured to (Fig. 5 teaches processor and memory; ¶31, teaches predicting user preferences),
select an action from a plurality of candidate actions each causing one or more observations (¶18, “recommender application 26 may then incorporate the response 40 into any of the latent factor models 28 and update the user's parameters 30. Using these updated user parameters, the recommender application 26 may then retrieve another pairwise question 38 from the database 36 of pairwise questions, and the user again provides the response 40. The recommender application 26 may continue posing a sequence of the pairwise questions 38, with each subsequent pairwise question picked using the updated (or current) user parameters, and soliciting the user's responses 40, for as long as the user is willing to provide feedback. An initial pairwise question 38 likely results in a rough estimate of the user's parameters 30. Each successive response 40, though, may be incorporated into the latent factor model 28 to recursively refine an estimate of the user's parameters”); and
transmit, to at least one device associated with at least one person, at least one electronic interaction generated based on the action (Fig. 1, ¶15, “FIG. 1 illustrates a client-server network architecture that recommends items to users. A server 20 communicates with a client device 22 via a communications network 24. The server 20 executes a recommender application 26 that recommends an item (such as movies, music, and other items) to a user of the client device 22”, ¶¶32-33, ¶42).
Balakrishnan et al. does not appear to explicitly teach calculate a parameter relating to a density of a prior distribution at each sample of a set of samples associated with the prior distribution; and estimate, for the plurality of samples, at least one differential entropy of at least one posterior distribution associated with at least one observation based on the parameter relating to the density of the prior distribution at each sample and the likelihood of observation for each sample.
However, Pant et al. teaches calculate a parameter relating to a density of a prior distribution at each sample of a set of samples associated with the prior distribution (pg. 71 Section 6.1:
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teaches calculating the distance between each sample to a kth-nearest neighbor relating to density; pg. 67 Section 2 Equation (2) teaches density of a prior distribution); and
estimate, for the plurality of samples, at least one differential entropy of at least one posterior distribution associated with at least one observation based on the parameter relating to the density of the prior distribution at each sample and the likelihood of observation for each sample, (pg. 67 Section 2:
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teaches estimating the differential entropy of the posterior distribution by updating the prior probability distribution to the posterior distribution using Bayes’ theorem and using the probability density function px(x), which corresponds to estimating the differential entropy of a posterior distribution associated with a parameter related to density; Equation (2) teaches that the likelihood of observation is used to calculated the posterior probability distribution and that the posterior distribution is not sampled in the calculation of differential entropy; instead, the prior distribution is sampled; the estimation of the differential entropy of the posterior distribution is performed on the subset of px|y(x|y)).
Balakrishnan et al. and Pant et al. are analogous art because they are directed to analysis related to posterior distributions.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate calculate a parameter relating to a density of a prior distribution at each sample of a set of samples associated with the prior distribution; estimate, for the plurality of samples, at least one differential entropy of at least one posterior distribution associated with at least one observation based on the parameter relating to the density of the prior distribution at each sample and the likelihood of observation for each sample as taught by Pant et al. to the disclosed invention of Balakrishnan et al.
One of ordinary skill in the arts would have been motivated to make this modification in order to provide a framework for quantification of information gain measurements that is easily parallelisable which allows for efficient, scalable analysis of large datasets (Pant et al. pg. 66-67 Section 1). This is simply combining prior art elements according to known methods to yield predictable results, and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
Regarding Claim 3,
Balakrishnan et al. in view of Pant et al. teaches the apparatus of claim 1.
Pant et al. further teaches wherein the distance from each sample to at least one neighboring sample is a distance from each sample to a kth-nearest neighbor, k being a natural number (pg. 71 Section 6.1:
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teaches calculating the distance between each sample to a kth-nearest neighbor relating to density; pg. 72 Section 7.1.1 teaches an example in which k is 10, a natural number).
Balakrishnan et al. and Pant et al. are analogous art because they are directed to analysis related to posterior distributions.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate wherein the distance from each sample to at least one neighboring sample is a distance from each sample to a kth-nearest neighbor, k being a natural number as taught by Pant et al. to the disclosed invention of Balakrishnan et al.
One of ordinary skill in the arts would have been motivated to make this modification in order to provide a framework for quantification of information gain measurements that is easily parallelisable which allows for efficient, scalable analysis of large datasets (Pant et al. pg. 66-67 Section 1). This is simply combining prior art elements according to known methods to yield predictable results, and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
Regarding Claim 7,
Balakrishnan et al. in view of Pant et al. teaches the apparatus of claim 1.
Balakrishnan et al. further teaches wherein the at least one processor device is further configured to obtain the at least one observation from a model having an internal state estimated by the prior distribution (Fig. 5 teaches processor and memory; ¶18, “In the movie domain, for example, the user may be asked whether she prefers the "The Godfather" or "Annie Hall." The user at the client device 22 provides a response 40, and the response 40 communicates back to the server 20. The recommender application 26 may then incorporate the response 40 into any of the latent factor models 28 and update the user's parameters 30. Using these updated user parameters, the recommender application 26 may then retrieve another pairwise question 38 from the database 36 of pairwise questions, and the user again provides the response 40” teaches the model incorporates user's feedback and update user parameters in order to select the next question to ask, thus rendering this process of updating to correspond to an internal state of the model; ¶22, ¶38 “Starting with a prior distribution for the user parameter vector, the IG criterion is used to find a pair of items and a feedback is sought for them. The pairwise response is combined with the prior distribution using Bayes rule, employing the likelihood given in Equation #5. This results in the posterior distribution for the user vector, which can be treated as the subsequent prior distribution for the next step of feedback in this sequential process” reasonably teaches that the process of updating user parameter based on feedback is estimated by combining prior distribution with the pairwise response using Bayes rule, thus reasonably corresponding to estimating an internal state of the model (the updating of parameters) by the prior distribution). The same motivation to combine for claim 1 equally applies for current claim.
Regarding Claim 8,
Balakrishnan et al. in view of Pant et al. teaches the apparatus of claim 7.
Balakrishnan et al. further teaches wherein the model is a behavioral model of at least one person (Fig. 5 teaches processor and memory; ¶16, ¶18, “In the movie domain, for example, the user may be asked whether she prefers the "The Godfather" or "Annie Hall." The user at the client device 22 provides a response 40, and the response 40 communicates back to the server 20. The recommender application 26 may then incorporate the response 40 into any of the latent factor models 28 and update the user's parameters 30. Using these updated user parameters, the recommender application 26 may then retrieve another pairwise question 38 from the database 36 of pairwise questions, and the user again provides the response 40” teaches the model incorporates user's feedback and update user parameters in order to select the next question to ask, thus rendering the model to be a model that analyzes the user’s behavior). The same motivation to combine for claim 1 equally applies for current claim
Regarding Claim 9,
Balakrishnan et al. in view of Pant et al. teaches the apparatus of claim 1, wherein the action from the plurality of candidate actions each causing one or more observations (Balakrishnan, ¶18, “retrieve another pairwise question … the user again provides the response … continue posing a sequence of the pairwise questions 38, with each subsequent pairwise question picked”, ¶28, “when choosing a pair of items for feedback, the pair of items that allows the recommender application 26 to rapidly learn the user's parameter vector is particularly useful. Other considerations of a feedback pair are that the pair of items should be interesting or intriguing to the user and that the pair be chosen adaptively”, ¶37, “ obtain a filtered list of candidate items. Exemplary embodiments may then evaluate the IG criterion on all pairs of these candidate items”) is selected based on expected values of the differential entropies (Balakrishnan, ¶29, “employ an information gain-based criterion. This criterion approximates the expected change in entropy or information gain between the user parameter distribution before and after receiving feedback, for any pair of items. This reduces the task of choosing a feedback pair to simply finding a pair that maximizes the information gain”, ¶30, “the (differential) entropy h for a k-dimensional MVN distribution with covariance matrix S, is given by …”, ¶31, “ the choice of the next pair of items (l, r) to get feedback for would be ones that maximize IGlr”) estimated for all observations caused by the action (Balakrishnan, ¶29, “expected change in entropy or information gain between the user parameter distribution before and after receiving feedback”, ¶30, “given any one pair of items l and r, its pair parameters θlr and pairwise binary response yl>r, the log-likelihood of the user parameters 30 can be obtained using Equation #5 … Using a local quadratic approximation of the expected likelihood …”, ¶¶38-39, “Starting with a prior distribution for the user parameter vector, the IG criterion is used to find a pair of items and a feedback is sought for them. The pairwise response is combined with the prior distribution using Bayes rule, employing the likelihood given in Equation #5. This results in the posterior distribution“, system selects the action using an information gain criterion that approximates the expected change in differential entropy or the user parameter distribution before and after receiving the user’s feedback (¶¶29-31). Because the expectation is taken over the possible outcomes of that action, and those outcomes are explicitly binary, this is selecting an action based on expected entropy over all observations caused by the action). The same motivation to combine for claim 1 equally applies for current claim
Regarding Claim 10,
Claim 10 recites analogous limitations to claim 1, therefore claim 10 is rejected based on the same rationale as claim 1.
Balakrishnan et al. teaches A computer-implemented method for implementing a computer system to predict preferences, comprising (Fig. 5 teaches processor and memory; ¶31, teaches predicting user preferences).
Regarding Claim 12,
Claim 12 recites analogous limitations to claim 3, therefore claim 12 is rejected based on the same rationale as claim 3.
Balakrishnan et al. teaches A computer-implemented method for implementing a computer system to predict preferences, comprising (Fig. 5 teaches processor and memory; ¶31, teaches predicting user preferences).
Regarding Claim 16,
Claim 16 recites analogous limitations to claim 7, therefore claim 16 is rejected based on the same rationale as claim 7.
Balakrishnan et al. teaches A computer-implemented method for implementing a computer system to predict preferences, comprising (Fig. 5 teaches processor and memory; ¶31 teaches predicting user preferences).
Regarding Claim 17,
Claim 17 recites analogous limitations to claim 8, therefore claim 17 is rejected based on the same rationale as claim 8.
Balakrishnan et al. teaches A computer-implemented method for implementing a computer system to predict preferences, comprising (Fig. 5 teaches processor and memory; ¶31, teaches predicting user preferences).
Regarding Claim 18,
Claim 18 recites analogous limitations to claim 9, therefore claim 18 is rejected based on the same rationale as claim 9.
Balakrishnan et al. teaches A computer-implemented method for implementing a computer system to predict preferences, comprising (Fig. 5 teaches processor and memory; ¶31 teaches predicting user preferences).
Regarding Claim 19,
Claim 19 recites analogous limitations to claim 1, therefore claim 19 is rejected based on the same rationale as claim 1.
Balakrishnan et al. teaches A computer program product for implementing a computer system to predict preferences, the computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform operations comprising (see Col. 15 lines 42-50; ¶31, teaches predicting user preferences).
Regarding Claim 20,
Claim 20 recites analogous limitations to claim 9, therefore claim 20 is rejected based on the same rationale as claim 9.
Balakrishnan et al. teaches A computer-implemented method for implementing a computer system to predict preferences, comprising (Fig. 5 teaches processor and memory; ¶31 teaches predicting user preferences).
Claims 2 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Balakrishnan et al. (US 2012/0143802 A1) in view of Pant et al. (“An information-theoretic approach to assess practical identifiability of parametric dynamical systems”) Disclosed in IDS submitted 9/25/2025 and further in view of “A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking” Published 2002 [hereinafter D1].
Regarding Claim 2,
Balakrishnan et al. in view of Pant et al. teaches the apparatus of claim 1.
Balakrishnan et al. further teaches wherein the at least one processor device is further configured to (Fig. 5 teaches processor and memory). The same motivation to combine for claim 1 equally applies for current claim
Balakrishnan et al. in view of Pant et al. does not appear to explicitly teach generate a plurality samples from the prior distribution; obtain, for each sample among the plurality of samples, a likelihood of an observation as an output of a likelihood function given the sample; and eliminate samples from the plurality of samples having a likelihood less than a threshold value to generate the set of samples.
However, D1 teaches generate a plurality samples from the prior distribution (P. 728, “choose the importance density to be the prior”
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, P. 780, Algorithm 4,
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);
obtain, for each sample among the plurality of samples, a likelihood of an observation as an output of a likelihood function given the sample (P. 728, “Substitution of (62) into (48) then yields … (63)”); P. 730, “For this particular choice of importance density, it is evident that the weights are given by
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”)
eliminate samples from the plurality of samples having a likelihood less than a threshold value to generate the set of samples (P. 728, “use resampling whenever a significant degeneracy is observed (i.e., when Ne / f falls below some threshold NT). The basic idea of resampling is to eliminate particles that have small weights and to concentrate on particles with large weights”
Balakrishnan et al., Pant et al., and D2 are analogous art because they are directed to the usage of likelihood function to produce posterior distributions.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate generate a plurality samples from the prior distribution; obtain, for each sample among the plurality of samples, a likelihood of an observation as an output of a likelihood function given the sample; and eliminate samples from the plurality of samples having a likelihood less than a threshold value to generate the set of samples as taught by Smallwood to the disclosed invention of Balakrishnan et al. in view of Pant et al.
One of ordinary skill in the arts would have been motivated to make this modification in order to use a recursive filtering approach so that received data can be processed sequentially rather than as a batch so that it is not necessary to store the complete data set nor to reprocess existing data if a new measurement becomes available which save resources and energy (D1, P. 723, Col. 2, ¶3).
Regarding Claim 11,
Claim 11 recites analogous limitations to claim 2, therefore claim 11 is rejected based on the same rationale as claim 2.
Balakrishnan et al. teaches A computer-implemented method for implementing a computer system to predict preferences, comprising (Fig. 5 teaches processor and memory; ¶31, teaches predicting user preferences).
Claims 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Balakrishnan et al. (US 2012/0143802 A1) in view of Pant et al. (“An information-theoretic approach to assess practical identifiability of parametric dynamical systems”) and further in view of Ajgl et al. (“Differential entropy estimation by particles”) disclosed in IDS.
Regarding Claim 4,
Balakrishnan et al. in view of Pant et al. teaches the apparatus of claim 1.
Balakrishnan et al. further teaches wherein the at least one processor device is further configured to (Fig. 5 teaches processor and memory).
Pant et al. further teaches estimate the at least one differential entropy of the at least one posterior distribution by approximating a probability density function of the prior distribution at each sample (pg. 67 Section 2:
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teaches estimating the differential entropy of the posterior distribution by updating the prior probability distribution to the posterior distribution using Bayes’ theorem and using the prior distribution’s probability density function px(x)).
Balakrishnan et al. and Pant et al. are analogous art because they are directed to analysis related to posterior distributions.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate w estimate the at least one differential entropy of the at least one posterior distribution by approximating a probability density function of the prior distribution at each sample as taught by Pant et al. to the disclosed invention of Balakrishnan et al.
One of ordinary skill in the arts would have been motivated to make this modification in order to provide a framework for quantification of information gain measurements that is easily parallelisable which allows for efficient, scalable analysis of large datasets (Pant et al. pg. 66-67 Section 1). This is simply combining prior art elements according to known methods to yield predictable results, and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
Balakrishnan et al. in view of Pant et al. does not appear to explicitly teach using a volume of a sphere having a radius equal to the distance.
However, Ajgl et al. teaches using a volume of a sphere having a radius equal to the distance (pg. 11993 ¶-1: “Another way is to use the volume of nx dimensional sphere with the radius
p
1
i
, i.e. the distance to the nearest neighbour” teaches a sphere with radius equal to the distance).
Balakrishnan et al., Pant et al., and Ajgl et al. are analogous art because they are directed to analysis related to distributions.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate using a volume of a sphere having a radius equal to the distance as taught by Ajgl et al. to the disclosed invention of Balakrishnan et al. in view of Pant et al.
One of ordinary skill in the arts would have been motivated to make this modification in order to provide nonparametric entropy estimator that can be applied in the fusion problem, while conventional running particle filter approaches lack such application (Ajgl et al. pg. 11992 ¶-2).
Regarding Claim 13,
Claim 13 recites analogous limitations to claim 4, therefore claim 13 is rejected based on the same rationale as claim 4.
Balakrishnan et al. teaches A computer-implemented method for implementing a computer system to predict preferences, comprising (Fig. 5 teaches processor and memory; ¶31, teaches predicting user preferences).
Claims 5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Balakrishnan et al. (US 2012/0143802 A1) in view of Pant et al. (“An information-theoretic approach to assess practical identifiability of parametric dynamical systems”) and further in view of Gupta et al. (“Parametric Bayesian Estimation of Differential Entropy and Relative Entropy”) disclosed in IDS.
Regarding Claim 5,
Balakrishnan et al. in view of Pant et al. teaches the apparatus of claim 1.
Balakrishnan et al. further teaches wherein the at least one processor device is further configured to (Fig. 5 teaches processor and memory). The same motivation to combine for claim 1 equally applies for current claim.
Balakrishnan et al. in view of Pant et al. does not appear to explicitly teach estimate the at least one differential entropy of the at least one posterior distribution having Euler's constant as a constant term.
However, Gupta et al. teaches estimate the at least one differential entropy of the at least one posterior distribution having Euler's constant as a constant term (pg. 826 ¶-1: “we estimate the differential entropy as: EN[h(N)], where the expectation is taken with respect to the posterior distribution over N” teaches that differential entropy is a characteristic of posterior distribution; pg. 821 ¶-3: “The special case that best validates the high-rate quantization assumptions is when the number of quantization cells is as large as possible, and they show that this special case produces the nearest-neighbor differential entropy estimator originally proposed by Kozachenko and Leonenko in 1987 [9]:
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where 𝛌 is the Euler-Mascheroni constant” teaches that an estimator for differential entropy (characteristic of posterior distribution) has a Euler-Mascheroni constant, which corresponds to Euler’s constant).
Balakrishnan et al., Pant et al., and Gupta et al. are analogous art because they are directed to analysis related to posterior distributions.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate estimate the at least one differential entropy of the at least one posterior distribution having Euler's constant as a constant term as taught by Gupta et al. to the disclosed invention of Balakrishnan et al. in view of Pant et al.
One of ordinary skill in the arts would have been motivated to make this modification in order to provide an approach for estimation of differential entropy and relative entropy that has significant performance improvement over other estimates approaches (Gupta et al. pg. 819 ¶-2).
Regarding Claim 14,
Claim 14 recites analogous limitations to claim 5, therefore claim 14 is rejected based on the same rationale as claim 5.
Balakrishnan et al. teaches A computer-implemented method for implementing a computer system to predict preferences, comprising (Fig. 5 teaches processor and memory; ¶31, teaches predicting user preferences).
Claims 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Balakrishnan et al. (US 2012/0143802 A1) in view of Pant et al. (“An information-theoretic approach to assess practical identifiability of parametric dynamical systems”) and further in view of Suzuki (US 2011/0060708 A1) Disclosed in IDS submitted 9/25/2025.
Regarding Claim 6,
Balakrishnan et al. in view of Pant et al. teaches the apparatus of claim 1.
Balakrishnan et al. further teaches wherein the at least one processor device is further configured to (Fig. 5 teaches processor and memory).
Pant et al. further teaches estimate the at least one differential entropy of each of a plurality of posterior distributions based on the at least one parameter relating to the density at each sample and a likelihood of transition for each sample from the prior distribution to each posterior distribution (pg. 67 Section 2:
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teaches estimating the differential entropy of the posterior distribution by updating the prior probability distribution to the posterior distribution using Bayes’ theorem and using the probability density function px(x) and the likelihood of py|x(y|x), which corresponds to estimating the differential entropy of a posterior distribution based on parameter related to density and likelihood of transition).
Balakrishnan et al. and Pant et al. are analogous art because they are directed to analysis related to posterior distributions.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate estimate the at least one differential entropy of each of a plurality of posterior distributions based on the at least one parameter relating to the density at each sample and a likelihood of transition for each sample from the prior distribution to each posterior distribution as taught by Pant et al. to the disclosed invention of Balakrishnan et al.
One of ordinary skill in the arts would have been motivated to make this modification in order to provide a framework for quantification of information gain measurements that is easily parallelisable which allows for efficient, scalable analysis of large datasets (Pant et al. pg. 66-67 Section 1). This is simply combining prior art elements according to known methods to yield predictable results, and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
Balakrishnan et al. in view of Pant et al. does not appear to explicitly teach wherein each likelihood of transition exceeds a threshold likelihood.
However, Suzuki teaches wherein each likelihood of transition exceeds a threshold likelihood (Fig. 31 S182: “Set a transition probability equal to or greater than a threshold (here, 0.01) to 0.9…” teaches a transition probability exceeding a threshold probability; Fig. 46 teaches the input data to the ACHMM includes samples).
Balakrishnan et al., Pant et al., and Suzuki are analogous art because they are directed to analysis related to posterior distributions.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate wherein each likelihood of transition exceeds a threshold likelihood as taught by Suzuki to the disclosed invention of Balakrishnan et al. in view of Pant et al.
One of ordinary skill in the arts would have been motivated to make this modification in order to provide a system that enables improvement to the posterior probability of the ACHMM (Suzuki pg. 44 [0944]).
Regarding Claim 15,
Claim 15 recites analogous limitations to claim 6, therefore claim 15 is rejected based on the same rationale as claim 6.
Balakrishnan et al. teaches A computer-implemented method for implementing a computer system to predict preferences, comprising (Fig. 5 teaches processor and memory; ¶31, teaches predicting user preferences).
Conclusion
The prior art made of record and not relied upon is considered pertinent to the applicant' s disclosure.
Osogami et al. “A hierarchical Bayesian choice model with visibility” that teach a hierarchical Bayesian choice model to estimate customer preference and visibility of items.
Examiner has pointed out particular references contained in the prior arts of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and Figures may apply as well. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior arts or disclosed by the examiner. It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED ABOU EL SEOUD whose telephone number is (303)297-4285. The examiner can normally be reached Monday-Thursday 9:00am-6:00pm MT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michelle Bechtold can be reached at (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MOHAMED ABOU EL SEOUD/Primary Examiner, Art Unit 2148