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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 7/21/26 has been entered.
Notice to Applicant
The following is a Non-Final Office action. In response to Examiner’s Final Rejection of 5/4/26, Applicant, on 7/2/26, amended claims. Claims 1, 3-8, 10-15, and 17-20 are pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments are acknowledged.
Claim Objections
Claim 8 is objected to because of the following informalities: it now says “(Currently Amended)” in two locations (Line 1 and line 7). Appropriate correction is required. Examiner also suggests deleting the duplicated preamble from claim 1 erroneously copied into lines 7-8.
Claim 15 is objected to because of the following informalities: it now says “(Currently Amended)” in two locations (Line 1 and line 6). Appropriate correction is required. Examiner also suggests deleting the duplicated preamble from claim 1 erroneously copied into lines 6-7.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “decipher engine” in the 4th limitation of claim 1.
Examiner interprets the corresponding structure to be based on [0023] as published where it states “ These computer readable program instructions are stored in various types of computer readable storage media, such as cache 121 and the other storage media discussed below. The program instructions, and associated data, are accessed by processor set 110 to control and direct performance of the inventive methods. In computing environment 100, at least some of the instructions for performing the inventive methods may be stored in insight discovery code 150 in persistent storage 113” and [0049] as published “To uncover the configuration that conduct to the observed pattern, insight discovery program 150 may utilize a decipher engine (not shown) to eliminate data points whose relationship with the intersect trajectory would break the observed pattern.”
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 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, 3-8, 10-15, and 17-20 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 "a computer-based method" in the preamble; “a computer” in the 2nd limitation; and “a computer” in the 3rd limitation; and “a decipher engine”, which under “Claim Interpretation” section above and FIG. 1 and [0023, 0049] as published, has corresponding structure of a computer. There is insufficient antecedent basis for the limitations in the claim. It is unclear if there are one, two, or three, or four computers involved. For purposes of applying prior art only, Examiner interprets claim 1 as reciting: “computer-based method" in the preamble; “[[a]] the computer” in the 2nd limitation; and “[[a]] the computer” in the 3rd limitation, and “a decipher engine stored in memory and executed by the computer” in the 4th limitation.
Claim 1 recites the limitation “determining cluster configuration to uncover an observed pattern by using a decipher engine to eliminate data points whose relationship with an intersect trajectory breaks the observed pattern; scanning any clustering matrix representation relating to uncovered cluster configuration along a row of binary vectors that contain a data point for the intersect trajectory and summing a plurality of columns of those rows, creating a histogram of data points versus a number of clusters in which it appeared.” However, later in the claim, it recites “identifying data point intersections within the generated weighted binary matrix representations” and ends with “executing automatic discovery across multiple statistical domains using data point intersections generated using the generated weighted binary matrix representation.” There is insufficient antecedent basis for “an intersect”; “intersect trajectory”; and “data point intersections” as it is unclear how many different intersections are being referred to –later in the claim (last limitation on 1st page of claim) is “generating weighted binary matrix representations of the generated clusters”. It is unclear what exactly is happening here, as the support for the new limitation is [0049] as published, which is from FIG. 2, which shows that the intersections eliminated are from the “identified intersections” in step 208. The claim though does not “identify intersections” until 3 steps after. This appears to mean that the limitations added [earliest recitations of “intersections”] is referring to the same intersection in end of the claim. Examiner is not sure what to suggest exactly, but suggests using consistent language.
Independent claims 8, 15 recite similar limitations and are rejected for the same reasons.
Claims 3-7, 10-14, and 17-20 depend from claims 1, 8, and 15 and are rejected for the same reasons.
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, 3-8, 10-15, and 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without reciting significantly more.
Step One - First, pursuant to step 1 in MPEP 2106.03, the claim 1 is directed to a method which is a statutory category.
Step 2A, Prong One - MPEP 2106.04 - The claim 1 recites–
“A … method of performing automatic insight discovery across multiple statistical or machine learning domains, the method comprising:
detecting a set of data point key performance indicators (KPIs) from one or more statistical or machine learning domain spaces;
integrating an Enterprise Performance Management (EPM) … to access any data collected including data sourced from databases, spreadsheets, and external sources derived from various … domain spaces and updating the KPI;
generating… clusters including a series of binary vectors corresponding to the detected set of data point KPIs, wherein neighboring binary vectors having a mutual Mahalanobis distance below a threshold value are clustered together;
determining cluster configuration to uncover an observed pattern by using a decipher engine to eliminate data points whose relationship with an intersect trajectory breaks the observed pattern ();
scanning any clustering matrix representation relating to uncovered cluster configuration along a row of binary vectors that contain a data point for the intersect trajectory and summing a plurality of columns of those rows, creating a histogram of data points versus a number of clusters in which it appeared
configuring to adjust a cluster size by increasing a predetermined cutoff threshold value for Mahalanobis distance, thereby adjusting which data point KPIs would be close enough, or too far away
generating weighted binary matrix representations of the generated clusters; and
identifying data point intersections within the generated weighted binary matrix representations;
applying a set of clustering rules following simultaneous linear congruence formats;
executing … discovery across multiple statistical domains using data point intersections generated (([0015] the insights discovered as published states “For example, by considering a previous correlation insight between an exemplary KPI ‘X’ related to ‘Sales deals won during the current year’ and a second exemplary KPI ‘Y’ related to ‘Budget available during current year’ the user may want to understand if this KPI pair insight is dependent of another datapoint such as ‘Country’, ‘Market’, or ‘GEO’, etc.”).
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “Certain methods of organizing human activity – marketing or sales activities) and “mathematical relationships” as here we are performing a series of mathematical operations- first, detecting KPIs (e.g. “sales” in [0015] as published) from statistical domain spaces, accessing data collected from various sources and updating KPIs, generating clusters with vectors and calculated distances from a known mathematical algorithm (Mahalanobis), determining clusters and observed patterns to eliminate data points with an intersect trajectory that breaks the pattern, by then summing a plurality of columns of rows that have a datapoint for the intersect trajectory, and creating a histogram of data points versus a number of clusters in which it appeared (math); and adjusting distances (math) to form clusters, generating a weighted binary matrix of the generated clusters (math), identifying interactions within the binary matrix representations, applying cluster rules following simultaneous linear congruence formats (math), and executing discovery across statistical domains using math (the weighted binary matrix; rules of linear congruence; statistical domains). Accordingly, claim 1 is directed to an abstract idea because it is doing a series of mathematical calculations to make a recommendation/analysis related to KPI (sales).
Step 2A, Prong Two - MPEP 2106.04 - This judicial exception is not integrated into a practical application. In particular, the claim 1 recites additional elements that are:
A computer-based method…
integrating an Enterprise Performance Management (EPM) using a computer to access any data collected including data sourced from databases, spreadsheets, and external sources derived from various from one or more statistical or machine learning domain spaces (MPEP 2106.05f applies –the claim involves a computer, and is considered “apply it [the abstract idea] on a computer”; merely uses a computer as a tool to perform an abstract idea; even if alternative of “machine learning” is positively recited, still would be “apply it [abstract idea of mathematical relationships] on a computer”, notably, the claim starts out in the alternative – so “machine learning” is still not yet required throughout the claim);
integrating an Enterprise Performance Management (EPM) system to access any data collected including data sourced from databases, spreadsheets, and external sources derived from various machine learning domain spaces and updating the KPI;
generating, by the computer, clusters…
determining cluster configuration to uncover an observed pattern by using a decipher engine… (under “Claim Interpretation” section above and FIG. 1 and [0023, 0049] as published, has corresponding structure of a computer)
…
executing automatic discovery across multiple statistical domains using data point intersections generated using the weighted binary matrix representation (MPEP 2106.05f applies –the claim involves a computer, and is considered “apply it [the abstract idea] on a computer”; merely uses a computer (or 2, or 3, or 4 as stated in the 112b rejection) as a tool to perform an abstract idea and send and receive information; , See July 2024 Subject Matter Eligibility Update, Example 47, claim 2; Example 48, claim 1; the “machine learning model” here is “mere instructions to implement abstract idea on a computer at MPEP 2106.05f); see also MPEP 2106.05h “field of use” for combination of computer, system, and alternative of “machine learning” for accessing/retrieving data.
*Suggestion – The claim now reflect just a portion of [0012], [0049] (decipher engine… with intersect trajectory. [0012] as published states “the identify of each data point KPI in presently described embodiments is ciphered within the clustering pattern rather than by its association with a particular binary vector sequence. Deciphering the pattern allows the trajectory of an original data point KPI to be inferred with high confidence. Consequently, this learning results in significant reduction in KPI insight discovery time (down to the scale of hours rather than weeks when compared to conventional correlation analysis tools) for new data points. ***Suggestion – if Applicant positively recites “machine learning” tied to the two new limitations presented 7/2/26, it would then be likely that the claims reflect the technical improvement).
Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim also fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55. The claim is directed to an abstract idea.
Step 2B in MPEP 2106.05 - The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of a computing system (or 2 or 3 or 4 communicating), is treated as MPEP 2106.05(f) (Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235)) and “field of use” (MPEP 2106.05h). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
In addition, the claim recites “system to access any data collected” which is considered a conventional computer function (See MPEP 2106.05d - iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306; i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321).
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. The claim is not patent eligible. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Independent claim 8 is directed to a system at step 1, which is a statutory category. Claim 8 recites similar limitations as claim 1 and is rejected for the same reasons at step 2a, prong one, 2a, prong 2, and step 2b. The additional limitations, of processor, computer-readable memories for storing program instructions executed by processors, are all part of “apply it on a computer” (MPEP 2106.05f) at step 2a, prong 2 and step 2b. The claim is not patent eligible.
Independent claim 15 is directed to an article of manufacture at step 1, which is a statutory category. Specification [0019] as filed states “A computer readable storage medium, as that term is used in the present disclosure, is not to be construed as storage in the form of transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide, light pulses passing through a fiber optic cable, electrical signals communicated through a wire, and/or other transmission media.” Claim 15 recites similar limitations as claim 1 and claim 8 and is rejected for the same reasons at step 2a, prong one, 2a, prong 2, and step 2b. The additional limitations, of “computing system”, tangible storage medium, are part of “apply it on a computer” (MPEP 2106.05f) at step 2a, prong 2 and step 2b. The claim is not patent eligible.
Claim 3-5, 7, 10-12, 14, and 16-19 narrow the abstract idea by having various mathematical algorithms, operations, and relationships recited. Claim 3 – probabilistic data; claim 4 – Chinese Remainder Theorem with numbers that are coprime; claim 5 – histogram; summing columns.
Claim 6 – statistical domain space may be only alternative used as it recites “from the one or more statistical… domain spaces.” Claims 6, 13, and 20 recite an additional element of “stored within an accessible enterprise performance management system.” This is viewed as “apply it [abstract idea] on a computer” (MPEP 2106.05f) at step 2a, prong 2 and step 2B. It is also a conventional computer function at step 2B (MPEP 2106.05d(II) - iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334).
Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
For more information on 101 rejections, see MPEP 2106.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3, 5-8, 10, 12-15, 17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Konuri (US 2022/0236957), in view of Sheppard (US 20220036390) and Qian (CN112256964).
Concerning claim 1, Konuri discloses:
A computer-based method of performing automatic insight discovery across multiple statistical or machine learning domains (Konuri – see par 16 - The EPM system 26 may be configured to implement one or more multidimensional databases 32 for an enterprise, which may also be referred to as cubes, and may be configured to leverage the multidimensional databases 32 for planning, budgeting, forecasting, and reporting business performance of the enterprise; see par 117 - The program code embodied in any of the applications/modules described herein is capable of being individually or collectively distributed as a program product in a variety of different forms. In particular, the program code may be distributed using a computer readable storage medium having computer readable program instructions thereon for causing a processor to carry out aspects of the embodiments of the invention.), the method comprising:
detecting a set of data point key performance indicators (KPIs) from one or more statistical or machine learning domain spaces ([0013] as published - of Enterprise Performance Management (EPM) systems to help manage the organization's performance and make better-informed decisions through insight discovery. For example, an EPM may help an organization process data to identify areas for improvement, track progress against key performance indicators (KPIs), and make informed decisions about resource allocation, investments, or other strategic initiatives
(Konuri – see par 20 - When the target platform 14 is serving an enterprise, for example, such applications may be employed for the purposes of planning, budgeting, forecasting, and reporting business performance at different levels of granularity. For instance, an application may be developed that is configured, upon execution on the target platform 14, to generate and display a comprehensive view of net sales revenue of the enterprise that is broken down by one or more members of one or more dimensions of the multidimensional databases 32 (e.g., by product, entity, customer, sales channel, period);
integrating an Enterprise Performance Management (EPM) system (Konuri – see par 14 - the target platform 14 may include a master data management (MDM) server 20 configured to implement an MDM system 22 for an enterprise, and may include an enterprise performance management (EPM) server 24 configured to implement an EPM system 26 for an enterprise; see par 27 - The application builder server 16 may host an application creation engine 34 configured to generate the custom applications for querying and deriving target outputs from an enterprise's multidimensional databases 32. The application creation engine 34 may include a discovery module 36)
using a computer (Konuri par 117 - In particular, the program code may be distributed using a computer readable storage medium having computer readable program instructions thereon for causing a processor to carry out aspects of the embodiments of the invention.) to access any data collected including data sourced from databases, spreadsheets, and external sources derived from various machine learning domain spaces and updating the KPIs (Konuri see par 15 - The MDM system 22 may be communicatively coupled to these business units and applications, and may be configured to update the master data 30 as operations across the enterprise result in changes to the master data 30 to ensure the timeliness, accuracy and completeness of the master data 30; see par 16 - The EPM system 26 may be configured to implement one or more multidimensional databases 32 for an enterprise, which may also be referred to as cubes, and may be configured to leverage the multidimensional databases 32 for planning, budgeting, forecasting, and reporting business performance of the enterprise; FIG. 1, par 27 - The application builder server 16 may host an application creation engine 34 configured to generate the custom applications for querying and deriving target outputs from an enterprise's multidimensional databases 32. The application creation engine 34 may include a discovery module 36; The modules may be configured to cooperate with one another, with various databases, and various external systems, such as the user device 12, the MDM system 22, and the EPM system 26, to facilitate creation of the dynamic machine written applications;
see also Sheppard – see par 147 - The processor platform 1600 can be, for example, a server, a personal computer, a workstation, a self-learning machine (e.g., a neural network));
generating, by the computer (Konuri par 117 - In particular, the program code may be distributed using a computer readable storage medium having computer readable program instructions thereon for causing a processor to carry out aspects of the embodiments of the invention), clusters including a series of binary vectors corresponding to the detected set of data point KPIs, wherein neighboring binary vectors having a … distance below a threshold value are clustered together (Konuri par 72 - Referring to FIG. 3, for example, the WbL module 60 may be configured to use a binary weighting system in which the WbL module 60 assigns one influencer weight 86 value (e.g., one) to each influencer granularity definition 82 indicating a nonzero level of granularity, and assigns another influencer weight 86 value (e.g., zero) for each influencer granularity definition 82 indicating a null level of granularity (e.g., “none”). The WbL module 60 may also be configured to generate and store a weight index 61 (See FIG. 1) in the application definition database 58 that tracks the influencer granularity definitions 82 for which the former influencer weight 86 (FIG. 3) value is assigned. See par 73 - In this case, the WbL module 60 may be configured to generate entries in the weight index 61 that track the members or group of members corresponding to each assigned nonzero influencer weight 86 value;
see also Sheppard par 30 – metrics or data for purchased items, or any other information; par 44 - A typical Bloom filter array is a vector or array of bits that are initialized to 0 and then populated by flipping individual ones of the bits from 0 to 1 based on the allocation or assignment of users (or other data entries) in a database (e.g., the databases 124a-b of the database proprietors 106a-b of FIG. 1) to respective ones of the bits in the Bloom filter array. The users (or other data entries) in a database that are represented in the Bloom filter array are identified as corresponding to summary statistics of interest (e.g., users that were exposed to a particular media item); see par 48 - As represented in FIG. 3, the first email address 302 is allocated to the first element of the Bloom filter array 202 based on the first hash function 304, the eighth element of the Bloom filter array based on the second hash function 306, and the fourth element of the Bloom filter array 202 based on the third hash function. As such, the bit value of each of the first, fourth, and eighth elements in the Bloom filter array 202 are flipped from a 0 (as shown in FIG. 2) to a 1 (as shown in FIG. 3). see FIGS. 6-9, par 58 - Alternating between 0 and 1 based on modulo 2 addition for increasing values is equivalent to flipping the value between 0 and 1 at every assignment of the Bloom filter as demonstrated in connection with FIGS. 6-9. FIG. 6 illustrates an initial modulo 2 Bloom filter array 602 that has a vector length of 10 bits with all values being initialized to 0 in a similar manner to the traditional Bloom filter array 202 of FIG. 2).
Qian discloses:
generating, by the computer, clusters including a series of binary vectors corresponding to the detected set of data point KPIs, wherein neighboring binary vectors having a “mutual Mahalanobis distance” below a threshold value are clustered together (Qian page 2 Background – Because regional development is not balanced, different income groups have the use of sheet-surface problems and
so on, which will cause growth of potential customers. we need a potential
customer recommendation device, first needs to obtain potential customer data, analyzing the potential customer, obtaining the recommendation level of the potential customer, according to the recommendation level can be known to the potential customer for popularization of the product; see page 2, 5th paragraph- A financial institution potential customer recommendation method based on multi-dimensional data learning, comprising the following specific steps:… page 3 - step four, using calculating the Mahalanobis distance; obtaining the distance between any two samples x, y. wherein x, y represent two different samples, sigma represents the variance calculated in step three; step five: sequencing the Mahalanobis distance in ascending order. Step six: setting the threshold value of the sample to be removed by the formula threshold=a (m-ceil (m * 0.02)), wherein a is the minimum distance of the Mahalanobis distance calculated in step five; m represents the number of samples of each characteristic.)
Konuri, Sheppard, and Qian disclose:
determining cluster configuration to uncover an observed pattern by using a decipher engine to eliminate data points whose relationship with an intersect trajectory breaks the observed pattern (Konuri discloses the limitations, as best understood in light of 112 rejections, and based on broadest reasonable interpretation in light of the specification – see par 31 - The granularity data for each target output may also indicate a granularity level to provide for the target output relative to the other dimensions (e.g., generate the target output broken down by … time period) (disclosing trajectory); See par 38 - The period granularity configuration parameter may define the time granularity in which the enterprise plans their financials, such as by year, quarter, month, week, day, or a combination thereof. The fiscal calendar configuration parameter may indicate the first month of a calendar year for the enterprise, such as defined in the enterprise's EPM system 26. The decision module 38 may be configured to utilize these configuration parameters to set up the proper computation logic for rolling balances, inventories, setting up reporting periods, and so on ) (disclosing trajectory). see par 75 - For instance, if a user desires to calculate a target output 72 as a function of only the positive values of a given influencer 74 within the multidimensional databases 32, then the user may apply an exception to the influencer 74 that indicates, in connection with the target output 72, to determine whether the value of the influencer 74 for a given intersection of the granularity dimensions 80 is negative (where one of the dimensions is “period 80E”). If so, then the exception may indicate to set the value to zero for the purposes of calculating the target output 72;
Sheppard - see par 125 - The example Bloom filter array analyzer 1110 analyzes Bloom filter arrays obtained from the database proprietors 106a-b to estimate the cardinality or total number of unique users represented in individual ones of the Bloom filter arrays and/or across the union of multiple such Bloom filter arrays. Further, in some examples, the Bloom filter array analyzer 1110 estimates cardinalities for any Boolean combination of an intersection between different ones of the multiple Bloom filter arrays);
scanning any clustering matrix representation relating to uncovered cluster configuration along a row of binary vectors that contain a data point for the intersect trajectory and summing a plurality of columns of those rows, creating a histogram of data points versus a number of clusters in which it appeared (Applicant’s [0049] as published states “To uncover the configuration that conduct to the observed pattern, insight discovery program 150 may utilize a decipher engine (not shown) to eliminate data points whose relationship with the intersect trajectory would break the observed pattern. In embodiments, for example, this is achieved by scanning a given clustering matrix representation along the binary vectors (rows) that contain a data point intersect trajectory and summing the columns of those rows, creating a histogram of data points versus the number of clusters in which it appeared.”
Sheppard discloses limitation aside from “histogram” – see par 84 - Let the variable X represent the exclusive-or cardinality of the union of modulo 2 Bloom filter arrays (e.g., the total number of users in either the first Bloom filter array or the second Bloom filter array but not both arrays). see par 85 - These variables are referred to herein as the disjoint cardinalities for the union of two datasets because they form a collection of mutually exclusive and exhaustive sets across all possibilities that memberships may occur between the datasets and their users included therein. see par 92 - As with Equation 17, Equation 20 is true regardless of the number of hash functions used to allocate users to the respective Bloom filter arrays because the number of hash functions k is taken into account in Equation 9. Notably, in addition to estimate the total cardinality across all datasets, it is possible to estimate individual dataset intersections and, by extension, any Boolean operation of dataset memberships by using the duality of the inclusion-exclusion principle.
Qian discloses the entire limitation (including histogram) based on broadest reasonable interpretation in light of the specification – See page 8, last paragraph – obtain mass of enterprise data; Y represents the data set after centralized using calculating the Mahalanobis distance; obtaining the distance between any two samples x, y. wherein x, y represent two different samples, sigma represents the variance calculated; sequencing the Mahalanobis distance in ascending order; supposing the obtained result is [a, b, c, ...], wherein a Mahali distance after calculating the minimum distance; setting the threshold value of the sample to be removed by the formula threshold=a (m-ceil (m * 0.02)), wherein a is the minimum distance of the calculated Mahalanobis distance; see page 9 - Y represents the data after being centralized using the formula calculating the covariance matrix E; histogram algorithm, firstly discretizing the continuous floating point characteristic value into k integer, at the same time, constructing a histogram of width k. when the data is traversed, according to the discretized value as index in the histogram accumulation statistic, when traversing the data, histogram accumulates the needed statistic, then according to the discrete value of the histogram, traversing finding the optimal segmentation point; … finding the optimal segmentation in the category feature of 1 k-dimensional; before enumerating the segmentation point, firstly ordering the histogram according to the average value of each category; … respectively searching the optimal dividing point on different characteristic sets of different machines; … recommending enterprise according to the level prediction of each enterprise));
configuring to adjust a cluster size by increasing a predetermined cutoff threshold value for Mahalanobis distance, thereby adjusting which data point KPIs would be close enough, or too far away,… (Konuri – disclosing “grouping”, considering distances” – see FIG. 2 – target outputs 72 = “net sales revenue” (disclosing a KPI), See par 70 - the decision module 38 may be configured to automatically group the target outputs 72 into a plurality of mutually exclusive groups each including two or more of the target outputs 72 by applying a weighting algorithm to the application definition 42 that assigns influencer weights to each influencer 74 relative to the granularity dimensions 80 based on the influencer granularity definitions 82 for that influencer 74, assigns target output weights to each target output 72 relative to the granularity dimensions 80 that correspond to the influencer weights assigned to the influencers 74 for the target output 72, and identifies the target outputs 72 for each group based on the target output weights assigned to each target output 72. See par 99 - each granularity dimension 80 may include members within the multidimensional databases 32 that are organized into mutually exclusive generations of the granularity dimension 80 each corresponding to a different distance from a root node of the granularity dimension 80, and the application definition 42 may be discovered by generating a GUI 50 with fields for receiving identification of the target outputs 72, influencers 74, and granularity dimensions 80.)
Konuri discloses that computation logic related to multidimensional databases can include computation logic including “addition” (See par 31). Konuri also discloses looking at for “each possible combination of the members of the granularity dimensions” while avoiding querying and processing other data stored in multidimensional databases that is “not relevant” (See par 88).
Sheppard discloses, as best understood in light of 112 rejections:
configuring to adjust a cluster size by increasing a predetermined cutoff threshold value for Mahalanobis distance, thereby adjusting which data point KPIs would be close enough, or too far away, “by summing data points for the generated clusters” (Sheppard – see par 44 – One type of data structure that is useful to provide summary statistics (e.g., sketch data) in the context of tracking exposure to media is the Bloom filter array. A typical Bloom filter array is a vector or array of bits that are initialized to 0 and then populated by flipping individual ones of the bits from 0 to 1 based on the allocation or assignment of users (or other data entries) in a database (e.g., the databases 124a-b of the database proprietors 106a-b of FIG. 1) to respective ones of the bits in the Bloom filter array (disclosing clustering). The users (or other data entries) in a database that are represented in the Bloom filter array are identified as corresponding to summary statistics of interest (e.g., users that were exposed to a particular media item); see par 71 - As each element in the Bloom filter array is assigned either an even number of times (to end up with a value of 0) or an odd number of times (to end up with a value of 1), the sum of c.sub.E and c.sub.O equals the total number of elements in the Bloom filter array (e.g., c.sub.E+c.sub.O=m); see par 88 – “sum” equals cardinality for Bloom filter arrays being 1, 2, or 3);
generating weighted binary matrix representations of the generated clusters (Applicant’s [0040] as published states FIG. 4 is an example weighted binary matrix, where values are 0 or 1; [0046] as published – “binary entry of ‘1’;
Konuri discloses the limitations based on broadest reasonable interpretation in light of the specification – see par 72 - Referring to FIG. 3, for example, the WbL module 60 may be configured to use a binary weighting system in which the WbL module 60 assigns one influencer weight 86 value (e.g., one) to each influencer granularity definition 82 indicating a nonzero level of granularity, and assigns another influencer weight 86 value (e.g., zero) for each influencer granularity definition 82 indicating a null level of granularity (e.g., “none”). The WbL module 60 may also be configured to generate and store a weight index 61 (See FIG. 1) in the application definition database 58 that tracks the influencer granularity definitions 82 for which the former influencer weight 86 (FIG. 3) value is assigned. See par 73 - In this case, the WbL module 60 may be configured to generate entries in the weight index 61 that track the members or group of members corresponding to each assigned nonzero influencer weight 86 value.); and
identifying data point intersections within the generated weighted binary matrix representations (Konuri – also discloses interactions – see par 17, 7 - if a user desires to calculate a target output 72 as a function of only the positive values of a given influencer 74 within the multidimensional databases 32, then the user may apply an exception to the influencer 74 that indicates, in connection with the target output 72, to determine whether the value of the influencer 74 for a given intersection of the granularity dimensions 80 is negative. If so, then the exception may indicate to set the value to zero for the purposes of calculating the target output 72. Konuri discloses using the binary matrix representation as above - see par 72 - Referring to FIG. 3, for example, the WbL module 60 may be configured to use a binary weighting system in which the WbL module 60 assigns one influencer weight 86 value (e.g., one) to each influencer granularity definition 82 indicating a nonzero level of granularity, and assigns another influencer weight 86 value (e.g., zero) for each influencer granularity definition 82 indicating a null level of granularity (e.g., “none”). see par 84 - Each group may include two or more of the target outputs 72 calculated at a same or similar level of granularity, such as according to the weighted target output identifiers 92. For instance, the AMS module 62 may be configured to identify and group target outputs 72 having the same weighted target output identifiers 92;
see also Sheppard – par 89 - As bit-wise modulo 2 addition among a subset of the modulo 2 Bloom filter arrays is equivalent to the same modulo 2 procedure on the exclusive-or union of their respective set memberships, any individual one of the arrays or any two or more of the r arrays, up to all r arrays taken together, may be analyzed. These different combinations of the arrays provide 2.sup.r−1 estimates of the cardinality of various exclusive-or set unions).
Konuri discloses having different granularity definitions for groups of members from granularity dimensions (See par 73) and using weighted identifiers for querying data from multidimensional databases (See par 85).
Sheppard discloses:
applying a set of clustering rules following simultaneous linear congruence formats (Applicant’s example clustering rule in par 40-41
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Sheppard – see par 44 – One type of data structure that is useful to provide summary statistics (e.g., sketch data) in the context of tracking exposure to media is the Bloom filter array. A typical Bloom filter array is a vector or array of bits that are initialized to 0 and then populated by flipping individual ones of the bits from 0 to 1 based on the allocation or assignment of users (or other data entries) in a database (e.g., the databases 124a-b of the database proprietors 106a-b of FIG. 1) to respective ones of the bits in the Bloom filter array (disclosing clustering). The users (or other data entries) in a database that are represented in the Bloom filter array are identified as corresponding to summary statistics of interest (e.g., users that were exposed to a particular media item); See par 56 - The final value in a Bloom filter array after all data entries (e.g., users) have been assigned to respective elements in the Bloom filter array may be determined based on modulo 2 arithmetic. Stated generally, in mathematics “modulo d” is defined as the remainder after dividing an integer number by d. The possible output is any number between 0 and d−1. Two numbers are said to be congruent if they share the same remainder. This can be stated as a≡b (mod d). For example 17 and 27 are both congruent modulo 10 as they share the same remainder of 7 after dividing by 10, which is written as a congruence relation as 17≡27 (mod 10).
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Konuri, Sheppard, and Qian disclose:
executing automatic discovery across multiple statistical domains using data point intersections generated (Applicant’s [0014] as published states “Exemplary statistical or machine learning domains may include, for example, random graphs, random trees, causal trees, Markov chains, analysis of variance, and inferences, among many others. Consequently, as vast amounts of complex data from multiple domains becomes increasingly common, utilizing correlation tools to perform Insights as a Service Discovery (IaaSD) becomes a highly complex combinatorial multiobjective optimization problem.”
Konuri – see par 20 - an application may be developed that is configured, upon execution on the target platform 14, to generate and display a comprehensive view of net sales revenue of the enterprise that is broken down by one or more members of one or more dimensions of the multidimensional databases 32 (e.g., by product, entity, customer, sales channel, period, and so on). see par 90- The DbDe module 64 (See FIG. 1 – within Decision Module 38) may thus be configured to generate an equation for each target output 72 … to determine the granularity dimensions 80, members, and/or member groups applicable to each influencer 74 for the target output 72 by querying the weight index 61 with the nonzero influencer weights 86 of the weighted influencer identifier 90 assigned to the influencer 74, and generate code for each influencer 74 for the target output 72 that indicates an intersection of the influencer 74 with the members of the granularity dimensions 80 determined as applicable to the influencer 74. The DbDe module 64 may then be configured to combine the generated intersections based on the computation logic 76 for the target output ) using the generated weighted binary matrix representation (Konuri – see par 72 - Referring to FIG. 3, for example, the WbL module 60 may be configured to use a binary weighting system in which the WbL module 60 assigns one influencer weight 86 value (e.g., one) to each influencer granularity definition 82 indicating a nonzero level of granularity, and assigns another influencer weight 86 value (e.g., zero) for each influencer granularity definition 82 indicating a null level of granularity (e.g., “none”). see par 91 - For instance, referring to the example illustrated in FIG. 3, the AMS module 62 may have grouped the net sales revenue target output 72A and the third party licensing revenue target output 72B based on the same weighted target output identifier 92 being assigned to each of these target outputs 72; par 85 - responsive to assigning the weighted identifiers 90, 92 and grouping the target outputs 72, the AMS module 62 may pass control to the DbDe module 64, which may generally be configured to analyze the system generated data, coupled with information supplied by user input, and synthesize/create recommendations using the complex weighted schematic to provide improved accuracy across a range of predictive outputs).
Konuri, Sheppard, and Qian are analogous art as they are directed to analyzing clusters/classification of data (see Konuri Abstract, par 15, 20; See Sheppard par 44 – assignment of users to respective bits; par 56; par 65 – member or non-membership; Qian Abstract, page 3 – distance between samples of data). 1) Konuri discloses assigning values of 0 or 1 for grouping data (see par 72-73), and discloses analyzing target output and granularity for different time periods (See par 31, 38) along with intersections relative to granularity (See par 75). Sheppard improves upon Konuri by disclosing using a Bloom Filter array with bits 0 or 1 (see par 44, 48), allocating users using unions and intersections in Bloom filter arrays (See par 84, 85, 92), using a “sum” for assigning items (See par 71, 88), and a congruent modulo arithmetic in a linear formulation a = b (mod d) (See par 44, 56). One of ordinary skill in the art would be motivated to further include allocating users using a modulo arithmetic in a linear formulation and using “summing” and Bloom filter arrays to efficiently improve upon the grouping of clustering and intersections in Konuri. 2) Konuri discloses tracking members assigned to different values (See par 72-73). Bloom discloses using values to summarize statistics of interest (See par 44) and estimating union of arrays and users represented. Qian improves upon Konuri and Sheppard by disclosing using Mahalanobis distances between data sets, and a histogram accumulation statistics, to find optimal segmentations, to then aid in a recommendations for an enterprise (See pages 8-9). One of ordinary skill in the art would be motivated to further include analyzing histogram amounts from sets of data that have distance measures from each other to efficiently improve upon the groupings in Konuri and the allocations of belonging to different datasets using Bloom filter arrays in Sheppard.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify use a binary matrix in the clustering/classification as disclosed in Konuri, and to further use a modulo linear formulation for data entries and using “summing” as disclosed in Sheppard, and to further use histograms in making recommendations for enterprises as disclosed in Qian, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable and there is a reasonable expectation of success.
Concerning independent claim 8, Konuri and Sheppard and Qian disclose:
A computer system (Konuri – see par 16 - The EPM system 26 may be configured to implement one or more multidimensional databases 32 for an enterprise, which may also be referred to as cubes, and may be configured to leverage the multidimensional databases 32 for planning, budgeting, forecasting, and reporting business performance of the enterprise.), the computer system comprising:
one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more computer-readable tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, wherein the computer system is capable of performing a method comprising (Konuri see par 117 - The program code embodied in any of the applications/modules described herein is capable of being individually or collectively distributed as a program product in a variety of different forms. In particular, the program code may be distributed using a computer readable storage medium having computer readable program instructions thereon for causing a processor to carry out aspects of the embodiments of the invention.)
The remaining limitations are similar to claim 1 above.
Claim 8 is rejected for the same reasons.
It would be obvious to combine Konuri and Sheppard and Qian for the same reasons as claim 1.
Concerning independent claim 15, Konuri and Sheppard and Qian disclose:
A computer program product, the computer program product comprising, one or more computer-readable tangible storage medium and program instructions stored on at least one of the one or more computer-readable tangible storage medium, the program instructions executable by a processor capable of performing a method, the method comprising: (Konuri – see par 16 - The EPM system 26 may be configured to implement one or more multidimensional databases 32 for an enterprise, which may also be referred to as cubes, and may be configured to leverage the multidimensional databases 32 for planning, budgeting, forecasting, and reporting business performance of the enterprise; see par 117 - The program code embodied in any of the applications/modules described herein is capable of being individually or collectively distributed as a program product in a variety of different forms. In particular, the program code may be distributed using a computer readable storage medium having computer readable program instructions thereon for causing a processor to carry out aspects of the embodiments of the invention.)
The remaining limitations are similar to claim 1 above.
Claim 15 is rejected for the same reasons.
It would be obvious to combine Konuri and Sheppard and Qian for the same reasons as claim 1.
Concerning claims 3, 10, and 17, Konuri and Sheppard and Qian disclose:
The computer-based method of claim 1, wherein generating the weighted binary matrix representations of the generated clusters further comprises:
utilizing probabilistic data structures representing intra-positional sequence data point KPIs (Sheppard – See FIGS. 3-5, par 46 - That is, where the Bloom filter array 202 has a length of m (e.g., m=10 in the illustrated examples), the probability p.sub.i that a given input (e.g., a particular email address 302, 402, 502) is assigned to the ith element is p.sub.i=1/m.).
It would be obvious to combine Konuri and Sheppard and Qian for the same reasons as claim 1.
Concerning claims 5, 12, and 19, Sheppard discloses having a “sum” and count of elements (See par 71) and having a sum that equals cardinality for Bloom filter arrays being 1, 2, or 3 (See par 88).
The computer-based method of claim 1, wherein performing the insight discovery by identifying the data point intersections within the generated weighted binary matrix representations further comprises:
generating a histogram by summing columns within the generated weighted binary matrix representations of the generated clusters (Qian – See page 8, last paragraph – obtain mass of enterprise data; Y represents the data set after
centralized using calculating the Mahalanobis distance; obtaining the distance between any two samples x, y. wherein x, y represent two different samples, sigma represents the variance calculated; sequencing the Mahalanobis distance in ascending order; supposing the obtained result is [a, b, c, ...], wherein a Mahali
distance after calculating the minimum distance; setting the threshold value of the sample to be removed by the formula threshold=a (m-ceil (m * 0.02)), wherein a is the minimum distance of the calculated Mahalanobis distance; see page 9 - Y represents the data after being centralized using the formula calculating the
covariance matrix E; histogram algorithm, firstly discretizing the continuous floating point characteristic value into k integer, at the same time, constructing a histogram of width k. when the data is traversed, according to the discretized value as index in the histogram accumulation statistic, when traversing the data, histogram accumulates the needed statistic, then according to the discrete value of the histogram, traversing finding the optimal segmentation point; … finding the optimal segmentation in the category feature of 1 k-dimensional; before enumerating the segmentation point, firstly ordering the histogram according to the average value of each category; … respectively searching the optimal dividing point on different characteristic sets of different machines; … recommending enterprise according to the level prediction of each enterprise).
It would be obvious to combine Konuri and Sheppard and Qian for the same reasons as claim 1.
Concerning claims 6, 13, and 20, Konuri and Sheppard and Qian discloses:
The computer-based method of claim 1, wherein the detected set of data point key performance indicators from the one or more statistical or machine learning domain spaces are stored within an accessible enterprise performance management system (Konuri – see par 14 - The target platform 14 may include a master data management (MDM) server 20 configured to implement an MDM system 22 for an enterprise, and may include an enterprise performance management (EPM) server 24 configured to implement an EPM system 26 for an enterprise. In alternative examples, the MDM system 22 and EPM system 26 for an enterprise may be hosted on separate target platforms 14. see par 27 - The modules may be configured to cooperate with one another, with various databases, and various external systems, such as the user device 12, the MDM system 22, and the EPM system 26, to facilitate creation of the dynamic machine written applications. see par 43 - A user may access the discovery module 36 and submit a request to build a custom application for the target platform 14, or more particularly for the EPM system 26, for querying and processing data from one or more multidimensional databases 32 of the EPM system 26 via the user device 12 and the frontend portal 48.).
Concerning claims 7, 14, Konuri and Sheppard and Qian disclose:
The computer-based method of claim 1, wherein the threshold value is adjustable to alter the size of the generated clusters (Qian page 3 - step four, using calculating the Mahalanobis distance; obtaining the distance between any two samples x, y. wherein x, y represent two different samples, sigma represents the variance calculated in step three; step five: sequencing the Mahalanobis distance in ascending order. Step six: setting the threshold value of the sample to be removed by the formula threshold=a (m-ceil (m * 0.02)), wherein a is the minimum distance of the Mahalanobis distance calculated in step five; m represents the number of samples of each characteristic.)
It would be obvious to combine Konuri and Sheppard and Qian for the same reasons as claim 1.
Claims 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Konuri (US 2022/0236957), in view of Sheppard (US 20220036390) and Qian (CN112256964), as applied above to claims 1, 3, 5-8, 10, 12-15, 17, and 19-20, and further in view of Cuyt (US 20140195200).
Concerning claims 4, 11, and 18, Sheppard discloses having a “remainder” while assigning elements in the Bloom filter based on a modulo 2 arithmetic (See par 56). Sheppard also discloses having congruence (see par 56-57). However, Konuri, Sheppard, and Qian do not disclose that the named Theorem used is “Chinese Remainder Theorem.”
Cuyt discloses:
The computer-based method of claim 3, wherein generating the weighted binary matrix representations of the generated clusters further comprises:
maximizing efficiency of the generated cluster schemes by employing a Chinese Remainder Theorem such that each set of clustering intervals is coprime with respect to each other (Cuyt – see par 82 – data to be represented may for example be made to… mathematical data… textual data, etc; See par 84 – determining a subset of a family of functions of data; see par 97 – using technique based on Chinese remainder theorem, where subset elements, taking into account “prime” relationship of components; par 123 (col. 2) – roots of unity of relatively prime order… retrieved from an application for the Chinese remainder theorem).
Konuri, Sheppard, Qian, and Cuyt are analogous art as they are directed to analyzing clusters/classification of data (see Konuri Abstract, par 15, 20; See Sheppard par 44 – assignment of users to respective bits; par 56; par 65 – member or non-membership; Qian Abstract, page 3 – distance between samples of data; Cuyt par 84). Sheppard discloses having a “remainder” while assigning elements in the Bloom filter based on a modulo 2 arithmetic (See par 56). Sheppard also discloses having congruence (see par 56-57). Cuyt improves upon Konuri, Sheppard, and Qian by disclosing using the known and named Chinese Remainder Theorem that includes having prime aspects. One of ordinary skill in the art would be motivated to further include the named “Chinese Remainder Theorem” to efficiently improve upon the grouping of clustering in Konuri (see page 209, 213) and the assigning of elements based on a modulo arithmetic in a linear formulation that has a “remainder” and congruence as disclosed in Sheppard, and the histogram accumulations for analyzing the data groupings and customers in Qian.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the binary matrix in the clustering/classification as disclosed in Konuri, to further assign elements based on a modulo linear formulation for data entries as disclosed in Sheppard, to further use histograms in making recommendations for enterprises as disclosed in Qian, and to further employ the Chinese Remainder Theorem, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable and there is a reasonable expectation of success.
Response to Arguments
Applicant's arguments filed 7/2/26 have been fully considered but they are not persuasive and/or are moot in view of the new rejections.
With regards to 101, Applicant argues the claims are not directed to an abstract idea because it is “applying an amount of data beyond what may be comprehensible by a single person” and is “utilizing ever-changing user input to formulate an updated suggestion.” Remarks, page 12. In response, Examiner respectfully disagrees. First, it is unclear how the claims even require something “beyond” a single person; the identified abstract idea was not “mental evaluation” anyways – it was “Certain Methods of Organizing Human Activity” and Mathematical relationships. See also MPEP 2106.05(a)(I) “Accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016).” Second, having “changing” data alone does not make something eligible. Third, there are no “updated suggestions” in the claim, as argued in the Step 2A, Prong 1 argument at the end. Moreover, the claim begins with “insight” and ends in “discovery” but the insights at this time cover business insights, e.g. [0015] as published states “For example, by considering a previous correlation insight between an exemplary KPI ‘X’ related to ‘Sales deals won during the current year’ and a second exemplary KPI ‘Y’ related to ‘Budget available during current year’ the user may want to understand if this KPI pair insight is dependent of another datapoint such as ‘Country’, ‘Market’, or ‘GEO’, etc.”
Applicant argues the claims are a practical application under Step 2A, Prong Two, that the claims here are a “particular/specific” solution similar to McRO. Remarks, page 12-13. In response, Examiner respectfully disagrees. In response, Examiner respectfully disagrees. First, eligibility based on 101 is not simply whether any “specific” limitations are recited in the claim – it needs to be a particular solution to “improve a computer or other technology.” Rather, McRo, as explained in MPEP 2106.05(a)(II)(“Improvements to Any Other Technology of Technical Field”), states “The McRO court also noted that the claims at issue described a specific way (use of particular rules to set morph weights and transitions through phonemes) to solve the problem of producing accurate and realistic lip synchronization and facial expressions in animated characters.” In contrast, here, the claim is not directed to improving some computing technology, but rather is using sets of mathematical operations as best understood in light of 112 rejections, to detect KPIs. Examiner has made revised suggestions in the 101 rejection above – and repeats the suggestion here - in interview summary mailed 10/17/2025 regarding [0049-0050] as examples of connecting learning, ciphering, and deciphering, which Applicant can consider. As additional suggestion - The current set of claims fail to even require any learning relative to the deciphering, on top of 112 issues. Rather, in many limitations, it access “learning domains” in the alternative; and the one time it is required it is only accessing data “from various machine learning domains.” The claim now reflect just a portion of [0012], [0049] (decipher engine… with intersect trajectory. [0012] as published states “the identify of each data point KPI in presently described embodiments is ciphered within the clustering pattern rather than by its association with a particular binary vector sequence. Deciphering the pattern allows the trajectory of an original data point KPI to be inferred with high confidence. Consequently, this learning results in significant reduction in KPI insight discovery time (down to the scale of hours rather than weeks when compared to conventional correlation analysis tools) for new data points. ***Suggestion – if Applicant positively recites “machine learning” tied to the two new limitations presented 7/2/26, it would then be likely that the claims reflect the technical improvement).
With respect to step 2b, Applicant argues the claims here are eligible based on Bascom. Remarks, pages 14. In response, Examiner respectfully disagrees with the analysis. With regards to step 2B, only those additional elements (analyzed under 2B) that are deemed “conventional” need to comply with Berkheimer. When elements are just part of “apply it” [abstract idea] on a computer, under MPEP 2106.05(f); or “field of use” under MPEP 2106.05h, no evidence is needed. Moreover, parts of the claim do identify conventional computer functions in rejection above. Bascom is discussed in MPEP 2106.05 – “Adding a specific limitation other than what is well-understood, routine, conventional activity in the field, or adding unconventional steps that confine the claim to a particular useful application, e.g., a non-conventional and non-generic arrangement of various computer components for filtering Internet content, as discussed in BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341.” There is no similar situation here, just by having “a computer” and “possible machine learning” and “accessing machine learning domain” as explained in the revised 101.
With regards to 103, the Arguments are moot in view of the revised rejections necessitated by the amendments.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IVAN R GOLDBERG whose telephone number is (571)270-7949. The examiner can normally be reached 830AM - 430PM.
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/IVAN R GOLDBERG/Primary Examiner, Art Unit 3619
Prosecution Insights