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 .
Status
This instant application No. 18/913,837 has claims 21-43 pending based on the preliminary amendment filed on July 24, 2025.
Priority
Applicant’s claims for priority of parent application No. 17/356,191 (now Pat. No. US 12147452), and provisional application No. 63/043,727 (filed on June 24, 2020) are acknowledged. The effective filing date for this instant application is June 24, 2020.
Drawings
The drawings filed on October 11, 2024 are acceptable for examination purposes.
Abstract
The abstract of the disclosure is acceptable for examination purposes.
Information Disclosure Statement
As required by M.P.E.P. 609(C), the Applicant’s submissions of the Information Disclosure Statements filed on 23 October 2024 and 18 March 2025 is acknowledged by the Examiner and the cited references have been considered in the examination of the claims. As required by M.P.E.P. 609 C(2), a copy of the PTOL-1449 initialed and dated by the Examiner is attached to the instant Office action.
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 claims at issue 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); and 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 a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this 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 §§ 706.02(l)(1) - 706.02(l)(3) 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/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 http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 21-43 are rejected on the ground of nonstatutory double patenting over claims 1-20 of Pat. No. US 12147452.
Claims 21-43 of the instant application recite similar limitations and claims 1-20 of ‘452 as being compared in the table below. For the purpose of illustration, only claims 21-30 (method claims) of the instant application are compared to the claims of the patent (underlining are used to indicate conflict limitations). The remaining claims of the instant application recite different categories (i.e., system and medium claims) and are therefore not compared for simplicity purposes.
Instant Application
Pat. No. US 12147452
Claim 21
A computer-implemented method, comprising:
computing a score indicative of novelty of observation data stored on one or more computing systems as a plurality of graph nodes corresponding to respective components of the observation data, the score computed by at least:
determining, for a graph node of the plurality of graph nodes, a number of alternative components corresponding to siblings of the graph node; an
determining an amount of relative information associated with one or more of the components of the observation data based, at least in part, on a conditional probability determined based, at least in part, on the determined number of alternate components; and
providing the score indicative of novelty to a client process.
Claim 11
A computer-implemented method, comprising: generating a graph data structure based, at least in part, on a stream of observations, wherein the graph data structure comprises at least a first plurality of nodes…
calculating a score indicative of a degree to which the observation is an outlier with respect to other observations, the score based, at least in part, on a probability computed based, at least in part, on the first number of times the first piece of categorical information has been observed and the second number of times the second piece of categorical information has been observed; and providing the score to a client process.
Claim 9
…wherein the score is adjusted based at least in part on relative information conveyed by a number of available alternative observations stored in the graph data structure.
See further Franceschini and Lamba below for mapping and motivation to combine with the claims of ‘452.
Claim 22
The computer-implemented method of claim 21, wherein one or more of the components of the observation data are stored as textual representations of numeric data, and wherein the score indicative of novelty is computed without converting the one or more of the components of the observation data to a non-textual representation.
See Franceschini below for mapping and motivation to combine with the claims of ‘452.
Claim 23
The computer-implemented method of claim 21, wherein one or more of the plurality of graph nodes comprises data indicative of a distribution of values.
See Franceschini below for mapping and motivation to combine with the claims of ‘452.
Claim 24
The computer-implemented method of claim 21, further comprising: generating an indication that variation of the score indicative of the novelty has fallen below a threshold amount.
See Franceschini below for mapping and motivation to combine with the claims of ‘452.
Claim 25
The computer-implemented method of claim 21, further comprising: computing the score indicative of novelty with respect to a time indicated by a query.
See Franceschini below for mapping and motivation to combine with the claims of ‘452.
Claim 26
The computer-implemented method of claim 21, wherein the score indicative of novelty is computed based, at least in part, on routing of observations using a fuzzy matching function.
See Franceschini below for mapping and motivation to combine with the claims of ‘452.
Claim 27
The computer-implemented method of claim 21, wherein a node of the plurality of graph nodes comprises a continuing counter and a termination counter, and wherein the method further comprises: incrementing the continuing counter when an observation traverses the node and the node does not represent a final node in the observation; and incrementing the termination counter when the node represents the final node in the observation.
See Murthy below for mapping and motivation to combine with the claims of ‘452.
Claim 28
The computer-implemented method of claim 21, further comprising computing the score indicative of novelty based, at least in part, on read-only observation data.
See Tellis below for mapping and motivation to combine with the claims of ‘452.
Claim 29
The computer-implemented method of claim 21, wherein the score indicative of novelty is computed with respect to a moving window of observations.
See Franceschini below for mapping and motivation to combine with the claims of ‘452.
Claim 30
The computer-implemented method of claim 21, further comprising learning an order of two or more components of the observation.
See Franceschini below for mapping and motivation to combine with the claims of ‘452.
Although the conflicting claims are not identical, they are not patentably distinct from each other because they are substantially similar in scope and they use the similar limitations to produce the same end result of novelty detection within a graph structure.
It would have been obvious to a person with ordinary skills in the art at the time of the invention was made to modify the elements of claims 1-20 of ‘452 with any combination of the cited references below to arrive at the pending claims of the instant application for the purpose of discovering surprises associated with a query based on ranking concepts matching the query using a quantity of matching sub-concepts of each concept.
Further, it would have been obvious to a person with ordinary skills in the art at the time of the invention was made to modify or to omit the additional elements of claims 1-20 of ‘452 to arrive at the pending claims of the instant application because the person would have realized that the remaining element would perform the same functions as before. “Omission of element and its function in combination is obvious expedient if the remaining elements perform same functions as before.” See In re Karlson (CCPA) 136 USPQ 184, decide Jan 16, 1963, Appl. No. 6857, U.S. Court of Customs and Patent Appeals.
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 31-36 are rejected under 35 U.S.C. 101 because the claimed invention is directed to nonstatutory subject matters.
Regarding claim 31, a system with one or more processors are is being recited in the claim. However, the claimed one or more processors can be interpreted by a person of ordinary skills in the art as software modules that carry out the claimed functions (i.e., software modules processing computing functions). Furthermore, in accordance with Applicant’s specification ([0158] of instant specification), Applicant states the one or more processors can be referred to as…virtual processing unit which is implemented by software consisting of data structures and computer programs to impart functionality when employed as a computer component. As such, the claim is not limited to statutory subject matter and is therefore non-statutory.
Applicant is suggested to define the system to include one or more hardware processors to overcome the raised issue.
Claims 32-36 fail to resolve the deficiencies of claim 31 since they only further limit the scope of claim 31. Hence, claims 32-36 are also rejected under 35 U.S.C. 101.
The claimed invention in claims 21-30, and 37-43 are directed to a judicial exception (i.e., an abstract idea) without significantly more. Applicant is noted that even if the system(s) of claims 31-36 comprise(s) one or more hardware processors (i.e., passing step 1 of the abstract idea analysis), claims 31-36 would still be rejected under 35 U.S.C 101 for the following reasons:
Claims 21-30, and 37-43 pass step 1 of the 35 U.S.C. 101 analysis since each claim is either directed to a method, or non-transitory computer-readable storage medium.
Claims 21, and 37 recite in each claim, in part, elements that are directed to an abstract idea (“Courts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind.” Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015)). Each claim recites the limitations of computing a score indicative of novelty…by at least: determining…a number of alternative components…; and determining an amount of relative information associated with one or more of the components… The limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components (e.g., mental calculation of the score by mentally determining the number of alternative components and mentally determining the amount of relative information). That is, other than reciting generic components (e.g., processors, computing devices, and executable instructions)), nothing in the claim precludes the limitations from being performed in the human mind per step 2A – prong 1 of the Abstract Idea Analysis. Thus, the limitations are parts of a mental process. Further, the claims each recites an additional step of providing the score indicative of novelty to a client process which is an extra-solution activity of result delivering (per step 2A – prong 2 of the Abstract Idea Analysis) that cannot be integrated into a practical application (e.g., the elements recite trivial elements that occurred or would occur after the mental process).
Each of the additional limitation(s) is no more than mere instructions to apply the exception using a generic computer component (e.g., processor, memory, and computer-executable instructions). The extra-solution activity in step 2A - prong 2 are reevaluated in step 2B to determining if each limitation is more than what is well-understood, routine, conventional (WURC) activity in the field. The background of the limitations does not provide any indication that the computer components (e.g., processor, memory, and computer-executable instructions) are not off-the-shelf computer components. The Symantec, TLI, and OOP Techs court decisions cited in MPEP 2106.05(d)(II) indicate that mere receiving, generating, storing, determining, identifying, and transmitting of data over a network are a well-understood, routine, and conventional functions when claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the claims are well-understood, routine, conventional activity is supported under Berkheimer Option 2. For these reasons, there is no inventive concept in each claims, thus, the claims are ineligible.
Claims 22, and 38 recite in each claim additional elements of one or more of the components…are stored as textual representations…, and …the score…is computed without converting …to a non-textual representation which are extra-solution and WURC activities similar to the above analysis (e.g., writing down the components as texts on paper, and mentally computing the score without any data conversion). Thus, the claims are ineligible.
Claim 23 merely provides definition for one or more of the plurality of graph nodes. Thus, the claim is ineligible.
Claims 24, and 39 recite in each claim an additional element of generating an indication that…the score…has fallen below a threshold amount which can be implemented in a human mind and/or with the aid of pen/paper similar to the above analysis (e.g., marking on paper that the score is below a threshold). Thus, the claims are ineligible.
Claims 25, and 40 recite in each claim an additional element of computing the score…with respect to a time indicated by a query which can be implemented in a human mind and/or with the aid of pen/paper similar to the above analysis (e.g., mentally calculating the score with respect to a query time). Thus, the claims are ineligible.
Claims 26, and 41 recite in each claim an additional element of …the score…is computed based…on routing…using fuzzy matching function which can be implemented in a human mind and/or with the aid of pen/paper similar to the above analysis (e.g., mentally calculating the score using fuzzy matching function). Thus, the claims are ineligible.
Claim 27 further recites additional elements of incrementing the continuing counter…, and incrementing the termination counter… can be implemented in a human mind and/or with the aid of pen/paper similar to the above analysis (e.g., mentally incrementing values of the counters and/or writing the incremented values on paper). Thus, the claim is ineligible.
Claims 28, and 42 recite in each claim an additional element of computing the score… based…on read-only observation data which can be implemented in a human mind and/or with the aid of pen/paper similar to the above analysis (e.g., mentally computing the score based on read-only data). Thus, the claims are ineligible.
Claims 29, and 43 recite in each claim an additional element of computing the score… based…on moving window of observations which can be implemented in a human mind and/or with the aid of pen/paper similar to the above analysis (e.g., mentally computing the score based on window of observations). Thus, the claims are ineligible.
Claim 30 further recites in each claim an additional element of learning an order of two or more components… which can be implemented in a human mind and/or with the aid of pen/paper similar to the above analysis (e.g., mentally learning the order of the components). Thus, the claim is ineligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 21-24, 26, 29-33, 36-39, 41, and 43 are rejected under AIA 35 U.S.C. 103 as being unpatentable over Franceschini et al. (Pub. No. US 2017/0161619, published on June 8, 2017; hereinafter Franceschini) in view of Lamba et al. (Pub. No. US 2012/0209858, published on August 16, 2012; hereinafter Lamba).
Regarding claims 21, and 31, Franceschini clearly show and discloses a computer-implemented method (Abstract); and a system, comprising: one or more processors to at least implement the method (Figure 1) comprising:
computing a score indicative of novelty of observation data (identifying, for a concept selected by the user, one or more related concepts (e.g., surprise concepts that are not linked to the selected concept) so that the identified concept(s) can be displayed to promote understanding and interpretation of concept vector relationships, [0025]-[0026]. Towards this end, the vector processing application 14 proceeds through the sorted list of D concepts until a concept C′ is identified whose number of co-occurrences with C is less than W (a parameter, for example 0.01 of the number of times C appears in the sequence). Based on the computation results, the vector processing application 14 may be configured to present the user with the “surprise” concept C′, [0056]) stored on one or more computing systems as a plurality of graph nodes corresponding to respective components of the observation data (The concept sequence identifier 12 may be configured to derive concept sequences (e.g., 12A) from one or more concept graphs 18 having nodes which represent concepts (e.g., Wikipedia concepts). As will be appreciated, a graph 18 may be constructed by any desired method (e.g., Google, etc.) to define “concept” nodes which may be tagged with weights indicating their relative importance. In addition, an edge of the graph is labeled with the strength of the connection between the concept nodes it connects, [0038]-[0041]), the score computed by at least:
determining, for a graph node of the plurality of graph nodes, a number of alternative components (a vector processing application 14 may be configured to identify “surprise” concepts that are strongly related though seldom mentioned together. For example, after a user explores a plurality of concepts (e.g., Wikipedia concepts), the user may request the user's browser to identify “surprise” concepts. In response, the vector processing application 14 may process the extracted concept vectors 13A to calculate a sorted list of D concepts (e.g., D=40) that are most strongly connected to a specified concept C, [0056]-[0057]); and
determining an amount of relative information associated with one or more of the components of the observation data based, at least in part, on a conditional probability determined based, at least in part, on the determined number of alternate components (the vector processing application 14 may process the extracted concept vectors 13A to calculate a sorted list of D concepts (e.g., D=40) that are most strongly connected to a specified concept C. The connection may be determined by identifying the D concepts having a high cosine distance to C that exceeds a specified threshold. In addition, the vector processing application 14 may then identify a concept C′ that is high on this list of D concepts, but yet there are a few co-occurrences of C and C′ in the sequence of concepts within a window of U concepts, [0056]-[0058]); and
providing the score indicative of novelty to a client process (Towards this end, the vector processing application 14 proceeds through the sorted list of D concepts until a concept C′ is identified whose number of co-occurrences with C is less than W (a parameter, for example 0.01 of the number of times C appears in the sequence). Based on the computation results, the vector processing application 14 may be configured to present the user with the “surprise” concept C′, [0056]. Causing surprise promotes content that is neither too close nor too far, this may be represented by a range [a,b] of cosine distances where a and b are parameters, from the user model, where the means of measuring proximity or distance is through the cosine distance between the respective vectors, [0058]).
Lamba then discloses the number of alternative components corresponding to siblings of the graph node (A relation can be determined between a summation of the affinity scores of the children under a parent, and the number of paths that can be taken from the parent to its children and used as a measure of how well the query matched that parent concept. A score determined in such a manner is referred to herein as a "density score." A density function is any function that relates node scores and some kind of volume, such as the number of children (or leaf level concepts), under a parent, [0093]).
It would have been obvious to an ordinary person skilled in the art at the time of the invention was effectively filed to incorporate the teachings of Lamba with the teachings of Franceschini for the purpose of discovering surprises associated with a query based on ranking concepts matching the query using a quantity of matching sub-concepts of each concept.
Regarding claim 37, Franceschini clearly shows and disclose a non-transitory computer-readable storage medium comprising instructions that, in response to execution by at least one processor of one or more computing devices (Figures 1-2), cause the one or more computing devices to at least:
store observation data as a plurality of graph nodes corresponding to respective components of the observation data (The concept sequence identifier 12 may be configured to derive concept sequences (e.g., 12A) from one or more concept graphs 18 having nodes which represent concepts (e.g., Wikipedia concepts). As will be appreciated, a graph 18 may be constructed by any desired method (e.g., Google, etc.) to define “concept” nodes which may be tagged with weights indicating their relative importance. In addition, an edge of the graph is labeled with the strength of the connection between the concept nodes it connects, [0038]-[0041]);
determine, for a graph node of the plurality of graph nodes, a number of alternative components (a vector processing application 14 may be configured to identify “surprise” concepts that are strongly related though seldom mentioned together. For example, after a user explores a plurality of concepts (e.g., Wikipedia concepts), the user may request the user's browser to identify “surprise” concepts. In response, the vector processing application 14 may process the extracted concept vectors 13A to calculate a sorted list of D concepts (e.g., D=40) that are most strongly connected to a specified concept C, [0056]-[0057]);
determine an amount of relative information associated with one or more of the components of observation data based, at least in part, on a conditional probability determined based, at least in part, on the determined number of alternate components (the vector processing application 14 may process the extracted concept vectors 13A to calculate a sorted list of D concepts (e.g., D=40) that are most strongly connected to a specified concept C. The connection may be determined by identifying the D concepts having a high cosine distance to C that exceeds a specified threshold. In addition, the vector processing application 14 may then identify a concept C′ that is high on this list of D concepts, but yet there are a few co-occurrences of C and C′ in the sequence of concepts within a window of U concepts, [0056]-[0058]);
compute a score indicative of novelty of the observation data, wherein the score is computed based, at least in part, on the determined amount of relative information (identifying, for a concept selected by the user, one or more related concepts (e.g., surprise concepts that are not linked to the selected concept) so that the identified concept(s) can be displayed to promote understanding and interpretation of concept vector relationships, [0025]-[0026]. Towards this end, the vector processing application 14 proceeds through the sorted list of D concepts until a concept C′ is identified whose number of co-occurrences with C is less than W (a parameter, for example 0.01 of the number of times C appears in the sequence). Based on the computation results, the vector processing application 14 may be configured to present the user with the “surprise” concept C′, [0056]); and
provide the score indicative of novelty to a client process (Causing surprise promotes content that is neither too close nor too far, this may be represented by a range [a,b] of cosine distances where a and b are parameters, from the user model, where the means of measuring proximity or distance is through the cosine distance between the respective vectors, [0058]).
Lamba then discloses the number of alternative components corresponding to siblings of the graph node (A relation can be determined between a summation of the affinity scores of the children under a parent, and the number of paths that can be taken from the parent to its children and used as a measure of how well the query matched that parent concept. A score determined in such a manner is referred to herein as a "density score." A density function is any function that relates node scores and some kind of volume, such as the number of children (or leaf level concepts), under a parent, [0093]).
It would have been obvious to an ordinary person skilled in the art at the time of the invention was effectively filed to incorporate the teachings of Lamba with the teachings of Franceschini for the purpose of discovering surprises associated with a query based on ranking concepts matching the query using a quantity of matching sub-concepts of each concept.
Regarding claims 22, 32, and 38, Lamba further discloses one or more of the components of the observation data are stored as textual representations of numeric data, and wherein the score indicative of novelty is computed without converting the one or more of the components of the observation data to a non-textual representation ("jaguar" could refer to the automobile, the mammal, an operating system, etc. Wikipedia attempts to mitigate such ambiguity by presenting a "disambiguation page" in scenarios such as where a user types in the ambiguous term into a search box. A related problem is that of synonyms. For example, "puma," "mountain lion," "panther," and "cougar" are all terms used to refer to the animal Felidae Puma P. concolor, [0047]-[0049]. At 1406 a density function is used to evaluate the received candidate concepts. At 1408, additional processing is optionally performed, as described in more detail below. At 1410 one or more final concepts are associated with the query. Using the information shown in FIG. 10, "concept1=jaguar_car, concept2=jaguar_animal, concept_3=jacksonville_jaguars" is an example of what might be returned at 1410, [0096]).
Regarding claim 23, Lamba further discloses one or more of the plurality of graph nodes comprises data indicative of a distribution of values (the density score of node 1212 is 3. Node 1208 contributes 6 points, while node 1206 does not contribute any. There are a total of two paths which can be taken from node 1212 to the leaf level. The density score of node 1210 is 9, like its child node 1204. The density score of node 1216 is 5. The density score of node 1220 is also 5, because while it is one level higher in the hierarchy, it has only one child (and thus has the same number of paths available as that child). The scores of nodes 1214 and 1218 are undefined because the score of node 1202 is 0. The score of node 1222 is 3.75, [0094]).
Regarding claims 24, 33, and 39, Franceschini further discloses generating an indication that variation of the score indicative of the novelty has fallen below a threshold amount (Towards this end, the vector processing application 14 proceeds through the sorted list of D concepts until a concept C′ is identified whose number of co-occurrences with C is less than W (a parameter, for example 0.01 of the number of times C appears in the sequence). Based on the computation results, the vector processing application 14 may be configured to present the user with the “surprise” concept C′, [0056]).
Regarding claims 26, and 41, Franceschini further discloses the score indicative of novelty is computed based, at least in part, on routing of observations using a fuzzy matching function (processing concept vectors 13A, a vector processing application 14 may be configured to identify “surprise” concepts that are strongly related though seldom mentioned together. For example, after a user explores a plurality of concepts (e.g., Wikipedia concepts), the user may request the user's browser to identify “surprise” concepts, [0056]).
Regarding claims 29, 36, and 43, Franceschini further discloses the score indicative of novelty is computed with respect to a moving window of observations (a graph 18 may be constructed by any desired method (e.g., Google, etc.) to define “concept” nodes which may be tagged with weights indicating their relative importance. In addition, an edge of the graph is labeled with the strength of the connection between the concept nodes it connects. When edge weights are given, they indicate the strength or closeness of these concepts, or observed and recorded visits by users in temporal proximity. An example way of relating the edge weights to user visits is to define the edge weight connecting concept “A” to concept “B” to be the number of times users examined concept “A” and, within a short time window, examined concept “B”, [0038]. It is clear that each related concept has a higher score compare to a source concept based on their temporal proximity or comparisons of time windows).
Regarding claim 30, Franceschini further discloses learning an order of two or more components of the observation (For each Di, the vector processing application 14 is configured to perform an analogy test “Ca to Cb is like Di to ?” The answer is some Di′ such that Xi=cos(Di−Ca+Cb, D′i) is maximal. Based on the computation results, the vector processing application 14 may be configured to present the user with the possible analogous concepts Di′ in order of decreasing Xi values, [0087]).
Claims 25, 34, and 40 are rejected under AIA 35 U.S.C. 103 as being unpatentable over Franceschini in view of Lamba and further in view of Roque et al. (Pub. No. US 2016/0203130, published on July 14, 2016; hereinafter Roque).
Regarding claims 25, 34, and 40, Roque then discloses computing the score indicative of novelty with respect to a time indicated by a query (at query time, for at least one edge in the given user query; i. retrieve matching edges from the keystore; ii. score target documents using at least in part details stored in the values of keys from said matched target document; iii. optionally score matched target documents using at least in part the target term details stored in the values of keys from said matched target document, [0110]-[0113]).
It would have been obvious to an ordinary person skilled in the art at the time of the invention was effectively filed to incorporate the teachings of Roque with the teachings of Franceschini, as modified by Lamba, for the purpose of identifying similar relationship patterns, concepts across different knowledge domains in order to perform information searching within a plurality of graph nodes to determine data of interest.
Claim 27 is rejected under AIA 35 U.S.C. 103 as being unpatentable over Franceschini in view of Lamba and further in view of Murthy (Pub. No. US 2014/0310813, published on October 16, 2014).
Regarding claim 27, Lamba then discloses a node of the plurality of graph nodes comprises a continuing counter and a termination counter (In block 318, the path count for the start node may be determined. The path count may be determined based on the path counts added during block 312 or from path counts determined previously for start nodes that are downstream from the start node, [0070]), and wherein the method further comprises:
incrementing the continuing counter when an observation traverses the node and the node does not represent a final node in the observation (The through path counter 262 may be configured to determine a number of through paths between each of or some of the pairs of the termination nodes 242 and entry nodes 246 based on the contracted flow graph 252, [0054]); and
incrementing the termination counter when the node represents the final node in the observation (The metric termination path counter 266 may be configured to determine a number of terminations paths between each of or some of the pairs of the metric nodes 244 and the termination nodes 242 based on the contracted flow graph 252, [0058]).
It would have been obvious to an ordinary person skilled in the art at the time of the invention was effectively filed to incorporate the teachings of Murthy with the teachings of Franceschini, as modified by Lamba, for the purpose of enhancing graph analysis based on determining metrics associated with a plurality of graph nodes using a path count between a start and end node.
Claims 28, 35, and 42 are rejected under AIA 35 U.S.C. 103 as being unpatentable over Franceschini in view of Lamba and further in view of Tellis (Pat. No. US 10586358, published on March 10, 2020).
Regarding claims 28, 35, and 42, Tellis then discloses computing the score indicative of novelty based, at least in part, on read-only observation data (FIG. 20 is an example flow diagram of a method for reducing the number of links in the animated sequence. The process may commence after the immutable representative node has been identified for each cluster. Using only the representative node of each cluster, a graph is created with each representative node representing a vertex, and the relatedness between nodes representing an undirected weighted edge or link, [Column 13, Lines 7-22]).
It would have been obvious to an ordinary person skilled in the art at the time of the invention was effectively filed to incorporate the teachings of Tellis with the teachings of Franceschini, as modified by Lamba, for the purpose of visualizing observation of user data as a plurality of graph nodes to determine link distances and strengths between unrelated/related nodes for better data analysis of the observed data.
Pertinent Prior Art
The following references are considered relevant to the claims:
Pratt et al. (Pat. No. US 10673880) teaches a security graph as a representation of the relationships between entities in the network and any anomalies identified. A security graph may map out the interactions between users, including information regarding which devices are involved, who or what is talking to whom/what, when and how interactions occur, which nodes or entities may be anomalous, and the like. The nodes of the security graph may be annotated with additional data if desired. the anomalies can be stored in graph database in the form of anomaly nodes in a graph or graphs; specifically, after an event is determined to be anomalous, an event-specific relationship graph associated with that event can be updated.
Sherman et al. (Pat. No. US 9779150) teaches filtering data in data visualizations based on identification a relation between tuples representing a dataset. The relation is a non-empty set of ordered pairs of tuples from the set of tuples. Selection of one or more filter conditions is received for the tuples, where at least one of the filter conditions uses the relation. A data visualization is displayed based on aggregating the set of tuples at the selected aggregation level to form a set of aggregated tuples, and display each aggregated tuple as a visible mark. Each tuple that satisfies all of the filter conditions is included in an aggregated tuple; all other tuples are excluded.
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/SON T HOANG/ Primary Examiner, Art Unit 2169 January 2, 2026