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 .
Response to Arguments
Applicant's arguments filed 11/24/2025 have been fully considered and they are partially persuasive.
Regarding applicant’s remarks directed to the rejection of claims under 35 USC § 112(b),
On pg. 25, Applicant contends each of the modules identified in the prior rejection of claims under 35 USC § 112(b) is a “structural component commonly implemented in systems” and clarifies that “module” refers to “standard conceptual unit in software and systems architecture, a functional block that performs a defined operation or set of operations on input data and produces structured outputs. This usage is well-established in the field and commonly
understood by those of ordinary skill in the art.” Applicant further provides “structural detail” of each module by providing citation to the specification on operations each module performs in pgs. 26-30. Applicant asserts on pg. 31 that “module” refers to “well-understood structural software components.”
However, Examiner respectfully disagrees that the recitation of module provides structure and refers to MPEP § 2181 A. to determine the recitation of “module” is indeed a non-structural generic placeholder:
“The following is a list of non-structural generic placeholders that may invoke 35 U.S.C. 112(f): "mechanism for," "module for," "device for," "unit for," "component for," "element for," "member for," "apparatus for," "machine for," or "system for." Welker Bearing Co., v. PHD, Inc., 550 F.3d 1090, 1096, 89 USPQ2d 1289, 1293-94 (Fed. Cir. 2008); Mass. Inst. of Tech. v. Abacus Software, 462 F.3d 1344, 1354, 80 USPQ2d 1225, 1228 (Fed. Cir. 2006); Personalized Media, 161 F.3d at 704, 48 USPQ2d at 1886–87; Mas-Hamilton Group v. LaGard, Inc., 156 F.3d 1206, 1214-1215, 48 USPQ2d 1010, 1017 (Fed. Cir. 1998). Note that there is no fixed list of generic placeholders that always result in 35 U.S.C. 112(f) interpretation, and likewise there is no fixed list of words that always avoid 35 U.S.C. 112(f) interpretation. Every case will turn on its own unique set of facts.
"The standard is whether the words of the claim are understood by persons of ordinary skill in the art to have a sufficiently definite meaning as the name for structure." Williamson v. Citrix Online, LLC, 792 F.3d 1339, 1349, 115 USPQ2d 1105, 1111 (Fed. Cir. 2015). The issue in Williamson was whether a "distributed learning control module" limitation in claims directed to a distributed learning system should be interpreted as a means-plus-function limitation. See Williamson, 792 F.3d at 1347, 115 USPQ2d at 1110. The Federal Circuit concluded that "the 'distributed learning control module' limitation fails to recite sufficiently definite structure and that the presumption against means-plus function claiming is rebutted." Id. at 1351, 115 USPQ2d at 1113. In support, the Federal Circuit determined that "the word 'module' does not provide any indication of structure because it sets forth the same black box recitation of structure for providing the same specified function as if the term ‘means’ had been used." Id. at 1350–51, 115 USPQ2d at 1112.”
Thus, the arguments are considered unpersuasive as the sufficient structure cannot be provided by the recited modules.
In regards to applicant’s arguments directed to the rejections of claims under 35 USC § 112(a), they are unpersuasive as the recited modules cannot provide sufficient structure and failure to disclose sufficient corresponding structure in the specification will also lack written description. See MPEP § 2163.03, subsection VI.
Regarding applicant’s remarks directed to the rejection of claims under 35 USC § 103, the arguments are directed to newly amended limitations that were not previously examined by the examiner. Therefore, applicants arguments are rendered moot. The examiner refers to the rejection under 35 USC § 103 in the current office action for more details.
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.
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, where the generic place holder has been underlined and the functional language italicize:
In claim 1, “a network detection module installed on the computing device and configuring the computing device to identify structural or behavioral relationships among elements of the system, including but not limited to nodes and links, similarity measures, associations or other indicators of connection or relatedness and determine a set of networked geospatial communities determined from the data from the data stream based on the identified relationships;
a partitioning module installed on the computing device and configuring the computing device to partition the geographical space into a plurality of regions based on indicators of connection or relatedness among elements within the space, each region containing a subset of the identified network relationships;
an output module installed on the computing device and configuring the computing device to:
based on an identification of the corresponding subset of network relationships contained in a first region of the plurality of regions, identify one or more actions to be performed”
In claim 6, “an input module interfacing with the computing device and configured to input into the partitioning module a description of a set of partitions or partition labels previously derived or externally defined, and to make the description available to system components for classification or analysis.”
In claim 7, “an identification module installed on the computing device and configuring the computing device to identify which member of the plurality of regions to associate to the new data.”
In claim 25, “a dimensional reduction module installed on the computing device and configuring the computing device to:
generate a low dimensional space defined by a second number of reduced dimensions determined from the plurality of vectors, the second number being less than the first number;
obtain a plurality of reduced vectors, each reduced vector of the plurality of reduced vectors:
having a corresponding vector of the plurality of vectors; and
having a plurality of values each associated with a corresponding reduced dimension of the plurality of reduced dimensions, and each obtained by applying a dimensional reduction algorithm to the data of the corresponding vector; and
using the corresponding plurality of values of each of the plurality of reduced vectors, map the plurality of reduced vectors onto the low dimensional space to produce a first mapping, the partitioning module using the first mapping to determine the plurality of regions.”
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(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-13 and 25-30 are further rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
In regards to claims 1, 6, 7, and 25, when a claim containing a computer-implemented 35 U.S.C. 112(f) claim limitation is found to be indefinite under 35 U.S.C. 112(b) for failure to disclose sufficient corresponding structure (e.g., the computer and the algorithm) in the specification that performs the entire claimed function, it will also lack written description under 35 U.S.C. 112(a). See MPEP § 2163.03, subsection VI.
Claims 2-13 and 25-30 are further rejected on virtue of their dependency to claim 1.
Claims 26 and 27 is further rejected on virtue of its dependency to claim 25.
Claim Rejections - 35 USC § 112(b)
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 1-13 and 25-30 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 “structural or behavioral relationships among elements of the system, including but not limited to nodes and links, similarity measures, associations or other indicators of connection or relatedness;” it is unclear if the “including but not limited to nodes and links, similarity measures, associations or other indicators of connection or relatedness” refers to the “structural or behavioral relationships” or the “elements of the system.”
Further, “including but not limited to…” is exemplary language and it is unclear whether the claimed narrower range is a limitation. See MPEP § 2173.05(d). For examination purposes, examiner interprets the limitation to be merely “structural or behavioral relationships among elements of the system.”
Claim 1 recites the limitation "based on indicators of connection or relatedness among elements within the space." There is insufficient antecedent basis for “the space” in the claim.
Claim 1 recites the limitation " the identified network relationships." There is insufficient antecedent basis for “the identified network relationships” in the claim and it is unclear if “the identified network relationships” is the same as “the identified relationships.”
Claim 13 recites the limitation "a community detection algorithm." There is insufficient antecedent basis for this limitation in the claim and it is unclear if it is the same community detection algorithm as in parent claim 10.
In claim 1, “a network detection module installed on the computing device and configuring the computing device to identify structural or behavioral relationships among elements of the system, including but not limited to nodes and links, similarity measures, associations or other indicators of connection or relatedness and determine a set of networked geospatial communities determined from the data from the data stream based on the identified relationships;
a partitioning module installed on the computing device and configuring the computing device to partition the geographical space into a plurality of regions based on indicators of connection or relatedness among elements within the space, each region containing a subset of the identified network relationships;
an output module installed on the computing device and configuring the computing device to:
based on an identification of the corresponding subset of network relationships contained in a first region of the plurality of regions, identify one or more actions to be performed”
In claim 6, “an input module interfacing with the computing device and configured to input into the partitioning module a description of a set of partitions or partition labels previously derived or externally defined, and to make the description available to system components for classification or analysis.”
In claim 7, “an identification module installed on the computing device and configuring the computing device to identify which member of the plurality of regions to associate to the new data.”
In claim 25, “a dimensional reduction module installed on the computing device and configuring the computing device to:
generate a low dimensional space defined by a second number of reduced dimensions determined from the plurality of vectors, the second number being less than the first number;
obtain a plurality of reduced vectors, each reduced vector of the plurality of reduced vectors:
having a corresponding vector of the plurality of vectors; and
having a plurality of values each associated with a corresponding reduced dimension of the plurality of reduced dimensions, and each obtained by applying a dimensional reduction algorithm to the data of the corresponding vector; and
using the corresponding plurality of values of each of the plurality of reduced vectors, map the plurality of reduced vectors onto the low dimensional space to produce a first mapping, the partitioning module using the first mapping to determine the plurality of regions.”
invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure (e.g. an algorithm and the hardware, or computer or microprocessor programmed with the algorithm), material, or acts to the function.
MPEP 2181 (II)(B) discloses “ …To claim a means for performing a specific computer-implemented function and then to disclose only a general purpose computer as the structure designed to perform that function amounts to pure functional claiming… The corresponding structure is not simply a general purpose computer by itself but the special purpose computer as programmed to perform the disclosed algorithm. Aristocrat, 521 F.3d at 1333, 86 USPQ2d at 1239. Thus, the specification must sufficiently disclose an algorithm to transform a general purpose microprocessor to the special purpose computer. See Aristocrat, 521 F.3d at 1338, 86 USPQ2d at 1241…Accordingly, a rejection under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph is appropriate if the specification discloses no corresponding algorithm associated with a computer or microprocessor. Aristocrat, 521 F.3d at 1337-38, 86 USPQ2d at 1242.” Therefore, the claims 1, 6, 7, and 25 are indefinite and are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Claims 2-13 and 25-30 are further rejected on virtue of their dependency to claim 1.
Claims 26 and 27 is further rejected on virtue of its dependency to claim 25.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-7, 25, 28 and 30 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US Pub No. US20160055190A1 (“Bar-Yam”).
In regards to claim 1,
Bar-Yam teaches A system, comprising: a computing device configured to obtain a plurality of vectors comprising data from a data stream;
(Bar-Yam, “[0006] In one embodiment, the disclosure provides a system including a computing device configured to obtain a plurality of vectors comprising data from a data stream, each of the plurality of vectors having a plurality of dimensions.”)
Bar-Yam teaches a network detection module installed on the computing device and configuring the computing device to identify structural or behavioral relationships among elements of the system, including but not limited to nodes and links, similarity measures, associations or other indicators of connection or relatedness;
Examiner’s note: Examiner interprets “network detection module” in BRI as software that performs the recited function on the computing device.
(Bar-Yam, Abstract, “Methods, systems, and apparatus, including computing device programs encoded on computing device storage media, for characterizing events in a data stream. In one of the methods a General Method, is used to construct a Specific Method, which performs the characterization of behavioral types of a particular system or set of systems [identify structural or behavioral relationships among elements of the system]. The Specific Method includes event extraction, dimensional reduction, and signature identification in the reduced dimensional space that map the events of a specific system into behavioral types.”)
Bar-Yam teaches and determine a set of networked geospatial communities determined from the data from the data stream based on the identified relationships; a partitioning module installed on the computing device and configuring the computing device to partition a geographical space into a plurality of regions based on indicators of connection or relatedness among elements within the space, each region containing a subset of the identified network relationships;
Examiner’s note: Examiner interprets “partitioning module” in BRI as software that performs the recited function on the computing device.
(Bar-Yam, “[0065] Referring to FIG. 4, a partitioning function may detect these outlier groups 204A-E and partition the low dimensional space 200 into regions that each include one of the detected groups 204A-E [determine a set of networked geospatial communities see groups 204A-E in fig. 4 determined from the data from the data stream; partition a geographical space into a plurality of regions, each region containing a subset of the identified network relationships]. Particularly regarding the example, the partitioning may identify that certain combined measures of the group 204A diverge significantly from the other groups 204B-E; a partition is then drawn to create a region 304A with boundaries that best include the vectors that are more similar to the vectors of the group 204A than they are to any other group [based on the identified relationships; based on indicators of connection or relatedness among elements within the space].”)
Examiner’s note: “networked geospatial communities” is not a term of art and there does not appear to be an explicit definition of “networked geospatial communities” in the specification. Thus, the BRI of “networked geospatial communities” is a group of items in a region wherein the determination of items to the same region is them being interconnected.
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Bar-Yam teaches and an output module installed on the computing device and configuring the computing device to: based on an identification of a corresponding subset of network relationships contained in a first region of the plurality of regions, identify one or more actions to be performed; and transmit one or more messages to one or both of an automated system and a system user, the one or more messages being associated with the one or more actions.
Examiner’s note: Examiner interprets “output module” in BRI as software that performs the recited function on the computing device.
(Bar-Yam, [0007], “The system may further include an output module installed on the computing device and configuring the computing device to receive a determination of the region that contains the location, and output the label of the region that contains the location. One or more of the regions may be labeled as an anomalous region, and the output module may further configure the computing device to output an alert if the location is in one of the anomalous regions [based on an identification of a corresponding subset of network relationships contained in a first region of the plurality of regions, identify one or more actions to be performed; wherein an action could be to output an alert].”)
(Bar-Yam, [0076], “…one or more output modules 612 for generating, formatting, or otherwise preparing data to be transmitted, and then transmitting that data [transmit one or more messages to one or both of an automated system and a system user, the one or more messages being associated with the one or more actions]; and a communication module 614 for communicating with other computing devices.”)
In regards to claim 2,
Bar-Yam teaches The system of claim 1,
Bar-Yam teaches wherein the partitioning module is further configured to associate a label with each of the plurality of regions, the label identifying a data derived characteristic of each of the plurality of regions.
(Bar-Yam, [0006], “The partitioning module further configures the computing device to associate a label with each of the plurality of regions, the label identifying the characteristic of each of the plurality of regions.”)
In regards to claim 3,
Bar-Yam teaches The system of claim 1,
Bar-Yam teaches wherein the partitioning module causes the computing device to partition the geographical space into a multiscale hierarchy of geospatial regions as the plurality of regions, with smaller and larger regions.
(Bar-Yam, [0032], “Where appropriate, the General Method makes use of multiscale mappings [to partition the geographical space into a multiscale hierarchy of geospatial regions] types of systems focusing on particular scales or ranges of scales.” See fig. 4 for differing size regions
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In regards to claim 4,
Bar-Yam teaches The system of claim 3,
Bar-Yam teaches wherein: the partitioning module is further configured to associate a labeling scheme for the multiscale hierarchy, the labels identifying a data derived characteristic of each of the plurality of regions.
(Bar-Yam, [0006], “The partitioning module further configures the computing device to associate a label with each of the plurality of regions, the label identifying the characteristic of each of the plurality of regions.”)
Bar-Yam teaches multiple scales.
(Bar-Yam, [0032], “Where appropriate, the General Method makes use of multiscale mappings between types of systems focusing on particular scales or ranges of scales.”)
In regards to claim 5,
Bar-Yam teaches The system of claim 3,
Bar-Yam teaches wherein the output module is further configured to output the geospatial regions.
(Bar-Yam, [0065], “FIG. 5 then shows a potential output of the system, the low dimensional space 200 and regions 304A-E being presented without the underlying data points.”
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In regards to claim 6,
Bar-Yam teaches The system of claim 1,
Bar-Yam teaches further comprising an input module interfacing with the computing device and configured to receive as input to the system a description of a set of partitions or partition labels previously derived or externally defined, and to make the description available to system components for classification or analysis.
Examiner’s note: Examiner interprets “input module” in BRI as software that performs the recited function on the computing device.
(Bar-Yam, “[0033] The General Method also enables more effective use of human pattern recognition as part of the process, particularly when behavioral types are being finalized. The General Method does not require a-priori human identification of measures, but makes use of the human ability to recognize domains of behavioral types once automatically generated measures are constructed. In particular, once the low dimensional space is partitioned (step 104), the system may obtain input from a human [previously derived or externally defined ie input from a human] to assist in the characterization of the resulting regions (step 105) [to make the description available to system components for classification or analysis]. The system may receive a label for each of the regions, the label describing the characteristics or properties of the region [receive as input to the system a description of a set of partitions or partition labels].”)
In regards to claim 7,
Bar-Yam teaches The system of claim 1,
Bar-Yam teaches wherein the computing device is further configured to obtain new data not previously included in the data streams, the system further comprising an identification module installed on the computing device and configuring the computing device to identify which member of the plurality of regions to associate to the new data.
Examiner’s note: Examiner interprets “identification module” in BRI as software that performs the recited function on the computing device.
(Bar-Yam, “[0007] The computing device may be further configured to obtain a new vector containing new data not wholly included in the data of the plurality of vectors; the dimensional reduction module may further configures the computing device to map, with the dimensional reduction algorithm, the new vector onto a corresponding reduced new vector having a location in the low dimensional space [which member of the plurality of regions to associate to the new data].”)
In regards to claim 25,
Bar-Yam teaches The system of claim 1,
Bar-Yam teaches wherein the computing device is configured to obtain the plurality of vectors having a first number of dimensions, the system further comprising a dimensional reduction module installed on the computing device and configuring the computing device to: generate a low dimensional space defined by a second number of reduced dimensions determined from the plurality of vectors, the second number being less than the first number; obtain a plurality of reduced vectors, each reduced vector of the plurality of reduced vectors: having a corresponding vector of the plurality of vectors; and
(Bar-Yam, [0029], “The dimension reduction maps each high-dimensional vector to a corresponding reduced vector having fewer dimensions [defined by a second number of reduced dimensions determined from the plurality of vectors, the second number being less than the first number; obtain a plurality of reduced vectors, each reduced vector of the plurality of reduced vectors: having a corresponding vector ie high-dimensional vector of the plurality of vectors] and containing a modified representation of the data contained in the high-dimensional vector. The computing device thus generates a low dimensional space [generate a low dimensional space] as a vector space containing, and defined by, the reduced vectors. The computing device thus generates a low dimensional space as a vector space containing, and defined by, the reduced vectors.”)
Bar-Yam teaches having a plurality of values each associated with a corresponding reduced dimension of the plurality of reduced dimensions, and each obtained by applying a dimensional reduction algorithm to the data of the corresponding vector; and
(Bar-Yam, [0029], “In some embodiments, the dimensions of the reduced vectors are a subset of the dimensions of the high-dimensional vectors [having a plurality of values each associated with a corresponding reduced dimension of the plurality of reduced dimensions]. In other embodiments, the dimensions of the reduced vectors are derived from the original dimensions, such as by generating combined-measure values having the highest variation of high-dimensional vector values across all such combined measures. Variation in normalization, measures of variation, imposed constraints, linear and nonlinear representations, may be used in obtaining combined measures while performing dimensional reduction [each obtained by applying a dimensional reduction algorithm to the data of the corresponding vector].”)
Bar-Yam teaches using the corresponding plurality of values of each of the plurality of reduced vectors, map the plurality of reduced vectors onto the low dimensional space to produce a first mapping, the partitioning module using the first mapping to determine the plurality of regions.
(Bar-Yam, “[0030] At step 104, the computing device may partition the low dimensional space into a plurality of regions [map the plurality of reduced vectors onto the low dimensional space to produce a first mapping, the partitioning module using the first mapping to determine the plurality of regions]. In some embodiments, the regions may initially be determined by grouping together subsets of the reduced vectors according to certain values of their dimensions [using the corresponding plurality of values of each of the plurality of reduced vectors].”)
In regards to claim 28,
Bar-Yam teaches The system of claim 1,
Bar-Yam teaches wherein the partitioning module further configures the computing device to map, using a partitioning algorithm, each of the plurality of vectors to a corresponding reduced vector to generate the set of networked geospatial communities.
(Bar-Yam, [0008], “The system further includes a partitioning module installed on the computing device and configuring the computing device to partition the vector space into a plurality of regions, each region containing a subset of the reduced vectors and being associated with a characteristic determined from the data in the one or more vectors that correspond to the one or more reduced vectors in the subset of the region [each of the plurality of vectors to a corresponding reduced vector to generate the set of networked geospatial communities]. The partitioning module may use a pre-set criterion or a pre-set algorithm to partition the vector space [using a partitioning algorithm,].”)
In regards to claim 30,
Bar-Yam teaches The system of claim 1,
Bar-Yam teaches wherein the network detection module further configures the computing device to map, with a community detection algorithm, each grouped edge of a plurality of grouped edges detected in the plurality of vectors, onto a corresponding reduced geospatial grid of multiple scales.
(Bar-Yam, “[0034] In some implementations, the partitions may be “hard” boundaries between regions: a vector located on one side of the partition is in a first region, and a vector located on another side of the partition is in a second region adjacent to the first region. In other implementations, the partitioning (step 104) may include determining a continuum between adjacent regions, which either replaces or traverses a hard boundary between the regions. The continuum may be used to assign a proportional value [with a community detection algorithm; ie the algorithm used to determine the boundaries], in terms of labeling, to a location in the low dimensional space [each grouped edge of a plurality of grouped edges detected in the plurality of vectors, onto a corresponding reduced geospatial grid]. For example, a vector located at an extreme edge of Region A may have a value of 1.0 or be “100% Region A,” while a vector located 10 units from the edge of Region A and 90 units from the extreme edge of Region B may have a value of 0.9 or be “90% Region A, 10% Region B.””)
Bar-Yam teaches of multiple scales
(Bar-Yam, [0032], “Where appropriate, the General Method makes use of multiscale mappings between types of systems focusing on particular scales or ranges of scales.”)
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 (i.e., changing from AIA to pre-AIA ) 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.
Claim(s) 8 and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Bar-Yam in view of Hedayatifar, Leila, Yaneer Bar-Yam, and Alfredo J. Morales. "Social Fragmentation at Multiple Scales." arXiv preprint arXiv:1809.07676 (2018). (“Hedayatifar”)
In regards to claim 8,
Bar-Yam teaches teaches The system of claim 1, wherein;
Bar-Yam teaches the network detection module further configures the computing device to obtain a multiscale fragmentation map
Examiner’s note: a “multiscale fragmentation map” is not a term of art and is not explicitly defined in the specification; thus, Examiner’s BRI is a multiscale map with divided regions
(Bar-Yam, [0032], “Where appropriate, the General Method makes use of multiscale mappings between types of systems focusing on particular scales or ranges of scales.”)
(Bar-Yam, “[0030] At step 104, the computing device may partition the low dimensional space into a plurality of regions [obtain a multiscale fragmentation map]. In some embodiments, the regions may initially be determined by grouping together subsets of the reduced vectors according to certain values of their dimensions.”)
Bar-Yam teaches that shows collective behaviors of people constructed from relationships between them that arise in communications or transactions described in the data streams;
(Bar-Yam, “[0044] In another embodiment of the invention, the data stream contains at least one of several types of data or metadata including but not limited to internet based server activity, computing device activity, health related indicators of an individuals, physician or hospital medical visits of multiple individuals, power transmission levels in the power grid, multiple infrastructure sensors, multiple sensors associated with an industrial process, multiple sensors connected to an urban environment, social media, telephone communications, and internet communications.”)
However, Bar-Yam does not explicitly teach and the partitioning module further configures the computing device to aggregate locations of the people geographically into corresponding groups by linking each location to a hierarchical partitioned, geographical grid comprising at least three hierarchical levels
Hedayatifar teaches and the partitioning module further configures the computing device to aggregate locations of the people geographically into corresponding groups by linking each location to a hierarchical partitioned, geographical grid comprising at least three hierarchical levels.
(Hedayatifar, pg. 2 para. 2-3, “We use geo-located Twitter data to generate geographical networks based on where people travel or communicate [aggregate locations of the people geographically into corresponding groups by linking each location to a hierarchical partitioned]. Nodes represent a lattice of 0:1_ latitude _ 0:1_ longitude cells overlaid on a map of the U.S. Each cell is approximately 10 km wide. Network edges reflect two types of data: mobility and communication. In the mobility network, edges are created when a user u tweets consecutively
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from two locations i and j. In the communication network, edges are created when a user u at location i mentions another user v that has most recently tweeted at location j. The weight of an edge represents the number of people who either travel or communicate between i and j. This network aggregates the heterogeneities of human activities in a large-scale representation of social collective behaviors [34].
In Figure 1, we show the spatial properties of the mobility network in terms of degree centrality (Fig. 1-a) and two levels of modular structure (Fig. 1-b and Fig. 1-c) [geographical grid comprising at least three hierarchical levels; see fig. 1-a to 1-c for the three different hierarchical levels].”
)
Bar-Yam and Hedayatifar is considered to be analogous to the claimed invention because they are in the same field of assigning behavioral meaning to regions. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bar-Yam to incorporate the teachings of Hedayatifar in order to provide a real-world scenario using Twitter and a generalized modularity optimization algorithm in order to understand similarities between networks (Hedayatifar, pg. 8 para. 2, “In order to further understand similarities between the mobility and communication networks, we perform a multi-scale analysis of their community structure using a generalized modularity optimization algorithm that introduces a resolution parameter, γ [36]. Smaller values of γ identify progressively larger communities, and vice-versa. The multiscale analysis of the mobility and communication networks are shown in Figures 4 and 5 respectively. Partitions range from a single large module of the entire US (top panels in Figure 4), down to urban scale partitions (bottom panels). Some states like Pennsylvania are split into multiple communities early in the process (γ ≈ 0.3), while other states like Texas first emerge as single communities (γ ≈ 0.6) and internally fragment later in the process (γ ≈ 0.1). These differences are directly associated with the internal structure of social ties and their geographical breakpoints.”)
In regards to claim 29,
Bar-Yam teaches The system of claim 1,
Bar-Yam teaches wherein the partitioning module further configures the computing device to map a continuum of values onto the set of networked geospatial communities, the continuum setting a corresponding value.
(Bar-Yam, “[0041] One embodiment of the invention constitutes a General Method for constructing Specific Methods for detecting in a data stream a set of activity types within which the vectors are assigned a label based on a continuum of label values, the detecting comprising: a first stage of data processing in which the data is converted to a vector of measures over time, a second stage of processing in which a dimensional reduction method is used to map the data within an interval of time of the trigger onto a lower dimensional space, and a third stage of processing in which a map of correspondence is made of the lower dimensional space onto a continuum of behavioral labels of types of activity [map a continuum of values onto the set of networked geospatial communities, the continuum setting a corresponding value for each of a plurality of social media users who are a part of a first community, of the set of networked geospatial communities], whereby the existence of a set of types of events are detected.”)
However, Bar-Yam does not explicitly teach for each of a plurality of social media users who are a part of a first community of the set of networked geospatial communities
Hedayatifar teaches for each of a plurality of social media users who are a part of a first community of the set of networked geospatial communities
(Hedayatifar, pg. 2 para. 2, “We use geo-located Twitter data to generate geographical networks based on where people travel or communicate [for each of a plurality of social media users who are a part of a first community of the set of networked geospatial communities]. Nodes represent a lattice of 0:1_ latitude _ 0:1_ longitude cells overlaid on a map of the U.S. Each cell is approximately 10 km wide. Network edges reflect two types of data: mobility and communication. In the mobility network, edges are created when a user u tweets consecutively from two locations i and j. In the communication network, edges are created when a user u at location i mentions another user v that has most recently tweeted at location j. The weight of an edge represents the number of people who either travel or communicate between i and j. This network aggregates the heterogeneities of human activities in a large-scale representation of social collective behaviors [34].”)
Claim(s) 9-13 are rejected under 35 U.S.C. 103 as being unpatentable over Bar-Yam in view of Hedayatifar in further view of Reichardt, Joerg, and Stefan Bornholdt. "Statistical Mechanics of Community Detection." arXiv preprint cond-mat/0603718 (2006). (“Reichardt”)
In regards to claim 9,
Bar-Yam and Hedayatifar teaches The system of claim 8,
Bar-Yam teaches wherein the network detection module is configured to produce the multiscale fragmentation map using a community detection algorithm
Bar-Yam discloses that the partitioning module may use a pre-set algorithm
(Bar-Yam, [0008], “The partitioning module may use a pre-set criterion or a pre-set algorithm to partition the vector space [community detection algorithm].”)
However, Bar-Yam does not explicitly teach using a community detection algorithm comprising one of Louvain, spin glass, and infomap.
Hedayatifar teaches using a community detection algorithm
(Hedayatifar, pg. 8 para. 2, “In order to further understand similarities between the mobility and communication networks, we perform a multi-scale analysis of their community structure using a generalized modularity optimization algorithm [using a community detection algorithm] that introduces a resolution parameter, [36].”)
Wherein Hedayatifar citation [36] discloses community detection comprising spin glass
Reichardt teaches comprising one of Louvain, spin glass, and infomap.
(Reichardt, Abstract, “Starting from a general ansatz, we show how community detection can be interpreted as finding the ground state of an infinite range spin glass.”)
Reichardt is considered to be analogous to the claimed invention because they are in the same field of community detection. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bar-Yam and Hedayatifar to incorporate the teachings of in order to provide a community detection algorithm comprising spin glass as doing so would provide a concise definition of communities while being adaptive (Reichardt, Abstract, “Starting from a general ansatz, we show how community detection can be interpreted as finding the ground state of an infinite range spin glass. Our approach applies to weighted and directed networks alike. It contains the at hoc introduced quality function from [1] and the modularity Q as defined by Newman and Girvan [2] as special cases. The community structure of the network is interpreted as the spin configuration that minimizes the energy of the spin glass with the spin states being the community indices. We elucidate the properties of the ground state configuration to give a concise definition of communities as cohesive subgroups in networks that is adaptive to the specific class of network under study. Further we show, how hierarchies and overlap in the community structure can be detected. Computationally effective local update rules for optimization procedures to find the ground state are given. We show how the ansatz may be used to discover the community around a given node without detecting all communities in the full network and we give benchmarks for the performance of this extension. Finally, we give expectation values for the modularity of random graphs, which can be used in the assessment of statistical significance of community structure.”)
In regards to claim 10,
Bar-Yam teaches The system of claim 1,
Bar-Yam teaches wherein the network detection module is configured to apply a community detection algorithm to the plurality of vectors to produce a multiscale fragmentation map comprising the set of networked geospatial communities,
(Bar-Yam, [0008], “The partitioning module may use a pre-set criterion or a pre-set algorithm to partition the vector space [community detection algorithm].”)
However, Bar-Yam does not explicitly teach using a community detection algorithm comprising one of Louvain, spin glass, and infomap.
Hedayatifar teaches using a community detection algorithm
(Hedayatifar, pg. 8 para. 2, “In order to further understand similarities between the mobility and communication networks, we perform a multi-scale analysis of their community structure using a generalized modularity optimization algorithm [using a community detection algorithm] that introduces a resolution parameter, [36].”)
Wherein Hedayatifar citation [36] discloses community detection comprising spin glass
Reichardt teaches comprising one of Louvain, spin glass, and infomap.
(Reichardt, Abstract, “Starting from a general ansatz, we show how community detection can be interpreted as finding the ground state of an infinite range spin glass.”)
In regards to claim 11,
Bar-Yam in view of Hedayatifar and Reichardt teaches The system of claim 10,
Bar-Yam teaches wherein the partitioning module further configures the computing device to map, using a partitioning algorithm, each of the plurality of vectors to a corresponding reduced vector to generate a map of communities
(Bar-Yam, [0008], “The system further includes a partitioning module installed on the computing device and configuring the computing device to partition the vector space into a plurality of regions, each region containing a subset of the reduced vectors and being associated with a characteristic determined from the data in the one or more vectors that correspond to the one or more reduced vectors in the subset of the region [each of the plurality of vectors to a corresponding reduced vector to generate a map of communities]. The partitioning module may use a pre-set criterion or a pre-set algorithm to partition the vector space [using a partitioning algorithm,].”)
Bar-Yam teaches at multiple scales.
(Bar-Yam, [0032], “Where appropriate, the General Method makes use of multiscale mappings between types of systems focusing on particular scales or ranges of scales.”)
In regards to claim 12,
Bar-Yam in view of Hedayatifar and Reichardt teaches The system of claim 11,
Bar-Yam wherein the partitioning module further configures the computing device to map a continuum of values onto the set of networked geospatial communities, the continuum setting a corresponding value for each of a plurality of social media users who are a part of a first community, of the set of networked geospatial communities,
(Bar-Yam, “[0041] One embodiment of the invention constitutes a General Method for constructing Specific Methods for detecting in a data stream a set of activity types within which the vectors are assigned a label based on a continuum of label values, the detecting comprising: a first stage of data processing in which the data is converted to a vector of measures over time, a second stage of processing in which a dimensional reduction method is used to map the data within an interval of time of the trigger onto a lower dimensional space, and a third stage of processing in which a map of correspondence is made of the lower dimensional space onto a continuum of behavioral labels of types of activity [map a continuum of values onto the set of networked geospatial communities, the continuum setting a corresponding value for each of a plurality of social media users who are a part of a first community, of the set of networked geospatial communities], whereby the existence of a set of types of events are detected.”)
However, Bar-Yam does not explicitly teach that is determined by location and social interactions
Hedayatifar teaches that is determined by location and social interactions.
(Hedayatifar, pg. 2 para. 2, “We use geo-located Twitter data to generate geographical networks based on where people travel or communicate [that is determined by location and social interactions]. Nodes represent a lattice of 0:1_ latitude _ 0:1_ longitude cells overlaid on a map of the U.S. Each cell is approximately 10 km wide. Network edges reflect two types of data: mobility and communication. In the mobility network, edges are created when a user u tweets consecutively from two locations i and j. In the communication network, edges are created when a user u at location i mentions another user v that has most recently tweeted at location j. The weight of an edge represents the number of people who either travel or communicate between i and j. This network aggregates the heterogeneities of human activities in a large-scale representation of social collective behaviors [34].”)
In regards to claim 13,
Bar-Yam in view of Hedayatifar and Reichardt teaches The system of claim 11,
Bar-Yam teaches wherein the network detection module further configures the computing device to map, with a community detection algorithm, each grouped edge of a plurality of grouped edges detected in the plurality of vectors, onto a corresponding reduced geospatial grid of multiple scales.
(Bar-Yam, “[0034] In some implementations, the partitions may be “hard” boundaries between regions: a vector located on one side of the partition is in a first region, and a vector located on another side of the partition is in a second region adjacent to the first region. In other implementations, the partitioning (step 104) may include determining a continuum between adjacent regions, which either replaces or traverses a hard boundary between the regions. The continuum may be used to assign a proportional value [with a community detection algorithm; ie the algorithm used to determine the boundaries], in terms of labeling, to a location in the low dimensional space [each grouped edge of a plurality of grouped edges detected in the plurality of vectors, onto a corresponding reduced geospatial grid]. For example, a vector located at an extreme edge of Region A may have a value of 1.0 or be “100% Region A,” while a vector located 10 units from the edge of Region A and 90 units from the extreme edge of Region B may have a value of 0.9 or be “90% Region A, 10% Region B.””)
Bar-Yam teaches of multiple scales
(Bar-Yam, [0032], “Where appropriate, the General Method makes use of multiscale mappings between types of systems focusing on particular scales or ranges of scales.”)
Claim(s) 26-27 are rejected under 35 U.S.C. 103 as being unpatentable over Bar-Yam in view of D. Wu, K. Niu and Z. He, "Robust community detection on dynamic graph," 2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE), Aalborg, Denmark, 2016, pp. 1-6, doi: 10.1109/SPLIM.2016.7528393. (“Wu”)
In regards to claim 26,
Bar-Yam The system of claim 25,
Bar-Yam teaches wherein the dimensional reduction module determines the second number of reduced dimensions and an association of the dimensions to the reduced dimensions based on the complex object.
(Bar-Yam, “[0036] In other implementations the labeling of categories may not be based upon partitions, but rather may itself be a continuum in which the category label is one value selected from a continuum, e.g. real numbers from 0 to 1, instead of a discrete set of partitions. The continuum category label is a linear or nonlinear function of the coordinates of the low dimensional space [wherein the dimensional reduction module determines the second number of reduced dimensions and an association of the dimensions to the reduced dimensions based on the complex object].”)
However, Bar-Yam does not explicitly teach wherein the dimensional reduction algorithm is a sigmoid model fitting algorithm that outputs a complex object which includes a fitted time series, an inflection time, and a slope corresponding to the data,
Wu teaches wherein the dimensional reduction algorithm is a sigmoid model fitting algorithm that outputs a complex object which includes a fitted time series, an inflection time, and a slope corresponding to the data,
(Wu, Section III B., “We spot communities according to the similarity value cu,v. In order to automatically get the best clusters, the sigmoid function is introduced as threshold function f(cu,v,b) [outputs a complex object ie sigmoid function which includes a fitted time series, an inflection time, and a slope corresponding to the data].f(cu,v, b) is a variation version of sigmoid function. As defined in Eq. (2) x=cu,v.b is calculated based on the present network, thus f(cu,v,b) is able to automatically adjust its output value,
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(4)
where b=∑u,v∈VCu,vm.f(cu,v,b) presents the probability that whether u and v belong to the same community or not.
At time t=0, given u,v∈V(0) and e(0)(u, v)∈E(0), if c(0)u,v≥f(c(0)u,v,b(0)), the communities which u and v belong to are merged into one new community.”)
Wu is considered to be analogous to the claimed invention because they are in the same field of community detection. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bar-Yam to incorporate the teachings of Wu in order to provide a threshold sigmoid function to reduce the dependency on input cluster number and ensure application robustness (Wu, Abstract, “Many approaches have been proposed to identify communities on complex networks. However the current algorithms are sensitive to the variation of input data and parameters. In this paper, we propose a new community detection approach called robust community detection on dynamic network (RCD). The robustness of our algorithm lies in two aspects. Firstly, by adopting the offset of sigmoid function, RCD reduces dependency on the input cluster number. Therefore, RCD is insensitive to the man-made interference and the robustness is guaranteed. Secondly, RCD is not restricted to the type of input networks, because it only depends on the topological structure of network rather than requiring labels or other information of networks. Thus, the application robustness is ensured. RCD are evaluated on both the synthetic and realistic network data. The experiment result shows that by introducing sigmoid function, the error rate of misclassification and iterative times are decreased.”)
In regards to claim 27,
Bar-Yam and Wu teaches The system of claim 26,
Bar-Yam wherein the partitioning module includes a partitioning algorithm to identify the regions corresponding to similar behaviors, the partitioning algorithm being one of k-means, hierarchical clustering, density segmentation, and regression.
(Bar-Yam, [0030], “The labeling of the points according to this dimension might make use of an algorithm that partitions space according to the clustering or density [density segmentation] of those who live or die, separating regions in which a high proportion of points associated with individuals who die, from regions in which a low proportion of points associated with individuals who die.”)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Pat No. US8683389B1 Bar-Yam et al. teaches Method and apparatus for dynamic information visualization
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/J.T.T./Examiner, Art Unit 2129
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129