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 of Claims
This Office action is in response to correspondence received January 23, 2026.
Claim 68 is amended. Claims 68-78 are pending and have been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on January 23, 2026 has been entered.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Applicant recites A computing apparatus comprising means for carrying out the method of claim 68. This is a means plus function limitation and therefore Applicant is limited to the specification, such as the generic computer hardware on pages 55 and 56 of the specification.
Claim Rejections - 35 USC § 112
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 68-78 are 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. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Examiner thoroughly searched the disclosure for support for these amendments, including but not limited to the paragraphs cited in Applicant remarks. If those paragraphs teach these limitations, further clarification is required to understand Applicant’s interpretation of the specification.
In claim 68, Applicant recites “wherein each node of the single model retrieves its status value directly from a locally stored file without performing a retrieval query in a database system, wherein a location of the status value in local memory is determined by the single model via the node and links indicating relevance of the node and its context to context of another node”
It appears that the closest support for this is in par 214:
“The Overlay also differs from prior art in that the run time engine is programmed to run conversions without running data location and retrieval queries associated with relational data models as in prior art. If data retrieval queries are used, the domain model traceability may be lost, therefore data location and retrieval queries are avoided in the Overlay domain model. So unlike prior art, the Overlay run time will not run relational data model queries combined with conversion algorithms. However for any multi-stage calculations in domain, each Overlay domain model conversion algorithm, must be able to refer to those nodes and their status and their state parameters that are linked to the conversion algorithm. But this reference to nodes, must only refer to the immediately subordinate nodes otherwise the traceability can be lost.”
Then applicant supplied the following: Par 111 (of the PGPUB):
“To overcome the limitations of the software model of FIG. 1 the present Overlay model is presented. FIG. 2 illustrates the operation of software code using the Overlay model 60. Overlay model 60 uses the notion of nodes 62 which represent problems/failures and opportunities/successes within processes in an enterprise (or other) environment. Nodes are connected to each other in a node cluster 64. Each node 62 retrieves its value from a file and does not require a retrieval query in a database system, which query would need to run at runtime.”
However, this teaching does not teach the specific limitation of “wherein each node of the single model retrieves its status value directly from a locally stored file without performing a retrieval query in a database system, wherein a location of the status value in local memory is determined by the single model via the node and links indicating relevance of the node and its context to context of another node”
Then paragraph 063:
“Relevance Criteria: Ways of expressing relevance which help build the enterprise activity model or structure. The relevance criteria is determined primarily by the node and its state including context and the logical links between nodes plus the links of similarity between nodes in different layers of abstraction. Relevance criteria can also include aspects of the cognitive structure that is assigned to nodes such as roles, users, workflows, actors and other concepts that help practitioners determine relevance. Relevance criteria can also be presented by node clusters configured in specific ways.”
And paragraph 0114: “The location of the stored value in the storage mechanism or cache is determined exclusively by the overlay model via the node and links that indicate relevance of one node and its state (context) to another node and its context.”
While this was reviewed, no support was shown for the following:
wherein each node of the single model retrieves its status value directly from a locally stored file without performing a retrieval query in a database system
First, there is no support for retrieves its status value directly from a locally stored file. The only support here, where it is connected to “without performing a retrieval query,” is in par 0111, where the node retrieves “its value” but the status value is not specified. There are other values described in the specification such as “identity value.” See par 0100. The neighboring paragraphs have been reviewed but there is no further specificity that limits the value in par 0111 to the status value and therefore there is not support for this limitation.
Then, there is no support for “a locally stored file.” Par 0114 describes a storage mechanism or cache, and par 0115 describes memory, but not a locally stored file.
Then, per the limitation,
the central processing unit uses the propagation path of changes for-adapting operation of the computing apparatus to changes in the single model, detected by comparing propagation paths between nodes in different layers of abstraction of the single model, and wherein the adapting operation comprises identifying next execution steps using the single model during uninterrupted computing system operation
Applicant recites the following as support:[112] In the Overlay, all information is part of the model so each node calls its own information by way of status values. Unlike prior art, there is no need to run queries on an external data model, so the computer readable coded logic embedded in the Overlay model lacks any retrieval aspect to an external data model and does away with the discontinuity in the computer readable logic to accommodate retrieval. Prior art code cannot be a model because the retrieval process and logic is adjacent to conversion logic and thus is very difficult to trace one conversion and its data to the directly following conversion and its data. That is, the code cannot be a continuous model because it is interrupted by retrieval queries which in turn are not precise depictions of status and state of the nodes being processed by the code, and therefore cannot link one stage of propagation to the next to form a model.
[0224] As compared to conventional software, by having far greater ability to trace changes in the system and their effect in propagation, the Overlay makes the testing and verification faster and more transparent, makes upgrades smaller in volume and the development process of upgrades much shorter. This can be achieved by the identification of dependent components and the consequent compilation and prioritization of the test cases to be considered during the testing and verification process. Therefore, in domains like major emergency response applications like a hostage or terrorist attack in a city, where upgrades can prove to be required during the emergency, since no emergency is ever the same as another, it is possible to upgrade using the Overlay by distributing very light upgrades of only the node clusters that have changed.
[0236] It may be used to build enterprise models designed centrally or enterprise models consisting of prebuilt components from disparate designers. It may also be used to build client devices like, but not limited to, robots, drones, medical devices, IoT, etc. Smartphones can also be included and can be connected to a server domain model either continuously or intermittently. A characteristic of this unit is its security, performance and data and system state synchronization to a server domain model, when it has access to a server. It has a connector that provides an ability to connect expansion boards with, for example but not limited to, Wi-Fi, biomedical sensors, cameras, keyboards, touch screens, touch displays etc. An enterprise could connect commercially available expansion boards or develop its own.
Applicant must show how each and every limitation is taught in the specification and there is not support for the following in these paragraphs:
uses the propagation path of changes for-adapting operation of the computing apparatus to changes in the single model, detected by comparing propagation paths between nodes in different layers of abstraction of the single model,
and wherein the adapting operation comprises identifying next execution steps using the single model
during uninterrupted computing system operation
Examiner has reviewed the above paragraphs, copied here for convenience as well as Applicant remarks, but 1) Applicant did not show specifically where support was for these three elements 2) nor is it present by reading it. Therefore, having failed to show support, the rejection is for new matter.
Therefore, like the previous limitation, without support for each and every element of this limitation, as well as the combination and order of elements, shown in the original disclosure, Applicant has recited new matter.
Claims 69 – 78 are rejected for either being dependent on claim 68 or referring in some form to claim 68.
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 76 and 77 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 76 recites A computer program product comprising instructions which , which the program is executed… there is no antecedent basis for the program, and therefore the scope is unclear and the claim is indefinite.
Claim 77 recites the computer program product which has the indefinite scope of claim 76.
Therefore, claims 68-78 are rejected under 35 USC 112.
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 68-78 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim(s) 68 recite(s):
the method comprising create a single model; wherein the single model comprises several layers of abstraction, each layer of abstraction containing a cluster of nodes, where each node has associated context, status value and state parameters, each node representing status and control of a process, wherein the context of each node comprises relevance and state of each node to another one of the nodes; wherein each node is connected by a link to at least one other node at the same layer of abstraction or at a different layer of abstraction and the embedded data of the single model [is processed] to connect nodes and transfer new values associated with the connected nodes along a propagation path from a source node to a target node by way of the link; wherein each node of the single model retrieves its status value directly without performing a retrieval query wherein a location of the status value is determined by the single model via the node and links indicating relevance of the node and its context to context of another node; prioritizes execution of node status propagation routines based on a goal proximity of the starting node to a high-level goal node; uses the propagation path of changes in the single model, detected by comparing propagation paths between nodes in different layers of abstraction of the single model, and wherein the adapting operation comprises identifying next execution steps using the single model
Claim 68 recites an abstract idea that is a mathematical concept – mathematical relationship; a mental process; and a certain method of organizing human activity. The above steps describe a mathematical relationship of graph or network theory. Absent of the generic, high level recitations of computing components, there is nothing more than steps to walk through a graph of nodes and retrieve values about the nodes. This is also a mental process as one could mentally step through these node steps. Note (this applies to all three interpretations) that node, here is used in its broadest reasonable interpretation in light of the specification. Applicant did not include “node” in the definition section see pars 013-070. Therefore Applicant left it to the descriptions elsewhere in the specification to give node some scope. To wit: Par 070: “In a similar example; Node; “transfer monthly pay to crewmember” Context “all crewmembers”, node status; “‘At risk of not being paid on time”, Timestamp of Status ‘1-1 2018″, role 1): Payroll officer, role 2); fleet manager.” This is an abstract idea element because it is an information element that one could have mentally or on paper. Therefore node, per paragraph 070, could be fairly described as information, data, ideas, thoughts, etc.
Finally this could be a certain method of organizing human activity as this node patent (where nodes have been described by the Applicant as information, thoughts, notes, ideas, etc) describes managing personal behavior or relationships or interactions between people, as the nodes here apply to paying people on time, for example.
Therefore, for these reasons, claim 68 recites an abstract idea.
This judicial exception is not integrated into a practical application. The additional elements are apply it elements because they are, in combination, generic computing components performing in their ordinary capacity or they are desired functions or outcomes without specific limitations describing how they achieve the desired result. See MPEP 2106(f)(1-2).
The additional elements of claim 68 are:
A computer implemented method for adapting a computing apparatus to hardware and/or data changes in a decentralized computing system
storing at a central processing unit of the computing apparatus, instructions which are embedded along with data in the computing apparatus; the central processing unit executing the stored instructions to
from a locally stored file
querying in a database system,
storing in local memory
the central processing unit
for adapting operation of the computing apparatus to
during uninterrupted computing system operation.
Limitations and elements such as “from a locally stored file” “querying…” “storing…” a “central processing unit” “storing…instructions…embedded…executing stored instructions to,” are generic computing components and processing such as storing, querying (collecting, comparing, and showing the results), running software on a computer. In combination this amounts to instructions to apply the abstract idea to a generic computer, see MPEP 2106.05(f)(2), Alice, Versata.
Limitations and elements such as “during uninterrupted…operation,” “for adapting operation of the computing apparatus to,” are desired functions or outcomes that connect the abstract idea to a desired computing outcome or result but no technical limitations as to how this is achieved. See MPEP 2106.05(f)(1). For example, “during uninterrupted computing system operation is generic and could even mean, while a computer is left on, therefore a further generic computing limitation. For adapting has no technical limitations whatsoever (to one ordinarily skilled in the art, how technically is the computer adapted its operation) as nearly the entirety of the claim language is an abstract idea. Further, “for adapting operation of the computing apparatus” could also be interpreted as applying the computer to the abstract idea, as there is no technical detail except that the computing apparatus performs these steps. One is in the preamble which, as there is sufficient definition of the claims in the body is only the intended result of the claim. The second adapted is in the last limitation, where the computing apparatus “operation” is adapted to “changes in the single model” which under a broadest reasonable interpretation could be described as changing the computer’s operation to reflect the model, in other words, changing the abstract idea on the computer (like changing words in a word doc that describe the payroll nodes). The final adaptation of operation limitation is defined as “identifying next execution steps” which under a broadest reasonable interpretation could be inputting data into a computer, for example defining when payroll will happen per the payroll nodes which describe the nodes (nodes equal ideas, information, thoughts, nodes, data, etc.). Therefore in combination the additional elements amount to no more than instructions to run an abstract idea of nodes, where the nodes are just thoughts, information, data, notes, ideas, words, numbers, etc. For these reasons the combination of additional elements are not a practical application.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the reasoning above is carried over into this step. For the same reason that the combination of additional elements is not a practical application, it is not significantly more.
Per the dependent claims:
Per claim 69, the additional elements of synchronizing a model with a version stored on another computer is an apply it limitation of copying something from one computer to another (in this case, payroll information in nodes). Then, replacing a computer that has broken is an apply it limitation of using a working computer (buying one from the store).
Per claim 70, the abstract idea is further defined by “changing the single model” in a way that is a part of the abstract idea.
Per claim 71, the abstract idea is further defined with tracing changes in a model, so collecting and comparing data about what changed.
Per claim 72, the abstract idea is further defined with changing a propagation path of changes based on certain models.
Per claim 73, the abstract idea is further defined with splitting models or replacing models.
Per claim 74, the abstract idea is further defined with limitations about redistributing a model with changes. The additional elements amount to copying data from one computer to another, which is an apply it limitation.
Per independent claims which refer back to claim 68, they recite applying the abstract idea to a computer in the following ways:
Claim 75 recites a computing apparatus for carrying out the method of claim 68, so the computing apparatus is an additional element, instructed to apply the abstract idea of claim 68 to the computer.
Claim 76 recites a computing program product which causes a computer to perform the steps of claim 68. In other words, a medium storing software instructions.
Claim 77 recites a medium storing a program of claim 76.
Claim 78 recites a system that has two computing apparatuses, instead of one computing apparatus. Here there are two computers.
Therefore none of the independent claims recite elements that would be a practical application of the abstract idea, or significantly more, because they recite computers (apparatuses), product (medium), or medium (medium).
Therefore claims 68-78 are rejected under 35 USC 101.
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.
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) 68-72 and 75-78 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lyras, US PGPUB 20140330616 A1 (“Lyras”), in view of Colecchia et al., US Pat No 7647335 B1 (“Colecchia”), further in view of Vincent, US PGPUB 20020165948 A1 (“Vincent”), further in view of Shekhar US PGPUB 20140058674 A1 (“Shekhar”).
Per claims 68, 75, 76, and 77, which are similar in scope, Lyras teaches A computer implemented method for adapting a computing apparatus to hardware and/or data changes in a decentralized computing system, the method comprising: storing at a central processing unit of the computing apparatus, instructions which are embedded along with data in the computing apparatus in par 0277: “A further tenet of this evolution of the Overlay/TA system is the explicit combination of the process model, as manifest by the interrelated the node structure, with the data model, defined as attributes. It is this combining of data with interrelated nodes connected by cause and effect, that is to say explicitly showing what the data is for and how it is transformed, which is central to the ability of the system to host meaningful computer readable transformation logic and meaningful user interface attributes without special programming skills. This is in contrast to traditional data structures, such as relational databases, that lack an explicit representation of what the data is for (i.e. the goals that it supports) and, as such, lack the resolution required to derive a suitable user interface. It is traditionally this information that is embedded and thus hidden and fixed within the code and which needs to be made explicit within the model in order for experts in the real world processes and computerized transaction processes, to be able to understand all the computerized processes within the enterprise application and extend them when needed.” The information – the cause and effect data – embedded, teaches the limitation above. See also par 0244: :The facility 1202 includes a computing device 1218 having a computer-readable medium with executable instructions encoded on non-transient digital storage medium, the non-transient digital storage medium also storing a model of the enterprise, content of the enterprise and data of the enterprise, wherein processes associated with the model include nodes, a computer readable description of relevance between different nodes, and a computer readable description of relevance between content and data and the different nodes.”
Lyras then teaches the central processing unit executing the stored instructions to create a single model in pars 0175-0180: “The following procedure (illustrated in FIG. 9) may be followed by an expert user 920 to identify nodes at various levels of abstraction, in accordance with the disclosure. [0176] (1) Identify nodes: The expert overlay users identify the nodes found in a context specific process whose similarity to other processes is sought (step 921); [0177] (2) Identify context relating to each node at the various levels of abstraction (step 922). This is achieved through the domain (context specific) process model particular to the enterprise in which the process in question and the processes which depend on it are analyzed for context specificity; [0178] (3) Identify lower context level node attributes (step 923): The expert overlay users identify the node attributes as described above as node attributes 930: Attributes 931 include context specific process goals and conditional goals; goal conflicts; context specific assets and obstacles; and context specific cause and effect of the nodes for the process whose similarity with other processes is sought; [0179] (4) Select from a set of abstracted lower context level node attributes (step 924): In other words select from a pre-populated set of node attributes at a stage of context abstraction above the level in steps 1 to 3. The overlay system 940 shows the user a selection of higher level node attributes; the expert user selects higher level attributes 932 to match the lower level attributes of the nodes identified in step 3; and [0180] (5) Select for a higher or highest context level set of node attributes (step 925): The overlay shows a selection of higher or highest context level enterprise node attributes. The expert user selects the highest level attributes that match the attributes identified in step 3.”
See also: par 0216: “The indexing engine indexes information against the enterprise stress points, using indexing model 1130. The indexing model is constructed using case histories (stories of how the enterprise dealt with stress points in the past) 1106 and current cases of problem-solving in the enterprise 1107. Both current and past cases are thus used to populate the system. Analysis of current cases creates the indexing for the case against the cognitive structure. Past cases can be entered as experiences as a separate process not directly a part of enterprise problem solving.”
See also: par 0265: “The following section discloses an embodiment of the Overlay concept, which is the elimination of the need for traditional code and thus seeing all system behaviour being driven by a newly extended node structure. Conceptually speaking, the business model thus becomes the code of the application. This will enable Overlay systems to meet the objectives outlined above.”
See also pars 0283-0284.
Lyras then teaches wherein the single model comprises several layers of abstraction, each layer of abstraction containing a cluster of nodes, where each node has associated context, status value and state parameters, each node representing status and control of a process, wherein the context of each node comprises relevance and state of each node to another one of the nodes in par 0322: “Node A 1502, node B 1504, node C 1506 and node D 1508 then represent a cluster of nodes overlaying the processes to be integrated. The abstracted nodes 1502-1508 are then connected to the attribute of door status and to the attribute of attendance. Each node has a cause and effect relationship with an intermediate goal. For example, intermediate goal E 1510 is a count of how many doors are closed and intermediate goal F 1512 are the number of students at a muster point. These goals then converge on the common goal determining the percentage of students at the muster point and the percentage of doors closed. When both are equal to 100%, the common goal 1500 is achieved.”’
See also pars 0313-0318: “[0313] Map the state model to the state models of the two systems to be integrated. [0314] Abstract the problems and solutions offered by each application to be integrated. [0315] Create a cluster of abstracted nodes overlaying the processes to be integrated. [0316] Connect the abstracted nodes to the attributes in each application to be integrated. [0317] If done right, the two sets of attributes (i.e. the abstracted set and the set from the applications) will be the same. In other words, create predictive patterns for each node in each domain and locate the data in the tables mentioned above and connect to the abstracted nodes. [0318] Eventually, the data in these predictive patterns will be equivalent despite the table relationships and groupings being different in each of the two applications that are being integrated. Any goal based relationships as implemented by the applications are thus replaced by the node structure.”
See also par 0346: “With reference to FIG. 19, a portion of facility 1202 is illustrated. Node 1206 is a stress point for an emergency and is linked to nodes identifying different types of emergencies such as fire 1252, flood 1254 and intruder 1256. These different types of emergencies have nodes in common such as location of problem 1258, number of persons who have reached a safe location 1260, number of persons unaccounted for 1262, emergency equipment on site 1264 and location of exits 1266. Some nodes may have sub-nodes, for example number of persons who have reached a safe location 1260 may have a sub-node of medical condition of each person, node 1268, and a corresponding sub-attribute in the state model corresponding to medical condition of each person.”
Lyras then teaches wherein each node is connected by a link to at least one other node at the same layer of abstraction or at a different layer of abstraction and the central processing unit processes the embedded data of the single model to connect nodes and transfer new values associated with the connected nodes along a propagation path from a source node to a target node by way of the link in par 0292-0293: “State model attribute values may also contain a link in the model via a node to a further attribute value to indicate a relationship. For example, age and name attributes may be linked to an employee ID attribute to collectively represent an employee and this may be represented in a node cluster. At a class level, the types of relationships that exist are derived from the process model (node clusters), typically as a result of attributes being part of the same cluster serving a larger emulated process or parent node.
[0293] A key mechanism here is the context component of the Overlay interface. Attributes used for contextual filtering, that is system navigation using key identifiers, become `key` attributes. A key attribute forms a primary navigational approach when many similar processes are differentiated in their cause and effect by a key identifier and when many related but different process steps use the same key identification. For example, if an employee name is defined as a key attribute, it becomes possible for the system to link employee age attributes to the name if each of these attributes form two nodes in a larger cluster identifying an employee. The key attribute and the use of the key attribute for system navigation helps make sure when processing employee details we are focusing on the right employee if there is more than one, while the process steps are the same for all employees.” See also par 0247: “The Overlay incorporates a set of interrelated nodes which are connected by cause and effect and similarity. The cause and effect can be determined also by formulas and relationships between properties related to nodes. In conventional programming, changes are made to relationships and to data representing problems and opportunities. After these changes are made, new user interfaces need to be designed to present these new relationships to users. The present embodiment bypasses the second programming exercise.” Cause and effect teaches propagation path (a direction).
Lyras then teaches the central processing unit prioritizes execution of node status propagation routines based on a goal proximity of the starting node to a high-level goal node in par 0192: “(3) Apply goal proximity to compare the importance of the common processes in each respective domain (step 1079): This step is more important than the following step in instances where the effect of the process on goals it serves is a more important or more revealing similarity criterion. This similarity tests whether the new contextually specific process has a similar goal proximity to its main goal and conditional goals as does the original process in another context. This includes dependent processes which are potentially affected by the nodes whose abstractions are common between the contextually specific processes being compared. This comes before context similarity when the importance of a process needs to be similar between the processes being compared.” Par 0254: “By contrast in the Overlay the cause and effect relationships are either depicted empirically by goal proximity or by an algorithm in the cases of more quantitatively explicit relationships as mentioned herein.” The empirical depiction of goal proximity teaches prioritization of execution because goals that are more proximate are depicted higher than other goals.
Lyras does not teach wherein each node of the single model retrieves its status value directly from a locally stored file without performing a retrieval query in a database system, wherein a location of the status value in local memory is determined by the single model via the node and links indicating relevance of the node and its context to context of another node
Colecchia teaches distributed generation and storage of relational data. See abstract.
Colecchia teaches wherein each node of the single model retrieves its status value directly from a locally stored file without performing a retrieval query in a database system, wherein a location of the status value in local memory is determined by the single model via the node and links indicating relevance of the node and its context to context of another node in col 14 ln 3-21: “In order to effectively assign records to nodes for processing and retrieve data (networks) from the nodes in response to user queries, a computer hardware and software management subsystem is provided which generates and stores logs of activity of the distributed system and indexes of records and relational data created by the distributed computing nodes. By virtue of local storage of such logs and indexes, each node knows where source data or input records are stored locally, e.g., in local data repositories such as data storage devices, and where generated relational structures (sub-networks) are stored and shared for remote access by the Query Management System. Furthermore, each node may maintain a flag indicating whether any of the management processes are currently active or inactive in that node, and information (such as IP address) of which node or computing platform in the overall system is currently acting as the network management process, indexing process, dispatch optimization process and query management process.” The relational structures (node to node) and status (logs and indexes) are stored locally and therefore a query retrieval is unnecessary.
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the node abstract and proximity teaching of Lyras with the status locally stored teaching of Colecchia because Colecchia teaches in col 1 ln 22-25 that: “Aspects of the system also provide methods for providing a user with persistent access to the relational data in a distributed computing environment.” As this would help a user who is not always in contact with the other nodes one would be motivated to modify Lyras with Colecchia to provide support to each node persistently. For this reason one would be motivated to modify Lyras with Colecchia.
Lyras does not teach the central processing unit uses the propagation path of changes for adapting operation of the computing apparatus to changes in the single model, detected by comparing propagation paths between nodes in different layers of abstraction of the single model, and wherein the adapting operation comprises identifying next execution steps using the single model during uninterrupted computing system operation.
Vincent teaches a method for discovering resources in a network of user nodes. See abstract.
Vincent teaches the central processing unit uses the propagation path of changes for adapting operation of the computing apparatus to changes in the single model, and wherein the adapting operation comprises identifying next execution steps using the single model during uninterrupted computing system operation in par 040: “Thus, in the second embodiment resource requests are both published through the messaging infrastructure layer (as in the first embodiment) and propagated through the user nodes of the decentralized network. Because this dual path process offers an alternative to the request propagation path that passes through the centralized server node, the single point of failure is eliminated. In other words, the resource request is propagated to other user nodes even when the server node is down. Further, in the second embodiment, it is not necessary for all of the user nodes to be connected to, or even know about the presence of the publish-subscribe infrastructure. Such a user node can merely forward resource requests to all of the user nodes to which it is connected. This feature allows the second embodiment to be implemented in an existing network without requiring the modification of all user nodes.”
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the proximity goal abstraction node student attendance teaching of Lyras with the propagation path compare (by taking different paths) teaching of Vincent because Vincent teaches that users can have enhanced privacy with these teachings in a peer to peer network, see par 010, which allows for an alternative to a centralized network. As more privacy increases security while providing network benefits, one would be motivated to modify Lyras with Vincent.
Lyras does not teach detected by comparing propagation paths between nodes in different layers of abstraction of the single model
Shekhar teaches finding a shortest path from a destination to a start time. See abstract.
Shekhar teaches detected by comparing propagation paths between nodes in different layers of abstraction of the single model in par 040: “times are identified and stored at step 510. To identify the critical times, each of the path functions for each edge leaving the current node are compared at each time point. The edge with the earliest path function for each starting time is identified. Each starting time at which the edge with the earliest path function changes is identified as a critical time. Identifying the critical times can also be described as determining transit times from the source to two separate locations for a plurality of start times and designating those start times at which the transit time to one of the two locations changes from being shorter than the transit time to the other of the two locations to being longer than the transit time to the other of the two locations.” Path functions teaches propagation paths as there are start times and transit times which teach propagation. Nodes are taught because each edge leaving current node is how the comparison is performed. Comparison is taught as underlined above.
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the node teaching of Lyras with comparison of propagation path of node teaching allows for determining the shortest route in many different scenarios, like shipping and travel, see par 002. As this is widely applicable one would be motivated to modify Lyras with Shekhar because as Lyras teaches the nodes teachings are for improving business, see pars 0225-0239 which describe “making strategic plans” and “dealing with problems and opportunities,” therefore Lyras would be motivated to combine Shekhar to find shorter paths, and therefore save time. For these reasons one would be motivated to modify Lyras with Shekhar.
Per claim 69, Lyras, Colecchia, Vincent, and Shekhar teach the limitations of claim 68, above. Lyras does not teach the central processing unit executing the stored instructions to synchronize the single model with a version of the single model stored at a second computing apparatus;
Vincent teaches the central processing unit executing the stored instructions to synchronize the single model with a version of the single model stored at a second computing apparatus in par 015: “Based on the information request of the user 312, the server 302 merges the stock trade information from the two sources, transforms the raw price and volume information into value information for each trade, and then filters the derived values to produce the subset of trades that are valued at over one million dollars. In a similar manner, each subscribing user (e.g., nodes 304, 306, and 308) specifies its own criteria, and the message-brokering server 302 performs information selection, transformation, filtering, and delivery in order to provide each user with the requested information.” See also par 032: “FIG. 4 is a flow chart of a process for obtaining a resource within such a scalable network framework in accordance with a first embodiment of the present invention. Whenever a first user node 416 in the network desires are source (e.g., file), are source request (or query) is sent to the server node 402 (step S10), and the server node 402 publishes the resource request by sending it to all of the user nodes that are subscribed to the channel corresponding to that type of request (step S12). A second user node 422 that receives the request and is willing to provide the resource contacts the first user node 416 (step S14), and the first and second user nodes 416 and 422 set up a peer-to-peer connection to provide the requested resource to the first user node 416 (step S16). In this manner, the publish-subscribe messaging infrastructure layer allows a resource request to reach nodes that are separated from the requesting node by a direct-connect path that includes a very large number of intermediary nodes.”
Vincent then teaches and the central processing unit adapting the operation of the computing apparatus in the absence of the second computing apparatus or the replacement of the second computing apparatus with a new computing apparatus when the second computing apparatus loses its ability to function in par 040: “Thus, in the second embodiment resource requests are both published through the messaging infrastructure layer (as in the first embodiment) and propagated through the user nodes of the decentralized network. Because this dual path process offers an alternative to the request propagation path that passes through the centralized server node, the single point of failure is eliminated. In other words, the resource request is propagated to other user nodes even when the server node is down. Further, in the second embodiment, it is not necessary for all of the user nodes to be connected to, or even know about the presence of the publish-subscribe infrastructure. Such a user node can merely forward resource requests to all of the user nodes to which it is connected. This feature allows the second embodiment to be implemented in an existing network without requiring the modification of all user nodes.” Where the server node goes down other nodes fulfill the delivery teaching that the new computing apparatus replaces the second computing apparatus.
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the proximity goal abstraction node student attendance teaching of Lyras with the propagation path compare (by taking different paths) teaching of Vincent because Vincent teaches that users can have enhanced privacy with these teachings in a peer to peer network, see par 010, which allows for an alternative to a centralized network. As more privacy increases security while providing network benefits, one would be motivated to modify Lyras with Vincent.
Per claim 70, Lyras, Colecchia, Vincent, and Shekhar teach the limitations of claim 68, above. Lyras does not teach further comprising upgrading the data and software instructions of the single model by selectively changing the single model based on which nodes, contexts, and users of the single model lie in a propagation path of changes
Vincent teaches further comprising upgrading the data and software instructions of the single model by selectively changing the single model based on which nodes, contexts, and users of the single model lie in a propagation path of changes in par 043: “In preferred embodiments, each determination of whether or not to send the request to the publish subscribe server node is a "random" decision made by the user node based on a weighting factor of between 0 and 1 that gives the probability that the request will be sent to the server node. For example, if the weighting factor is 0.25, then there is a 25% chance that the user node will send the request to the server node and a 75% chance that the request will be forwarded to another user node. Thus, on average a weighting factor of 0.25 should cause resource requests to be forwarded to other user nodes three times before being sent to the publish-subscribe server node. The value of the weighting factor is set based on factors such as the desired level of privacy. Further, the weighting factor can be a fixed value that is used throughout the network or can be set by the user nodes on a per message basis. Other criteria such as a maximum number of forwards or a maximum elapsed time can also be incorporated into the determination that is made by each user node receiving the request.”
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the proximity goal abstraction node student attendance teaching of Lyras with the propagation path compare (by taking different paths) teaching of Vincent because Vincent teaches that users can have enhanced privacy with these teachings in a peer to peer network, see par 010, which allows for an alternative to a centralized network. As more privacy increases security while providing network benefits, one would be motivated to modify Lyras with Vincent.
Per claim 71, Lyras, Colecchia, Vincent, and Shekhar teach the limitations of claim 68, above. Lyras does not teach comprising tracing changes in the single model and the data using the propagation path of changes.
Vincent teaches comprising tracing changes in the single model and the data using the propagation path of changes in par 043: “Further, the weighting factor can be a fixed value that is used throughout the network or can be set by the user nodes on a per message basis. Other criteria such as a maximum number of forwards or a maximum elapsed time can also be incorporated into the determination that is made by each user node receiving the request.” Tracing is taught by the maximum number of forwards or maximum elapsed time.
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the proximity goal abstraction node student attendance teaching of Lyras with the propagation path compare (by taking different paths) teaching of Vincent because Vincent teaches that users can have enhanced privacy with these teachings in a peer to peer network, see par 010, which allows for an alternative to a centralized network. As more privacy increases security while providing network benefits, one would be motivated to modify Lyras with Vincent.
Per claim 72, Lyras, Colecchia, Vincent, and Shekhar teach the limitations of claim 68, above. Lyras does not teach the propagation path of changes is implemented based on context, a cause-and-effect model, propagation logic, or a propagation model.
Vincent teaches the propagation path of changes is implemented based on context, a cause-and-effect model, propagation logic, or a propagation model n par 043: “Further, the weighting factor can be a fixed value that is used throughout the network or can be set by the user nodes on a per message basis. Other criteria such as a maximum number of forwards or a maximum elapsed time can also be incorporated into the determination that is made by each user node receiving the request.” This teaches a propagation model or propagation logic (rules that must be followed).
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the proximity goal abstraction node student attendance teaching of Lyras with the propagation path compare (by taking different paths) teaching of Vincent because Vincent teaches that users can have enhanced privacy with these teachings in a peer to peer network, see par 010, which allows for an alternative to a centralized network. As more privacy increases security while providing network benefits, one would be motivated to modify Lyras with Vincent.
Per claim 78, the limitations of claims 75 and 68 are taught by Lyras, Colecchia, Vincent and Shekhar, above. Lyras does not teach at least two computing apparatuses
Vincent teaches at least two computing apparatuses in par 048: “Because resource requests are forwarded through one or more other user nodes rather than being sent directly to the server node, the third embodiment offers privacy to requesting user nodes. The actual user node requesting a resource remains anonymous to the server node, so the server node cannot keep track of which users are sharing (or even requesting) which resources. As in a conventional viral network, in the third embodiment only a user node that actually provides the resource has knowledge of the resource sharing and the identity of the requesting node. Further, unlike a conventional viral network, in the third embodiment the use of a publish-subscribe messaging infrastructure layer allows for efficient resource discovery in a network having a very large number of user nodes. Thus, the present invention allows scalability to be achieved in a decentralized network while enhanced user privacy is maintained.”
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the proximity goal abstraction node student attendance teaching of Lyras with the propagation path compare (by taking different paths) teaching of Vincent because Vincent teaches that users can have enhanced privacy with these teachings in a peer to peer network, see par 010, which allows for an alternative to a centralized network. As more privacy increases security while providing network benefits, one would be motivated to modify Lyras with Vincent.
Claim(s) 73 and 74 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lyras, US PGPUB 20140330616 A1 (“Lyras”), in view of Colecchia et al., US Pat No 7647335 B1 (“Colecchia”), further in view of Vincent, US PGPUB 20020165948 A1 (“Vincent”), further in view of Shekhar US PGPUB 20140058674 A1 (“Shekhar”), further in view of Siebel et al., US PGPUB 20170006135 A1 (“Siebel”).
Per claim 73, Lyras, Colecchia, Vincent, and Shekhar teach the limitations of claim 68, above. Lyras does not teach splitting the single model into at least one smaller model; embedding the smaller model into the computing apparatus; and replacing the single model with the smaller model in the computing apparatus.
Siebel teaches cyberphyscial software development platform. See abstract.
Siebel teaches splitting the single model into at least one smaller model; embedding the smaller model into the computing apparatus; and replacing the single model with the smaller model in the computing apparatus in par 0235: “n at least one embodiment, batch analytics processing utilizes Map reduce, a best-practice programming model for improving the performance and reliability of processing-intensive tasks through parallelization, fault-tolerance, and load balancing. A Map reduce processing job splits a large data set into independent chunks and organizes them into key-value pairs for parallel processing. This parallel processing improves the speed and reliability of the cluster, returning solutions more quickly and with greater reliability. Map reduce processing utilizes a map function that divides the input based on the specified batch size and creates a map task for each batch. An input reader distributes those tasks to worker nodes to perform reduce functions. The output of each map task is partitioned into a group of key-value pairs for each reduce. The reduce function collects various results and combines them to answer the larger problem that the job needs to solve. Map output results are “shuffled,” which means that the data set is rearranged so that the reduce workers can efficiently complete the calculation and quickly write results to storage via the data services component 204. Batch processing services, such as Map reduce, may be used on top of the types of a data abstraction layer provided by the data services component 204.”
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the abstract proximity goal cause and effect teaching of Lyras with the splitting and replacing teaching of Siebel because Siebel teaches in par 0237 that “This parallel processing improves the speed and reliability of the cluster, returning solutions more quickly and with greater reliability.” Because increased reliability and speed would return solutions more quickly and reliably, one would be motivated to modify Lyras with Siebel.
Per claim 74, Lyras, Colecchia, Vincent, Shekhar, and Siebel teach the limitations of claim 73, above. Lyras does not teach redistributing a smaller model and data embedded either on the replaced second computing apparatus or on the absent second computing apparatus, to the computing apparatus using the traced changes; embedding the redistributed smaller model and data on the computing apparatus; and processing the embedded data according to the embedded redistributed smaller model using the central processing unit on the computing apparatus for connecting nodes and transferring new values along the propagation path of changes in par 0238: “A Map reduce job may include a map function that divides input based on the specified batch size and creates a map task for each batch. The input reader 1102 distributes those tasks to corresponding worker 1104 nodes. The output of each map task is partitioned into a group of key-value pairs for each reduce. A reduce function collects the various results and combines them to answer the larger problem that the job needs to solve. Map output results (e.g., as performed by the workers 1104) are shuffled by the shuffler 1106, which means that the data set is rearranged so that the workers 1104 can perform a reduce function efficiently to complete the calculation. The output writer 1108 writes results to a data services layer. In one embodiment, retrieving or writing data to the data storage nodes may be done via one or more types of a type layer or abstraction layer provided by the data services component 204. The data for processing may be obtained from one or more data storage nodes and results of the calculation may be written to a service bus or stored in one or more data storage nodes 1110.” See also par 0245 where stream services are performed at least once or exactly once which teaches transferring new values on the propagation path of changes.
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the abstract proximity goal cause and effect teaching of Lyras with the splitting and replacing teaching of Siebel because Siebel teaches in par 0237 that “This parallel processing improves the speed and reliability of the cluster, returning solutions more quickly and with greater reliability.” Because increased reliability and speed would return solutions more quickly and reliably, one would be motivated to modify Lyras with Siebel. See also taught motivation in par 0245 that “the stream services provide scalability by using parallel calculations that run across a cluster of machines. The stream services may provide fault-tolerance by automatically starting workers services or nodes when they fail or die.” Fault tolerance would motivate one ordinarily skilled as it would prevent failure.
Therefore, claims 68-78 are rejected under 35 USC 103.
Response to remarks:
35 USC 112 – New matter,
The substance of the remarks was fully considered and addressed above in the rejection.
35 USC 112 – indefiniteness
This particular indefiniteness issue was resolved by amendment.
35 USC 101
This argument is considered and is persuasive for claims 76-77 as the method of claim 68 requires hardware (not pure wireless signal) and therefore as these additional elements of claim 68 are incorporated into claims 76-77, the claim is not a transitory signal only.
35 USC 103
Applicant’s arguments per Colecchia are unpersuasive. The scope of retrieving status value “without” performing a retrieval query in a database is taught by Colecchia which does not teach database here. Applicant appears to argue that the word query cannot be used but this is not the case, query can be used, so long as it is not a retrieval query in a database. As this is unpersuasive the rejection is maintained.
Applicant’s arguments per Vincent are persuasive per comparing paths but due to the RCE filing wherein further search and consideration of the amendment is required, new art is found and applied. Therefore the argument is moot.
Conclusion
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/RICHARD W. CRANDALL/Primary Examiner, Art Unit 3619