DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2025-06-08 has been entered. The status of claims is as follows:
Claims 1-20 remain pending in the application.
Claims 1-20 are amended.
Response to Arguments
Applicant has amended the claims, and pointed out their right to act as a lexicographer. No specific arguments against the previously applied combination of prior art references have been presented.
Examiner wishes to assist the Applicant in moving prosecution forward, and as such is compelled to caution the Applicant that while Applicant is certainly allowed to act as one’s own lexicographer, one should be aware of the following: examples of what some element includes or may be should not be construed as a strict definition of what some element is.
Examiner also cautions Applicant that a description of what some element achieves is not a strict definition of what some element is.
Claim Interpretation
An example of “what some element achieves is not a strict definition of what some element is” is “Dynamic Algebraic Causal Subsequence”. It is never defined in the Specification. Spec [00107] states: “The latent feature learner 520 may initiate self-organized cognitive algebraic neural network structure (SCANN), a multi-layered multi-dimensional structure, and dynamic algebraic causal subsequence (DACS) to arrange information to confirm, detect anomalies and rank order signal content of the current activity, for the multiple options, for e.g. by increasing traffic to assign additional resources to the knowledge processor 116 and the access device 104 for the user.” Examiner notes that “to arrange information to confirm, detect anomalies and rank order signal content of the current activity” is not a definition of DACS – it is merely a description of the intended result of a combination of SCANN, a multi-layered multi-dimensional structure, and DACS.
Furthermore, Specification [00133-00134] also states that DACS “DACS to use shortest path algorithms and longest path problems using various methods including, but not limited to, simulated annealing, cellular automata, dynamic programming, molecular dynamics, stochastic gradient descent ("SGD"), quasi-Newton, optimal tree-search, sequential Monte Carlo, etc. to find globally optimal space or station. Furthermore, Specification [00157] discloses: “The DACS method 700 may generalize conditional dependency programming and define sets, for example, as X, A and B that are finite.” Specification [00158] also discloses “the number of levels in the corresponding DACS method”, “total number of nodes in DACS.” Thus, it appears that DACS has no metes or bounds, and is capable of performing various algorithms, and is capable of producing various results. An internet search has also yielded no results for “Dynamic Algebraic Causal Subsequence”, and thus it is not a known term of the art. As there appears to be no explicit meaning or definition for this term whatsoever, the broadest reasonable interpretation of DACS is nothing more than “any operation that results in the result described in the claimed limitation”. In other words, in the limitation “the dynamic algebraic causal subsequence (DACS) learner is to obtain, allocate and assign sequence and subsequence that has a topological ordering for single individual” – any action that achieves “obtain, allocate and assign sequence and subsequence that has a topological ordering” will be treated as reading upon “DACS”.
Regarding “quantum candidate”, this term is never defined in the Specification. Examiner is interpreting the term as a “an amount of data to be processed.” A “quantum” can merely be defined as “an amount of something”, and a “candidate” is merely something that is eligible to have some action performed upon it. An internet search reveals that the term “quantum candidate” is not a known term of the art.
Remarks
Examiner notes that a significant amount of 112 rejections are hereby issued, as the claims are replete with grammatical errors and, in some limitations, completely incomprehensible to any person of ordinary skill in the art. Examiner notes that addressing these issues can smooth the process by which Examiner and Applicant work in a cooperative fashion to put the claims in better condition for allowance.
As such, Claims 9-16 do not have Prior Art rejections issued. Rather than an admission of patentability over prior art, this is because to properly issue a high-quality prior art rejection is simply not possible, as to do so requires an understanding of the claim. Independent Claim 9 is replete with grammatical errors and run-on limitations with no discernible meaning, such that no person of ordinary skill in the art could reasonably be apprised of what the intention of the claim is. Significant amendments to Claim 9 are required in order for a proper prior art analysis to be performed.
Furthermore, Examiner notes the same as above for Claims 2, 4-8, and 17-20. The claims are sufficiently incomprehensible as to render a proper prior art search impossible.
Examiner notes that Claim 1 also has significant problems with understandability, and has significant 112 rejection issues. However, Examiner has made a special effort to provide a prior art rejection of sufficient quality for Claim 1, with the intention to try to assist Applicant in moving this application forward.
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 1-8 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.
Claim 1 recites the limitation “a neural station integrator”. This term does not appear in the Specification. Thus, Claim 1 is rejected due to new matter.
Claims 2-8 are rejected because they inherit the deficiencies of Claim 1.
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 1-8 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation “wherein data pertains to signal, signal change and transactional data of dynamical perform action with combinatorial, imperfect or asymmetric and incomplete conditions of activity pertains to continuous, frequency of use, and related to automatic and performed action, associated with neural station.” Examiner notes that this is a run-on sentence rife with grammatical defects whose intended meaning is incomprehensible in its current form. Clarification is required.
Claim 1 recites the limitation "such that model each set of vector fields of individual, or machine, as a quantum candidate". There is insufficient antecedent basis for this limitation in the claim. The claim references “each” set of vector fields, but no set of vector fields has been established. It is unknown precisely which “set of vector fields” the term “each” refers to. Furthermore, Examiner notes that it is unclear as to what “of individual, or machine”, is referring to. The limitation requires further clarification.
Claim 1 recites the limitation “a neural station integrator, executable by the processor, the neural station integrator is to arrange a plurality set of flow vector fields represent deterministic law of evolution in time and space complexities, and in its phase space, and mean-fields in continuous-time stochastic process, vectors in topological space, and combine data as a single unit as an integrated unit in multi-layered-multi- dimensional, and combinations thereof, in sequence and subsequence.” Examiner notes that this is a run-on sentence rife with grammatical defects whose intended meaning is incomprehensible in its current form. Clarification is required.
Claims 2-8 are rejected because they inherit the deficiencies of Claim 1.
Claim 2 recites the limitation “a conditional dependency programming constructor, executable by the processor, the conditional dependency programming (CDP) constructor is to construct causal substructure, as vertices, determine cost/loss function and make with full comprehension in monadic and with paired-comparison for allocation and assignment learning for resource, station, workspace, and cell.” Examiner notes that this is a run-on sentence rife with grammatical defects whose intended meaning is incomprehensible in its current form. Clarification is required. For example, what does “make with full comprehension in monadic” mean? What is being paired in a “paired-comparison”. Examiner notes that it appears the multiple distinct ideas are tangled in a single limitation, wherein no person of ordinary skill in the art could reasonably discern the scope of the limitation.
Claim 2 recites the limitation “an abstract of dynamic and active workspace creator executable by the processor, the abstraction of dynamic and active workspace creator is to create a layer for each optimized the choice data, ‘wait’ for more signal data for ‘capacity’ or lack of capacity, and make ‘coverage’ or lack of coverage on the data.” Examiner notes that this is a run-on sentence rife with grammatical defects whose intended meaning is incomprehensible in its current form. Clarification is required. Claim 7 is also rejected for this reason, as it inherits from Claim 2.
Claim 4, as a whole, is incomprehensible, and requires extensive amendment. For example, what is being “erase and write into a chain”, and what data is being allocated in “optimize allocation to the geometry of the data”. What is meant by “block of a behavior”? This term is not defined in the Specification. Clarification is required.
Claim 5, as a whole, is incomprehensible, and requires extensive amendment. For example, this appears to be a tangle of multiple concepts into a single limitation, such that one of ordinary skill in the art could not make sense of it, as in “sheafing data to sets of topological space and stochastic option” – these concepts are not related, but are grouped together in the same clause. What is being “orderly organize for an individual and a group” Clarification is required. Furthermore, Examiner notes that “orderly” is a relative term or term of degree, and the Specification provides no guidance on how one of ordinary skill in the art would ascertain the requisite degree of “orderly”.
Claim 6, as a whole, is incomprehensible, and requires extensive amendment. Examiner cannot make sense of the limitation “find globally optimal space, station and cell in combinatorial, asymmetric, perfect, imperfect, and incomplete information conditions to construct dominated point.” For example, given the “and” here, it is unclear how any “information condition” can be “perfect” and “imperfect”, as these are mutually exclusive. IT is also unclear what is a “combinatorial information condition”, as this is not defined in the Specification. Examiner thought that perhaps the “and” means “each of” (i.e., capable of “find globally optimal space” in each of “combinatorial, asymmetric, perfect, imperfect, and incomplete” information conditions). However, Specification [0134] appears to be inferring coexistence of these conditions (i.e. “perfect and incomplete”).
Specification [0134] states: “The user activity in a group may be combinatorial, imperfect and incomplete information conditions much in the same way as graph probabilities, as formalized, are accessible in random graph. The cognitive operating system 110 may enable, in contrast to arbitrary graphs, DACS to use shortest path algorithms and longest path problems using various methods including, but not limited to, simulated annealing, cellular automata, dynamic programming, molecular dynamics, Stochastic Gradient Descent ("SGD"), quasi-Newton, optimal tree-search, sequential Monte Carlo, etc. to find globally optimal space or station in asymmetric, perfect and incomplete information conditions.”
Thus, it is unclear in Claim 6 what is meant by these 5 types of conditions being considered together.
Furthermore, Claim 6 recites “form a sequence of the vertices such that every edge…” Examiner notes that there is insufficient antecedent basis for these terms in the claim because no vertices nor edges have been established. What is it that is supposed to have vertices and edges? Not even a graph has been claimed in Claim 6 nor in its parent Claim 1, so it is unclear from where one is to derive vertices and edges on which to operate.
Claim 8 recites the limitation “a dynamics engine, executable by the processor, the dynamics engine is to relate the changing conditions in the sequence and subsequence, autonomously change dimensionalities, accelerate or decelerate the speed of information flow, deduce optimal control policies, determine true dimensionalities, and t-dimensional sub-manifold”. Examiner notes that this is a run-on sentence rife with grammatical defects whose intended meaning is incomprehensible in its current form. Clarification is required. For example, the list of what the dynamics engine is “to” do, which is a list of actions, ends with a noun (“and t-dimensional sub-manifold”). What are “true” dimensionalities? This term is not defined in the Specification.
Claim 8 recites the limitation “a classifier, executable by the processor, the classifier is to model risk, the causality, differentiable manifold, maps a tuple, values of decisions, aggregations, optimal control criteria and conditions, and derive optimal control policies for a plurality of choice-set”. Examiner notes that this is a run-on sentence rife with grammatical defects whose intended meaning is incomprehensible in its current form. Clarification is required. For example, the list of what the classifier is “to” do, includes both verbs and nouns (i.e., “differentiable manifold”, “maps a tuple”). The tangling of various concepts together in this limitation does not allow one of ordinary skill in the art to be apprised of the scope of the limitation.
Claims 9-16 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 9 recites the limitation "the flow vector field". There is insufficient antecedent basis for this limitation in the claim. No such “flow vector field” has been established.
Claim 9 recites the limitation "determining a plurality of input and output vectors including parameter vectors that form sequence and subsequence in DACS". There is insufficient antecedent basis for this limitation in the claim. The claim appears to be assuming that something called “DACS” has already been established, and that one should know to form the sequence and subsequence “in DACS”. But DACS is never established, and it is unclear what this limitation means as a result.
Claim 9 recites the limitation “arranging combinatorial, imperfect, or asymmetric and incomplete of partial-complete information condition in multiplex and in a multi-layered multi-dimensional structure in the different layers”. There is insufficient antecedent basis for this limitation in the claim. It is completely unclear as to what is supposed to have “different layers”.
Claim 9 recites the limitation “deriving the continuous emergence of drivers”. There is insufficient antecedent basis for this limitation in the claim. No “continuous emergence of drivers” has been established.
Claim 9 recites the limitation “determining utility score for the plurality of choices”. There is insufficient antecedent basis for this limitation in the claim. Examiner has no idea what a “plurality of choices” is referring to.
Claim 9 recites the limitation “organizing enumeration of sets of a topological space in sequence and subsequence that has a topological ordering, a sequence of the vertices, for individual, as quantum candidate and sheafing systematically tracking each individual’s data attached to group, to form networks.” This is a run-on sentence with no discernible meaning, such that no person of ordinary skill in the art could reasonably comprehend the intention of this limitation. Here, there appears to be several ideas tangled with one another in a single limitation, and no person of ordinary skill in the art could be reasonably apprised of the scope of the limitation. For example, “a sequence of the vertices” is a hanging clause standing alone between commas, with no indication of what is to be done with them. Also, something is undergoing “systematically sheafing”, but it is unclear what, and how this is related to “tracking”, or having data “attached to group, to form networks.”
Claim 9 recites the limitation “determining a saliency ordered set for sequence ordering in structure, compute first-order, transform higher-order design including partially ordered set, sheafing data to open sets of topological space and stochastic option that orderly organized, for an individual and a group, in assignment, allocation, and scheduling a plurality of choices to access device and interfaces.” This is a run-on sentence with no discernible meaning, such that no person of ordinary skill in the art could reasonably comprehend the intention of this limitation. For example, what does “compute first-order” mean? “first-order” what? How is the computing of “first order” related to the rest of the limitation? What data is being “sheafed”? It appears that there are at least 3 or 4 ideas being stated here, but they are so tangled with one another that no person of ordinary skill in the art could be apprised of the scope of the limitation.
Claim 9 recites the limitation “creating new cell data of neural network with differential manifold and multi-dimensional sub-manifold in different layers that do not reveal their true dimensionality and remain unaided”. The meaning of this is unclear, and no definition of “unaided” in this context appears in the Specification, nor is it clear what is means by “do not reveal their true dimensionality.”
Claims 10-16 are rejected because they inherit the deficiencies of Claim 9.
Claim 10, like Claim 6, also recites “every edge from earlier to later in the sequence of the vertices”, for which there is no antecedent basis, as no “vertices” nor “edges” have been established.
Claim 15 recites the limitation “determining representative Lorentzian manifold that determine causal structure from non-totally vicious, chronological relation, causal relation, distinguishing, strongly causal, stable causal, causally continuous, globally hyperbolicity in the assigned neural stations, and in the neural network connector in communication to perform functionalities.” This is a run-on sentence with no discernible meaning, such that no person of ordinary skill in the art could reasonably comprehend the intention of this limitation.
Claim 15 recites the limitation “modeling risk and classifying the causality, differentiable manifold, maps a tuple including values of decisions or aggregations, optimal control criteria and conditions for plurality of choice-set.” This is a run-on sentence with no discernible meaning, such that no person of ordinary skill in the art could reasonably comprehend the intention of this limitation.
Claims 17-20 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 17 recites the limitation “authenticating and securing a connection with interface and one or more access devices that can read data from the one or more neural station and neural connectors with optical security using optical technique”. There is insufficient antecedent basis for this limitation in the claim, as no “neural station” has been established. Examiner is interpreting as “one or more neural station” without the “the”.
Claim 17 recites the limitation “determining forward stochastic process and backward stochastic process for the flow vector field representing deterministic law of evolution, in its phase space and in a set of vector fields”. There is insufficient antecedent basis for this limitation in the claim, as no “flow vector field” has been established. It is completely unclear as to where one is to acquire a “flow vector field” in order to perform “stochastic processes” on it.
Thus, one of ordinary skill in the art could not possibly be apprised of exactly what data a “forward stochastic process” and “backward stochastic process” is being performed on, nor is it clear what “its phase space” is referring to – what is it that has a phase space? Unfortunately, right from the beginning of this Claim 17, one of ordinary skill in the art could not possibly know what is being claimed.
Claim 17 recites the limitation “configuring to transmit to the computing device connected to the network to form interactions with neural station, neural network connectors, that that performs DACS based sequence and subsequence learning sets of a topological space that has a topological ordering, SCANN-based learning and CDP on the action data and generates unaided choice-set on a plurality of choices.” This is a run-on sentence with no discernible meaning, such that no person of ordinary skill in the art could reasonably comprehend the intention of this limitation. What is being “transmitted”? Are “neural network connectors” being transmitted? Or are the “neural network connectors” being used to transmit some data related to the various subsequent actions being performed? No person of ordinary skill in the art would be apprised of the scope of this limitation.
Claim 17 recites the limitation “creating the new cell data of neural network with differential manifold and multi-dimensional sub-manifold in different layers that do not reveal their true dimensionality and remain unaided”. The meaning of this is unclear, and no definition of “unaided” in this context appears in the Specification, nor is it clear what is meant by “do not reveal their true dimensionality.” Furthermore, there is insufficient antecedent basis for “the” new cell data in the claim.
Claim 17 recites the limitation “determining the saliency ordered set for sequence ordering in structure, compute first-order, transform higher-order design including partially ordered set on action, nearest neighbor, optimal controls, the plurality of choice-set, and allocation and assignment of the resources.” This is a run-on sentence with no discernible meaning, such that no person of ordinary skill in the art could reasonably comprehend the intention of this limitation. For example, “compute first-order” what? “First-order” is a modifier, not a standalone noun. Also what is meant here by “nearest neighbor” and “optimal controls”? “Nearest neighbor” is an algorithm run on a collection of data, but what data is one to be running the nearest neighbor algorithm on? Are optimal controls means here as a verb, or a noun? Is one to identify optimal controls, or is one to perform “optimal control” actions? Clarification is required.
Claims 18-20 are rejected because they inherit the deficiencies of Claim 17.
Claim 18 recites the limitation “arranging information in determining sequence ordering, adopt a strategy of ‘retention’ and ‘attrition’ to control the scale, that is retained for continuous or subsequent activities and choices in the plurality of choice-set.” Examiner notes that the meaning of this is unclear: as for “the scale” – what scale? There is insufficient antecedent basis for “the scale”, and it is unclear what this is referring to. Clarification is required.
Claim 20 recites the limitation “successful” in “performing successful connection mechanism”. The term “successful” is a relative term that renders the claim indefinite. Neither the claims nor Specification provide a standard for ascertaining the requisite degree of what constitutes “successful.”
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 and 3 are rejected under 35 U.S.C. 103 as being unpatentable over Nitz et al. (US 2014/0052681 A1; hereinafter “Nitz”) in view of Arbach et al. (“Dynamic Causality in Event Structures”; hereinafter “Arbach”), and further in view of Zhang et al. (“Senz: A Context Awareness Middleware System Used in Mobile Devices”; hereinafter “Zhang”)
As per Claim 1, Nitz teaches a system of networks environment (Nitz [0026]: “The mobile device referred to herein may be implemented as any suitable mobile, handheld, or portable electronic device, including a cellular phone, smartphone, tablet computer, personal digital assistant, laptop computer, portable Internet device”) comprising:
a processor (Nitz [0085]: “As should be appreciated by those skilled in the art, the computing system 210 includes one or more sensors 218, computer hardware 220 (e.g., processor(s), controllers, digital signal processors, memory, I/O systems, etc.)”)
an input-output mode, executable by the processor, the input-output mode is to communicate (Nitz [0085]: “At runtime, the user interface and applications software 212 interfaces with the mobile user directly (using, e.g., various I/O mechanisms) and interfaces with the sensors 218, computer hardware 220, and peripheral devices 222 via the framework/middleware 214 and the systems software 216.”)
wherein the input mode is to assign and allocate data defined by a neural station (Nitz [0049]: “Many types and sources of stored information 118 can be automatically identified and reviewed by the monitoring module 112 to elicit details that may be pertinent to the user's current context, such as calendar information, meetings and appointments, plans and schedules, contacts information (e.g., address books, social media "friends" or "connections" lists, email or message distribution lists, etc.), electronic documents or blog posts recently created, viewed, or edited by the user; voice, text or email messages recently accessed, sent or received; music, pictures or videos recently taken, viewed, downloaded, or listened to, social media posts recently made by the user or received by the user from other persons (e.g., status updates, tweets, wall posts, checkins, etc.), and/or others. As should be apparent to those skilled in the art, the stored information 118 may be obtained from a number of different software applications and/or other electronic sources and stored in any suitable format (e.g., datasets, database tables, data files, etc.). Thus, the stored information 118 may include structured data, unstructured data, or a combination thereof.”
Examiner notes that a “neural station” is described in the Specification for example to merely be an integrated unit that combines various combinations of types of data. Here, the input mode (“monitoring module 112”) assigns and allocates data to a neural station (“stored information 118”) that stores “a combination thereof” of various types of data.)
and the output mode is to connect with at least one access device and an interface that is communicatively coupled (Nitz [0097]: “As noted above, candidate actions may include a variety of different system-generated responses to the current situation and context. As an example, a candidate action may include a notification that the system 100, 200 has already sent a message to other meeting attendees informing them that the user will be late due to traffic. Another candidate action may include automatically sending a notification, instruction, or information to an embedded system or other source of real-time inputs 116 with or without the user's advance approval (e.g., an instruction to a home appliance or entertainment system to turn on or off based on the user's current situation and context or a notification to an in-vehicle system).” Examiner notes that here Nitz describes an output (“candidate action”) that connects with an access device with an interface to the internet because it (“sending a notification”)).
wherein data pertains to signal, signal change and transactional data of dynamical perform action with combinatorial, imperfect or asymmetric and incomplete conditions of activity pertains to continuous, frequency of use, and related to automatic and performed action, associated with neural station (Examiner notes that this limitation is incomprehensible, see 112(b) rejections above. Nitz [0093] discloses: “Having assessed the user's current situation from a location/movement/position perspective, the illustrative method 300 generate one or more possible context scenarios at block 316. As an example, if real-time inputs 116 indicate that the user has arrived at a shopping mall, one possible context scenario may be that the user is going shopping to buy a birthday present for a friend; another context scenario may be that the user is planning to meet a friend at a restaurant or movie theater; and another context scenario may be that the user is headed to a particular store in the mall to return an item previously purchased.” Here, Examiner notes that Nitz discloses data pertains to a signal change as a user’s location has changed. Furthermore, this is “imperfect” as it is a guess as to where the user is going (“one possible context scenario is … another context scenario may be …”). It is continuous and related to a frequency of use because it is tracking the user’s movements in real time. It is related to automatic and performed actions because it automatically sends notifications as shown above in Nitz [0097].)
However, Nitz does not teach a dynamic algebraic causal subsequence learner, executable by the processor, the dynamic algebraic causal subsequence (DACS) learner is to obtain, allocate and assign sequence and subsequence that has a topological ordering for single individual, such that model each set of vector fields of individual, or machine, as a quantum candidate; wherein the processor is further to: sheave multiple set of vector fields of individual, or machine, as sets of a topological space that form groups, as the quantum candidates; form a sequencing learning for the allocation and assignment of resources; a neural station integrator, executable by the processor, the neural station integrator is to arrange a plurality set of flow vector fields represent deterministic law of evolution in time and space complexities, and in its phase space, and mean-fields in continuous-time stochastic process, vectors in topological space, and combine data as a single unit as an integrated unit in multi-layered-multi- dimensional, and combinations thereof, in sequence and subsequence; a neural network connector, executable by the processor, the neural network connector is to form a plurality of neural pathways with a plurality of input and output vectors that send and obtain combined data and in any combination data between one neural station and another neural station, in a multi- layered multi-dimensional structure, and retrieve them for DACS to perform in sequence and subsequence; a probabilities generator, executable by the processor, the probability generator is to determine a prior and posterior probabilities, a plurality of mean- fields, and spin-states including intensity of action per spin to measure maximum likelihood for choices; and an assignor executable by the processor, the assignor is to assign vectors to each point of time and space in muti-layered-multi-dimensional, multiplex, multi-links and multi-degrees that provide a capacity-based assignment of at least one resource to each activity of one or more resources, and exactly one activity to each resource for plane, station, workspace, cell in a manner that total cost of assignment is minimized
Arbach teaches a dynamic algebraic causal subsequence learner, executable by the processor, the dynamic algebraic causal subsequence (DACS) learner is to obtain, allocate and assign sequence and subsequence that has a topological ordering for single individual, such that model each set of vector fields of individual, or machine, as a quantum candidate (Arbach, Page 6, discloses: “Bundle event structures (BESs - among other- were designed to overcome these limitations [16]. Bundles are pairs (X; e), denoted as Xe, where X is a set of events and e is the event pointed by that bundle … A Bundle Event Structure (BES) is a triple
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, where E is a set of events … For a sequence t = e1 … en of events, let t = { e1, …, en } … A trace is a sequence of distinct events t = e1 … en with
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.”
Arbach, Page 7: “A partially ordered set, or poset, is a pair (A;), where A is a finite set and is a partial order over A. Posets are used as a semantic model for several kinds of ESs and also other models of concurrency [19]. For example, and in contrast to mere configurations, if A is a set of events, then the poset (A;) does not only record which events have happened, but the order also captures their precedence relations.”
Arbach, Page 10: “Transition Graphs. For a transition-based ES with a few additional properties, there is a natural embedding into RCESs.”
Arbach, Page 24: “As visualized by the DCES in Figure 9, the order of events may be relevant for the behavior of a DCES.”
Here, Examiner notes that Arbach teaches to obtain, allocate and assign sequence (“sequence … of events”) and subsequence (
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) that is arranged in a topology (“transition graphs”), and in which such topology has an ordering (“partially ordered set”). These topologies comprise vector fields, as a bundle event structure is a “triple
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”, thus representing a vector.
wherein the processor is further to: sheave multiple set of vector fields of individual, or machine, as sets of a topological space that form groups, as the quantum candidates (Arbach, Page 27: “A configuration structure is a pair C = (E;C) with E a set and C P(E) a collection of subsets.”
Here, Arbach discloses sheaving the multiple sets of vector fields, which were shown above to represent a topological space, into groups (“collection of subsets”)).
form a sequencing learning for the allocation and assignment of resources (Arbach, Page 2: “Overview. We study the idea - motivated by application scenario - of events changing the causal dependencies of other events. In order to deal with dynamicity in causality, usually duplications of events are used (see e.g. [10], where copies of the same event have the same label, but different dependencies). In this paper, we want to express dynamic changes of causality more directly without duplications. We allow dependencies to change during a system run, by modifying the causality itself. In this way, we avoid duplications of events, and keep the model simple and more intuitive. We separate the idea of dropping (shrinking) causality from adding (growing) causality and study each one separately first, and then combine them into so-called Dynamic Causality ESs (DCESs). Example. Figure 1 presents an example DCES: In the regular workflow, after plowing and watering, some crop can be planted and finally harvested. Exceptional behavior changes those dependencies: First, rain deletes the necessity of watering for planting. Second, a pest infestation inserts a new precondition - pest control - for harvesting. Note that pest control could also be done prophylactically, but it becomes mandatory after a pest infestation.”
Here, Arbach discloses learning a sequence of events, that is, determining the causality in a chain of events, and using this for the allocation of resources. In the example above, it is used to allocate resources for pest control.
a neural station integrator, executable by the processor, the neural station integrator is to arrange a plurality set of flow vector fields represent deterministic law of evolution in time and space complexities, and in its phase space, and mean-fields in continuous-time stochastic process, vectors in topological space, and combine data as a single unit as an integrated unit in multi-layered-multi- dimensional, and combinations thereof, in sequence and subsequence (Arbach, Abstract: “Event Structures (ESs) address the representation of direct relationships between individual events, usually capturing the notions of causality and conflict. Up to now, such relationships have been static, i.e. they cannot change during a system run. Thus, the common ESs only model a static view on systems. We make causality dynamic by allowing causal dependencies between some events to be changed by occurrences of other events.” Arbach, Page 2, discloses:
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Figure 1: An example Dynamic Causality ES (DCES). Events are represented as dots, dependencies as solid arrows. The fact that an event can insert or delete a dependency is represented by an arrow between the event and the dependency, with a filled arrowhead for insertion and an empty arrowhead for deletion. Initially absent dependencies, which may be added, are dotted.”
Examiner notes that here, Arbach discloses a plurality set of flow vector fields (the figure above shows arrow vectors that represent the flow of events and time) that represent deterministic law of evolution (a chain of causality is a deterministic evaluation of how events evolve over time) in time and space complexities (planting and insect infestations are complex processes that occur over time and in a given space) in a continuous-time stochastic process (the events in the graph happen in real time, which is continuous, and are stochastic because there are probabilities as to whether or not the infestation will occur). Arbach above was shown to show vectors in topologic space, such as the “triple
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”, and combines the data into a single unit (a topological graph) that is multi-layered and dimensional (there are various dimensions of the graph, such as filled versus dotted lines which are different dimensions of aspects of the graph) and on either side of the dotted line can be considered different dimensions) and in sequence and subsequence (as shown above, Arbach discloses subsequence
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an assignor executable by the processor, the assignor is to assign vectors to each point of time and space in muti-layered-multi-dimensional, multiplex, multi-links and multi-degrees that provide a capacity-based assignment of at least one resource to each activity of one or more resources, and exactly one activity to each resource for plane, station, workspace, cell in a manner that total cost of assignment is minimized (Arbach, as shown above, assigns vectors to points of space and time (causality structures) that assign resources (pest control resources) and exactly one activity to each resource (“Second, a pest infestation inserts a new precondition - pest control - for harvesting”). Here, one activity (pest control) is assigned to the resource (the harvest)).
Arbach is analogous art because it is in the field of endeavor of causality determination. It would have been obvious before the effective filing date of the claimed invention to combine the user context determination of Nitz with the event causality determination of Arbach. One of ordinary skill in the art would have been motivated to do so in order to be able to flexibly adapt to changes in the mobile user’s context so that suggestions can be made based on updated causal chains (Arbach, Intro: “Modern process-aware systems emphasize the need for
flexibility into their design to adapt to changes in their environment [22]. One form of flexibility is the ability to change the workflow during the run-time of the system deviating from the default path, due to changes in regulations or to exceptions.”)
However, the combination of Nitz and Arbach does not teach a neural network connector, executable by the processor, the neural network connector is to form a plurality of neural pathways with a plurality of input and output vectors that send and obtain combined data and in any combination data between one neural station and another neural station, in a multi- layered multi-dimensional structure, and retrieve them for DACS to perform in sequence and subsequence; a probabilities generator, executable by the processor, the probability generator is to determine a prior and posterior probabilities, a plurality of mean- fields, and spin-states including intensity of action per spin to measure maximum likelihood for choices; and an assignor executable by the processor, the assignor is to assign vectors to each point of time and space in muti-layered-multi-dimensional, multiplex, multi-links and multi-degrees that provide a capacity-based assignment of at least one resource to each activity of one or more resources, and exactly one activity to each resource for plane, station, workspace, cell in a manner that total cost of assignment is minimized
Zhang teaches a neural network connector, executable by the processor, the neural network connector is to form a plurality of neural pathways with a plurality of input and output vectors that send and obtain combined data and in any combination data between one neural station and another neural station, in a multi- layered multi-dimensional structure, and retrieve them for DACS to perform in sequence and subsequence (Zhang, Page 5, discloses:
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Here, Zhang discloses connections between neural stations in an LSTM, with a plurality of input and output vectors between them, in a multi-layered and multi-dimensional structure.)
a probabilities generator, executable by the processor, the probability generator is to determine a prior and posterior probabilities, a plurality of mean- fields, and spin-states including intensity of action per spin to measure maximum likelihood for choices (Zhang, Page 5, discloses: “We stack a Softmax classifier[12] on top of stacked RBM to label the context activity per the input data sequence. The energy function of the RBM is defined as follows:
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where v and h are the visible units and hidden units; c and b are the biases; W is the weight matrix of each RBM. The posterior probability distribution between the hidden and visible units is a Bernoulli distribution that extends to a sigmoid function in real field.” Here, Zhang discloses a probabilities generator (“posterior probability”) that determines prior and posterior probabilities (one of ordinary skill in the art will appreciate that in Bayesian statistics, a posterior distribution is derived from a prior distribution). Examiner further notes that this also teaches mean-fields and spin-states to determine maximum likelihood in view of Nitz. Primary reference Nitz discloses in [0010]: "In some embodiments, a probabilistic and/or statistical model 138 includes data relating to the likelihood, probability, or degree of confidence or certainty with which inferences or determinations can be made by the inference engine 110, such as data relating to the likelihood that certain physical activities may occur on certain days or at certain times, the likelihood that some activities may logically follow other activities, or the likelihood or probability that certain candidate actions may be relevant to or appropriate in particular situations and/or contexts.” Thus, Nitz and Zhang combined suggest the limitation of a probabilities generator to generate prior and posterior probabilities and spin-states including intensity of action per spin to measure maximum likelihood for choices, as each choice being given a probability amounts to an intensity of the action associated with that probability which is used to determine maximum likelihood, and is thus a spin state.)
Zhang is analogous art because it is in the field of endeavor of applying machine learning to user context. It would have been obvious before the effective date of the claimed invention to combine the user context identification with causality chains of Nitz and Arbach with the neural network approach of Zhang. One of ordinary skill in the art would have been motivated to do so in order to recognize user behavior patterns reliably, accurately, and efficiently (Zhang, Abstract: “By leveraging high-efficiency mobile data transmission method, using high-performance context recognition algorithms, and combining mobile data with online third-party data, this middleware system can recognize various user behavior patterns reliably, accurately and efficiently.”)
As per Claim 3, the combination of Nitz, Arbach, and Zhang teaches the system of networks environment as claimed in Claim 1. Zhang teaches the neural station is a first neural station, and wherein the neural network connector is further to: receive integrated unit data associated with a second neural station; and transmit, to the first neural station, the data pertaining to a dynamical action associated with the second neural station, as an integrated unit in multi- layered-multi-dimensional and in sequence and subsequence (Zhang, Page 5, discloses:
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Here, Zhang discloses a plurality of neural stations with connections between them, which integrate the various data and transmit the data between them, and an LSTM processes sequences and subsequences. The network is multi layered and multi-dimensional.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Zhang with Nitz and Arbach for at least the reasons recited in the rejection to Claim 1.
Prior Art of Record
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Viswanathan et al. (US 2021/0075749 A1) discloses in Abstract: “The present disclosure relates to an intelligent, adaptable, and trainable bot that orchestrates automation, event data integration, and application programming interfaces across multiple applications.”
Smith et al. (US 2013/0297551 A1) discloses in [0047]: “The platform variations of the method of the preferred embodiment can have various applications. As mentioned, the platform enables multiple applications and services to use and/or integrate with the location information and content of the location prediction platform.”
Zhu et al. (“Interactive Context for Mobile OS Resource Management”) discloses in Introduction Para 3: “A user session may involve executions on a number of application and OS tasks. A process (or thread) may perform work on behalf of interactive user sessions and background jobs alternately. Thus tracking the intricate interdependencies between different tasks on the fly is necessary to maintain the correct interactive context.”
Conclusion
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/LEONARD A SIEGER/Examiner, Art Unit 2126
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