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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In regards to claim 1,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim directs to a statutory category – process.
Step 2A – Prong 1: Judicial Exception Recited?
MPEP 2106.04(a)(2)(I) “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.”
Further, the MPEP recites “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation.”
Yes, the claim recites a mental process, specifically:
determining, by the UE, a zone membership in accordance with UE parameters and the zone determination function
This limitation encompasses an evaluation of the received zone determination function and consideration of “UE parameters” to provide an opinion of a zone membership ie which zone the UE belongs to with the aid of a generic computer (BRI of a UE aka User Equipment). The specification provides no details in regards to what elements are considered UE parameters, however, Examiner interprets the UE parameters to be attributes and settings of the UE in light of figure 7 and para. [0090] “As described, the attributes and settings may include, but are not limited to, a geographic location, a default language, or a user interface theme. As an example, each zone 704a or 704b may be based on a geographic location of the participating devices 710a-710f.” wherein a geographic location or default language are considered UE parameters. For example, one of ordinary skills would be able to determine a generic computer’s default language in the system settings and determine a zone membership along consideration to the received function.
selecting the first federated learning model, by the UE, based on the zone membership
This limitation encompasses providing an opinion of a federated learning model based on the previously evaluated zone membership with the aid of a generic computer (UE).
Therefore, the claim recites a mental process.
Step 2A – Prong 2: Integrated into a Practical Solution?
MPEP 2106.05(f) Mere Instructions To Apply An Exception has found simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. The following steps are mere instructions to apply:
A processor-implemented method
a user equipment (UE)
MPEP 2106.05(h) Field of Use and Technological Environment has found limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. The following steps are field of use:
and training the first federated learning model by the UE
The recitation of generically training the learning model on the UE merely indicates a field of use or technological environment in which the judicial exception is performed. This type of limitation merely confines the use of the abstract idea to a particular technological environment and thus fails to add an inventive concept to the claims as the training is recited at the highest level of generality.
MPEP 2106.05(g) Insignificant Extra-Solution Activity has found mere data gathering to be insignificant extra-solution activity. The following steps are insignificant extra-solution activities:
Mere data gathering:
receiving, by a user equipment (UE), a zone determination function based on registering for a federated learning process for training a first federated learning model
(The BRI of the limitation encompasses receiving data using a generic computer)
The additional elements have been considered both individually and as an ordered combination in to determine whether they integrate the exception into a practical application.
The full context of the claim encompasses obtaining data by a generic computer, determining an attribute based on the obtained data and attributes of the generic computer, selecting a generic AI model based on the previous evaluations with the aid of the generic computer, and training the AI model on the generic computer wherein the training is recited at the highest level of generality.
Therefore, no meaningful limits are imposed on practicing the abstract idea.
The claim is directed to the abstract idea.
Step 2B: Claim provides an Inventive Concept?
No, as discussed with respect to Step 2A, the additional limitation is mere data gathering (Insignificant Extra-Solution Activity) and a generic device do not impose any meaningful limits on practicing the abstract idea and therefore the claim does not provide an inventive concept in Step 2B.
Particularly, the claim recites receiving data by generic device.
This has been determined to be insignificant extra-solution activity as found in MPEP § 2106.05(d)(II)(i): Receiving or transmitting data over a network, e.g., using the Internet to
gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary
computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607,
610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP
Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)
(sending messages over a network); buy SAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112
USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);
but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106
(Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how
interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides
the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink."
(emphasis added)).
The additional elements have been considered both individually and as an ordered
combination in the significantly more consideration.
The claim is ineligible.
In regards to claim 2,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim directs to a statutory category – process.
Step 2A Prong 1: The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
tabulating training data based on the zone membership
This limitation directs to a mental process that can be performed in the human mind, by a human using pen and paper, or using a computer as a tool to perform the concept and encompasses arranging information in the form of a table. See MPEP 2106.04(a)(2)(III)
communicating with a federated learning zone manager corresponding to the zone membership
This limitation directs to a mental process that can be performed in the human mind, by a human using pen and paper, or using a computer as a tool to perform the concept and encompasses writing a correspondence (for example, a letter with the aid of pen and paper or an email with the aid of a generic computer) about the zone membership. See MPEP 2106.04(a)(2)(III)
Step 2A Prong 2: The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
Step 2B: The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
In regards to claim 3,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim directs to a statutory category – process.
Step 2A Prong 1: The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
Step 2A Prong 2: The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
further comprising receiving an updated zone determination function from a zone partition keeper
This limitation directs to mere data gathering of insignificant extra-solution activity. See MPEP § 2106.05(g)
Step 2B: The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
further comprising receiving an updated zone determination function from a zone partition keeper
This limitation directs to mere data gathering of insignificant extra-solution activity. See MPEP § 2106.05(g)
This has been determined to be insignificant extra-solution activity as found in MPEP § 2106.05(d)(II)(i): Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
In regards to claim 4,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim directs to a statutory category – process.
Step 2A Prong 1: The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
Step 2A Prong 2: The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
further comprising receiving an updated zone topology graph from a zone partition keeper, the updated zone topology graph indicating updated federated learning zone managers for each zone
This limitation directs to mere data gathering of insignificant extra-solution activity. See MPEP § 2106.05(g)
The specificity directed to the obtained data does not provide significantly more as the data is still merely received.
Step 2B: The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
further comprising receiving an updated zone topology graph from a zone partition keeper, the updated zone topology graph indicating updated federated learning zone managers for each zone
This limitation directs to mere data gathering of insignificant extra-solution activity. See MPEP § 2106.05(g)
This has been determined to be insignificant extra-solution activity as found in MPEP § 2106.05(d)(II)(i): Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
In regards to claim 5,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim directs to a statutory category – process.
Step 2A Prong 1: The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
further comprising periodically determining the zone membership
This limitation directs to a mental process that can be performed in the human mind, by a human using pen and paper, or using a computer as a tool to perform the concept and encompasses performing the determining step periodically (for example, one might perform this task every week day as part of their job). See MPEP 2106.04(a)(2)(III)
Step 2A Prong 2: The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
Step 2B: The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
In regards to claim 6,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim directs to a statutory category – process.
Step 2A Prong 1: The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
determining data samples for training with respect to a specific zone, based on the parameter associated with the sensor data
This limitation directs to a mental process that can be performed in the human mind, by a human using pen and paper, or using a computer as a tool to perform the concept and encompasses an evaluation of the parameter, zone, and stored sensor data to determine data samples. One of ordinary skills in the art would realize this as data analysis. See MPEP 2106.04(a)(2)(III)
Step 2A Prong 2: The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
storing sensor data associated with a parameter
This limitation directs to mere data gathering of insignificant extra-solution activity. See MPEP § 2106.05(g)
Step 2B: The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
storing sensor data associated with a parameter
This limitation directs to mere data gathering of insignificant extra-solution activity. See MPEP § 2106.05(g)
This has been determined to be insignificant extra-solution activity as found in MPEP § 2106.05(d)(II)(iv): Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
In regards to claim 7,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim directs to a statutory category – process.
Step 2A Prong 1: The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
further comprising determining the zone membership in response to a triggering event
This limitation directs to a mental process that can be performed in the human mind, by a human using pen and paper, or using a computer as a tool to perform the concept and encompasses performing the determining step after an observation of a triggering event. See MPEP 2106.04(a)(2)(III)
Step 2A Prong 2: The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
Step 2B: The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
In regards to claim 8,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim directs to a statutory category – process.
Step 2A Prong 1: The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
further comprising selecting a second federated learning model for inference based on the zone membership
This limitation directs to a mental process that can be performed in the human mind, by a human using pen and paper, or using a computer as a tool to perform the concept and encompasses performing the selecting based on the zone membership with the intended use of inferencing. See MPEP 2106.04(a)(2)(III)
Step 2A Prong 2: The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
Step 2B: The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
In regards to claim 9,
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. See MPEP 2106.03:
The claim directs to a statutory category – process.
Step 2A Prong 1: The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
Step 2A Prong 2: The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
storing, by the UE, federated learning data while switching from a first federated learning zone to a second federated learning zone, the switching occurring without network service; and uploading the federated learning data to the federated learning zone manager after resuming network service.
These limitations direct to mere data gathering and post solution activity of insignificant extra-solution activity. See MPEP § 2106.05(g)
Step 2B: The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
storing, by the UE, federated learning data while switching from a first federated learning zone to a second federated learning zone, the switching occurring without network service;
This limitation directs to mere data gathering of insignificant extra-solution activity. See MPEP § 2106.05(g)
This has been determined to be insignificant extra-solution activity as found in MPEP § 2106.05(d)(II)(iv): Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
and uploading the federated learning data to the federated learning zone manager after resuming network service
This limitation directs to post solution activity of insignificant extra-solution activity. See MPEP § 2106.05(g)
This has been determined to be insignificant extra-solution activity as found in MPEP § 2106.05(d)(II)(i): Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
Base claims 10, 19 and 28 (machine) and corresponding dependents (11-18), (20-27) and (29-20) are rejected on the same grounds under 35 U.S.C. 101 as analogous claim 1 and dependents (2-10) as they are substantially similar, respectively, Mutatis mutandis.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-30 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jiang, Xiaopeng, et al. "Federated meta-location learning for fine-grained location prediction." 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. (“Jiang”)
In regards to claim 1,
Jiang teaches A processor-implemented method,
(Jiang, Section I., “We benchmarked the model on Android phones, and the results demonstrate that both training and inference are feasible in terms of execution time and battery consumption.”)
Jiang teaches comprising: receiving, by a user equipment (UE), a zone determination function
(Jiang, Section III., “The fundamental information to predict location is travel direction and speed. The user movement preferences and road characteristics also help the prediction. The GPS trajectories of each user contain this information. FMLL on the phones processes the raw location data to generate the meta-location, which represents trajectories as relative points in an abstract 2D space [receiving, by a user equipment (UE), a zone determination function ie the process in FMLL that determines the abstract 2D space from raw location data]. This section presents the process of meta-location generation and its benefits.”)
Jiang teaches based on registering for a federated learning process for training a first federated learning model;
Examiner interprets the zone determination function in light of the specification, “[0029] According to aspects of the present disclosure, when a device registers to participate in federated learning, the device is provided with the latest zone topology graph along with a "zone determination function." This function accepts a set of parameters from the device and returns the zone information to which the device belongs.”
(Jiang, Section I B., “(1) Initialization. Newly participating phones are required to register with the server [based on registering for a federated learning process for training a first federated learning model] to ensure that the server knows when model gradients uploaded at different times come from the same user. This could further allow the server to remove potential malicious users who may inject fake data into the model. 1”)
Jiang teaches determining, by the UE, a zone membership in accordance with UE parameters and the zone determination function;
(Jiang, Section III B., “To generate the input sequences, FMLL splits the user trajectories into fixed-length sub-trajectories. The length in time of the trajectories is determined experimentally. Each sub-trajectory is transformed into a sequence of relative points in an abstract 2D space. The X and Y coordinates of relative points at time t are determined based on their offsets from the location at previous time step t-1. The location of the very first point in a trajectory session is excluded. A location offset is denoted as ∆Lt =< latt− latt−1, lont lont−1 >. An input sequence at time t that looks back−k steps is denoted as St = (∆Lt−k+1, ∆Lt−k+2, ..., ∆Lt−1, ∆Lt). In its training, FMLL considers all possible k-length sequences, including overlapping sequences.
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The historic region occupancy matrices are extracted from a historic occupancy matrix of the entire space (e.g., a city) [determining, by the UE, a zone membership ie historic region occupancy matrix in accordance with UE parameters ie raw location data (see Section III A.) and the zone determination function ie the process in FMLL that determines the abstract 2D space from raw location data]. FMLL divides the entire space into a grid of fixed-size cells, and each cell corresponds to an element in the historic occupancy matrix. Each element represents the number of visits of the user in its corresponding cell. The matrix represents the occupancy of a bounded region Rt with area A, which is centered at the physical location Lt at time t. Rt is divided into M ×M fixed-size grid-cells, where A and M are predefined constants based on the maximum speed of users and the desired spatial granularity for the prediction. Each historic region occupancy matrix Ht is a M × M matrix, and it is extracted from the historic occupancy matrix for the entire space. Once extracted, this matrix is a meta-location input that does not maintain any relation with the physical locations that it represents. A matrix can implicitly tell if a road exists in a given cell (i.e., non-zero value for the corresponding matrix element) and can also tell if adjacent cells form routes taken frequently by the user.”)
Jiang teaches selecting the first federated learning model, by the UE, based on the zone membership;
(Jiang, Section III C., “C. Meta-Location Output for Prediction Model
The location to be predicted Lt+i is mapped into the region R [selecting the first federated learning model ie prediction model, by the UE, based on the zone membership ie historic regional occupancy matrix; wherein Examiner interprets this specific schema for a prediction model to be selecting wherein it is further based on a ‘zone membership’]. FMLL builds a prediction matrix Yt+i, as shown below:
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where yi,j,t+i is an element of Yt+i, and Ri,j (1 ≤ i, j ≤ M) is a cell in region R. The meta-location output is formulated as a categorical class rather than a numerical value, so that we can set the spatial granularity of the prediction as a constant. Another reason for using categories is that the historic region occupancy matrix does not contain information to predict with spatial granularity beyond the grid-cell size. Overall, the output is a relative grid-cell, which is translated into a physical grid-cell on the user’s phone.”)
Jiang teaches and training the first federated learning model by the UE.
(Jiang, Section III B., “The raw location data of each user is processed on their phone to produce meta-location as two types of inputs for the prediction model: fixed-length sequences of relative points and historic region occupancy matrices of the space considered for prediction. The input sequences contain the speed and direction information of the user trajectories. The occupancy matrices record frequently visited places and the most likely trajectories between these places. The inputs are computed offline (e.g., when the phones are charging) and can be updated over time based on new data to enable re-training [training the first federated learning model by the UE].”)
In regards to claim 2,
Jiang teaches The method of claim 1,
Jiang teaches further comprising: tabulating training data based on the zone membership;
(Jiang, Section III B., “The raw location data of each user is processed on their phone to produce meta-location as two types of inputs for the prediction model: fixed-length sequences of relative points and historic region occupancy matrices of the space considered for prediction. The input sequences contain the speed and direction information of the user trajectories. The occupancy matrices record frequently visited places and the most likely trajectories between these places. The inputs are computed offline (e.g., when the phones are charging) and can be updated over time based on new data to enable re-training [tabulating training data ie converting the raw location data to a table ie historic region occupancy matrix based on the zone membership wherein the historic region occupancy matrix is based on zone membership (the region of Jiang is analogous to the zone of the instant application)].”)
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Jiang teaches and communicating with a federated learning zone manager corresponding to the zone membership.
(Jiang, Section V., “The system architecture of our framework is shown in Figure 3. The framework software runs on a server and on the phones of the users, and it uses federated learning (FL) [2] for training across all users. The FMLL Controller on the phones mediates the communication between the server and the phones. The Meta-Location Generation module on the phones processes the physical location data and generates meta-location for training. The FMLL Training and Prediction module runs on the phones. This module performs local model training on the phones and then submits the model gradients to the server through the Controller [communicating with a federated learning zone manager ie the server corresponding to the zone membership wherein the local models send gradients corresponding to the predicted ‘zone membership’]. The FMLL Aggregator module at the server aggregates the gradients of the local models into a global model, and then distributes this model to the phones. When the OS or apps need a prediction, the Training and Prediction module is invoked. The output of the prediction is a meta-location, which is then converted into a physical location, with help from Meta-Location Generation module.”)
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In regards to claim 3,
Jiang teaches The method of claim 1,
Jiang teaches further comprising receiving an updated zone determination function from a zone partition keeper.
(Jiang, Section III B., “The raw location data of each user is processed on their phone to produce meta-location as two types of inputs for the prediction model: fixed-length sequences of relative points and historic region occupancy matrices of the space considered for prediction. The input sequences contain the speed and direction information of the user trajectories. The occupancy matrices record frequently visited places and the most likely trajectories between these places. The inputs are computed offline (e.g., when the phones are charging) and can be updated over time based on new data to enable re-training [receiving an updated zone determination function ie updated process in FMLL that determines the abstract 2D space from raw location data wherein the updated process is the same process on new data from a zone partition keeper wherein the zone partition keeper is interpreted to be each user].”)
In regards to claim 4,
Jiang teaches The method of claim 1,
Jiang teaches further comprising receiving an updated zone topology graph from a zone partition keeper,
(Jiang, Section III B., “The raw location data of each user is processed on their phone to produce meta-location as two types of inputs for the prediction model: fixed-length sequences of relative points and historic region occupancy matrices of the space considered for prediction. The input sequences contain the speed and direction information of the user trajectories. The occupancy matrices record frequently visited places and the most likely trajectories between these places. The inputs are computed offline (e.g., when the phones are charging) and can be updated over time based on new data to enable re-training [receiving an updated zone topology graph ie historic region occupancy matrices based on new data from a zone partition keeper wherein the zone partition keeper is interpreted to be each user].”)
Jiang teaches the updated zone topology graph indicating updated federated learning zone managers for each zone.
(Jiang, Section III B., ‘The historic region occupancy matrices are extracted from a historic occupancy matrix of the entire space (e.g., a city). FMLL divides the entire space into a grid of fixed-size cells, and each cell corresponds to an element in the historic occupancy matrix. Each element represents the number of visits of the user in its corresponding cell [the updated zone topology graph indicating updated federated learning zone managers the formulating of the meta-data representing for example the number of visits of the user for each zone ie region]. The matrix represents the occupancy of a bounded region Rt with area A, which is centered at the physical location Lt at time t. Rt is divided into M ×M fixed-size grid-cells, where A and M are predefined constants based on the maximum speed of users and the desired spatial granularity for the prediction. Each historic region occupancy matrix Ht is a M × M matrix, and it is extracted from the historic occupancy matrix for the entire space. Once extracted, this matrix is a meta-location input that does not maintain any relation with the physical locations that it represents. A matrix can implicitly tell if a road exists in a given cell (i.e., non-zero value for the corresponding matrix element) and can also tell if adjacent cells form routes taken frequently by the user.”)
In regards to claim 5,
Jiang teaches The method of claim 1,
Jiang teaches further comprising periodically determining the zone membership.
(Jiang, Section IV, “The problem is defined based on the meta-location input and output, defined in Section III. Let St ∈ R2k be the size-k sequence of relative points at time t for a given user. Let Ht ∈ Z+M×M be the historic regional occupancy matrix of the same user, which is a square matrix of order M centered at the user location at time t. Our goal is to predict the relative location of this user Ŷt+i∈ Z2 M ×M for the future ith timestamp [periodically ie ith timestamp determining the zone membership].”)
In regards to claim 6,
Jiang teaches The method of claim 1,
Jiang teaches further comprising: storing sensor data associated with a parameter;
(Jiang, Section III A., “The raw location data is recorded by each phone using the embedded GPS sensor. Let Lt =< latt, lont > denote the latitude and longitude of a user at time t [storing sensor data ie raw location data recorded by the embedded GPS sensor associated with a parameter ex latitude and longitude].”)
Jiang teaches and determining data samples for training with respect to a specific zone, based on the parameter associated with the sensor data.
(Jiang, Section III B., “The raw location data of each user is processed on their phone to produce meta-location as two types of inputs for the prediction model: fixed-length sequences of relative points and historic region occupancy matrices of the space considered for prediction. The input sequences contain the speed and direction information of the user trajectories. The occupancy matrices record frequently visited places and the most likely trajectories between these places. The inputs are computed offline (e.g., when the phones are charging) and can be updated over time based on new data to enable re-training [determining data samples for training with respect to a specific zone, based on the parameter associated with the sensor data].”)
In regards to claim 7,
Jiang teaches The method of claim 1,
Jiang teaches further comprising determining the zone membership in response to a triggering event.
(Jiang, Section V, “When the OS or apps need a prediction [determining the zone membership in response to a triggering event; wherein the triggering event is a determined need for a prediction], the Training and Prediction module is invoked.”)
In regards to claim 8,
Jiang teaches The method of claim 1,
Jiang teaches further comprising selecting a second federated learning model for inference based on the zone membership.
(Jiang, Section III C., “C. Meta-Location Output for Prediction Model
The location to be predicted Lt+i is mapped into the region R [selecting a second federated learning model for inference ie updated prediction model based on the zone membership ie an updated historic regional occupancy matrix provided new data; wherein Examiner interprets this specific schema for a prediction model to be selecting wherein it is further based on a ‘zone membership’]. FMLL builds a prediction matrix Yt+i, as shown below:
PNG
media_image2.png
50
603
media_image2.png
Greyscale
where yi,j,t+i is an element of Yt+i, and Ri,j (1 ≤ i, j ≤ M) is a cell in region R. The meta-location output is formulated as a categorical class rather than a numerical value, so that we can set the spatial granularity of the prediction as a constant. Another reason for using categories is that the historic region occupancy matrix does not contain information to predict with spatial granularity beyond the grid-cell size. Overall, the output is a relative grid-cell, which is translated into a physical grid-cell on the user’s phone.”)
In regards to claim 9,
Jiang teaches The method of claim 1,
Jiang teaches further comprising: storing, by the UE, federated learning data
(Jiang, Section V, “The Meta-Location Generation module on the phones processes the physical location data and generates meta-location for training. The FMLL Training and Prediction module runs on the phones [storing, by the UE, federated learning data ie gradients obtained from training]. This module performs local model training on the phones and then submits the model gradients to the server through the Controller.”)
Jiang teaches while switching from a first federated learning zone to a second federated learning zone, the switching occurring without network service;
(Jiang, Section III B., “The raw location data of each user is processed on their phone to produce meta-location as two types of inputs for the prediction model: fixed-length sequences of relative points and historic region occupancy matrices of the space considered for prediction. The input sequences contain the speed and direction information of the user trajectories. The occupancy matrices record frequently visited places and the most likely trajectories between these places. The inputs are computed offline (e.g., when the phones are charging) and can be updated over time based on new data [while switching from a first federated learning zone to a second federated learning zone wherein it is recorded that the user moves from one region to a new region (recorded in the historic region occupancy matrix), the switching occurring without network service wherein the inputs are computed offline] to enable re-training.
Jiang teaches and uploading the federated learning data to the federated learning zone manager after resuming network service.
(Jiang, Section V, “This module performs local model training on the phones and then submits the model gradients to the server through the Controller [uploading the federated learning data to the federated learning zone manager after resuming network service].”)
Base claims 10, 19 and 28 and corresponding dependents (11-18), (20-27) and (29-20) are rejected on the same rationale under 35 U.S.C. 102(a)(1) as analogous claim 1 and dependents (2-10) as they are substantially similar, respectively, Mutatis mutandis.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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/J.T.T./Examiner, Art Unit 2129
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129
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