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 . The following is a Final Office action. Claims 1-20 are currently pending and have been rejected below.
Response to Amendments
Applicant’s amendments are acknowledged.
Response to Arguments
Applicant’s arguments with respect to 101 rejections have been fully considered but is non-persuasive. Applicant’s arguments are based on an argument that obtaining mobility/GPS data is inherently technical in nature. Examiner notes GPS/mobile tracking technology is not in the claim language. The mobility data is simply obtained and mental process decisions are made based on the mobility data. Thus, 101 Rejection is sustained.
Applicant’s arguments with respect to 103 rejections have been fully considered but is moot in view of new reference Rose (US Patent 9291700).
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, Claims 1-20 are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea.
Step 1 of the Alice/Mayo analysis is directed to determining whether or not the claims fall within a statutory class. Based on a facial reading of the claim elements, Claims 1-20 fall within a statutory class of process, machine, manufacture, or composition of matter.
With respect to Step 2A Prong One of the framework, the claims recite an abstract idea. Claim 1, 16, and 20 include limitations reciting functionality for identifying an end user that exhibits a type of behavior, including:
Obtain an identification of a first plurality of parcels managed by a service user;
Obtain mobility data that comprises an identification of a first user end device...
Determine, using the mobility data, that the first end user device is present at a first geographic location in the plurality of geographic locations...
Identify that a first parcel in the first plurality of parcels has a geographic location that matches the first geographic location;
Map the first end user device to the first parcel;
For individual geographic locations in the plurality of geographic locations other than the first geographic location, identify a parcel in a second plurality of parcels that has a geographic location... Identify a subset of the second plurality of parcels...
Identify a subset of the second plurality of parcels that each comprise a characteristic that corresponds with the type of behavior
Cause information related to the first end user to be updated based on the identification of the subset of the second plurality of parcels...
which is an abstract idea reasonably categorized as
Certain methods of organizing human activity –managing personal behavior (including social activities, teaching, and following rules or instructions); and
Mental processes – as each of the steps above can be performed in the human mind (including an observation, evaluation, judgment, opinion).
Similarly, Claims 2-15 and 17-19 recite operations that further narrow the certain methods of organizing human activity and mental processes.
With respect to Step 2A Prong Two, the claims do not include additional elements that integrate the abstract idea into a practical application. Claim 1 16 and 20 includes various elements that are not directed to the abstract idea under Step 2A Prong One of the framework. These additional elements include memory stores computer-executable instructions, processor, computer. When considered in view of the claim as a whole, Examiner submits that the additional elements are not additional elements that integrate the abstract idea into a practical application because, these elements are generic computing elements performing generic computing functions and amount to mere instructions to apply the abstract idea on a computer under MPEP 2106.05(f).
Claims 3 and 17 the use of the “trained machine learning model” for the inputting and outputting generally links the use of the abstract idea to a particular technological environment or field of use under MPEP 2106.05(h).
Claims 6-7, 12, 18-19, the displaying of a user interface at a user device and displaying a digital advertisement amount to insignificant extrasolution data presenting activities to the judicial exception.
In Claims 6, 10, and 18, the transmitting, is mere data gathering/data exchange and insignificant extrasolution activities which do not provide a practical application to the abstract idea (See MPEP 2106.05(g)).
Claim 11, including information in a file, amounts to insignificant extrasolution data storing activities to the judicial exception
Claims 2, 4-5, 8-11, 13-15, and 20 do not include any additional elements beyond those recited with respect to the claims above. As a result, Claims 2-15, and 17-19 do not include additional elements that would integrate the abstract idea into a practical application under Step 2A Prong Two for the same reasons as stated above with respect to claim 1 16 and 20.
With respect to Step 2B of the framework, the claims do not include additional elements amounting to significantly more than the abstract idea. As noted above, claim 1 16 and 20 include various elements that are not directed to the abstract idea under Step 2A Prong One of the framework. These additional elements include memory stores computer-executable instructions, processor, computer. Examiner submits that the additional elements do not amount to significantly more than the abstract idea because these elements are generic computing elements performing generic computing functions and amount to mere instructions to apply the abstract idea on a computer under MPEP 2106.05(f) and/or recite generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry.
Claims 3 and 17 the use of the “trained machine learning model” for the inputting and outputting generally links the use of the abstract idea to a particular technological environment or field of use under MPEP 2106.05(h) without improving the associated technology.
Claims 6-7, 12, 18-19, the displaying of a user interface at a user device and displaying a digital advertisement when reconsidered under step 2B the presentation does not amount to significantly more because based on case law in MPEP 2106.05(d) transmitting a display, even an updated display, is well-understood, routine and conventional activity.
In Claims 6, 10, and 18 the transmitting is mere data gathering/data exchange and insignificant extrasolution activities which do not provide significantly more to the abstract idea (See MPEP 2106.05(g)); and these limitations are equivalent to receiving/transmitting data and are well-understood routine and conventional which do not provide significantly more to the abstract idea (See MPEP 2106.05(d)).
Claim 11, including information in a file amounts to well-understood routine and conventional activity based on MPEP 2106.05(d) 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;
Further, looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the additional elements individually.
Claims 2, 4-5, 8-11, 13-15, and 20 do not include any additional elements beyond those recited with respect to the claims above. As a result, Claims 2-15 and 17-19 do not include additional elements amounting to significantly more than the abstract idea under Step 2B for the same reasons as stated above with respect to claim 1 16 and 20.
Accordingly, Claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 of this title, 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 1-4, 6-9, and 11-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gross (20230419262) in view of Fox (20190378348) in view of Rose (US Patent 9291700).
Regarding Claim 1, Gross discloses: A system for identifying an end user that exhibits a type of behavior, the system comprising:
memory that stores computer-executable instructions; and a processor in communication with the memory, wherein the computer-executable instructions, when executed by the processor, cause the processor to (0113-0114 – processors; computer-executable instructions stored on non-transitory computer-readable media or medium)
obtain an identification of a first plurality of parcels managed by a service; obtain mobility data that comprises an identification of a first end user device and an identification of a plurality of geographic locations visited by the first end user device;
(Figure 1, 0068 - adaptive mapping and travel planning services with AM computing device 102 managing user data and optimal travel plans (ie merchants); 0039, 0051, 0079 – different users with different historical activities/locations within telematics data;
determine, using the mobility data, that the first end user device is present at a first geographic location in the plurality of geographic locations; identify that a first parcel in the first plurality of parcels has a geographical location that matches the first geographical location; map the first end user device to the first parcel (0037, 0039, 0051, 0054-collecting one user’s telematics data that identifies a merchant (ie. location/parcel) the user frequently travels to)
for individual geographic locations in the plurality of geographic locations other than the first geographic location, identify a parcel in a second plurality of parcels that has a geographic location that matches the respective geographic location (0027, 0039, 0051 – identifying other locations (parcels) along the route to the merchant)
identify a subset of the second plurality of parcels that each comprise a characteristic that corresponds with the type of behavior (0037, 0039, 0054 - frequent travel to the same merchant (e.g., grocery store, coffee shop, etc.) at similar times using the same mode of transportation along similar routes (characteristic) based on the telematics data, may be recorded to the user's historical data)
and cause information related to a first end user of the first end user device to be updated based on the identification of the subset of the second plurality of parcels. (0037, 0040– tasks parameters/scores within task model updated based on user history, user preferences, and user-identified tasks)
Gross does not explicitly state the parcels are managed by a service user (ie. entity). Fox discloses a company database that stores locations visited by its employees
[0089] ... Optionally, the historical data comprises at least one of schedule-data of the at least one previous activity, previous sales information, previous locations visited, time associated with visit of the previous locations. Furthermore, the term “historical data” used herein relates to previous activities carried out by the user. Optionally, the historical data relates to a periodically stored data associated with activity performed by the user. In an instance, the historical data of a user may include data related to locations that are visited by the user. In such an instance, the historical data may also include data related to time of visit and nature of activity performed by the user at the visited locations. In an example, the user ‘ABC’ has visited 30 different places at different time slots. The historical data may include all the places visited by the user ‘ABC’ in the past along with the time of each visit....In yet another example, the historical data may also include all the previous locations visited by the user along with the time of visit for day-to-day activities such as lunch, dinner, movies and so forth. It will be appreciated that such, client-data, historical data and schedule-data may be received by the database-server from an existing company database or customer relationship management software.
0096- the first user may be an employee of a sales company and the second user is a manager of the first user. Since the second user may be managing a plurality of users
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to associate Fox’s service user to Gross’s service, helping a company “aid in increasing the productivity of the user (thereby, enabling to increase a profit for the second user)...help identify patterns associated with performing tasks by the user...and manage the user's schedule accordingly (such as, by planning breaks for the user in-between activities), to further increase the productivity of the user and consequently, further increasing the profit....” (0057)
Gross does not explicitly state: “determine, using the mobility data, that the first end user device is present at a first geographic location in the plurality of geographic locations for a time that is longer than a time at which the first end user device is present at any other geographic location in the plurality of geographic locations during a first time period; identify that a first parcel in the first plurality of parcels has a geographic location that matches the first geographic location; map the first end user device to the first parcel; for individual geographic locations in the plurality of geographic locations other than the first geographic location, identify a parcel in a second plurality of parcels that has a geographic location that matches the respective geographic location” (Under BRI- “present...for a time that is longer than a time at which the first end user device is present at any other geographic location...” is interpreted as a highest ‘presence frequency” (ie. highest visitation frequency) of a location/parcel compared to all other location/parcels during a time period;
Abstract - Mobile devices determine the coordinates of their locations during a period of time using a location determination system, such as a global positioning system. The coordinates are converted to cell identifiers to look up corresponding residential parcels that have been visited by the mobile devices. A server generates a visitation data set for each residential parcel visited by each mobile device, including different types of frequencies of the mobile device visiting the residential parcel (e.g., night, weekend). A server filters the residential parcels based on visitation frequencies to identify home candidates and then further filters the home candidates based on the count of mobile devices having the home candidates. A home parcel, and thus its address, is identified from the filtered home candidate(s) for each mobile device...
13(28)-14(59) - The identities of the residential parcels that contain the locations are determined or looked up for the locations of the mobile devices. The timing of the locations in the respective residential parcels is used to determine different visitation frequencies; and the server (187) selects a residential parcel as the home of the mobile device (109) based on the visitation frequencies, and thus determines the home address of the mobile device (109).
18(17-27, 52-67) - The computing apparatus computes (365), for each residential parcel (e.g., 101) visited by each mobile device (e.g., 109), a visitation dataset (315), including frequencies of visits of different types identified based on timing of visits, such as the total number of visits (321) in a predetermined period of time....when the filtering is at a higher level, the score is based on the number (321) of visits.) It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to associate Rose’s highest visitation/presence time to Gross’s in view of Fox’s tracked locations/parcels of a device, helping identify a user’s home location/parcel out of multiple locations/parcels based on a highest visit frequency (Abstract, 18(66-67))
Regarding Claim 2, Gross discloses: The system of Claim 1, wherein the computer-executable instructions, when executed, further cause the processor to:
obtain supplemental data that identifies an action performed by the first end user that corresponds with the type of behavior; (0037 - a user profile may be generated based upon the user preferences and history of activities performed. For example, the user may input a task to accomplish, indicate that the task is a regular task that is repeated at regular intervals, indicate a time period to accomplish the task, and identify a geographic location where the task is to be accomplished. In some embodiments, the user may further select a mode of transportation. Additionally or alternatively, the user may identify a ranking and/or rating for accomplishing the task. For example, traveling to work may be designated a highly required task while visiting a mother-in-law may be regarded as a low priority task.) and
cause information related to a first end user of the first end user device to be updated based on the identification of the subset of the second plurality of parcels and reception of the supplemental data that identifies the action performed by the first end user that corresponds with the type of behavior. (0037, 0040– tasks parameters /scores within task model being updated based on user preferences, and user-identified tasks)
Regarding Claim 3, Gross discloses: The system of Claim 1, wherein the computer-executable instructions, when executed, further cause the processor to provide at least one of the mobility data associated with the first end user device or parcel data as an input to a trained artificial intelligence model, wherein providing at least one of the mobility data associated with the first end user device or the parcel data as the input to the trained artificial intelligence model causes the trained artificial intelligence model to output a propensity score that represents a likelihood that the first end user is engaging in an action that corresponds with the type of behavior. (0065 – machine learning is used by task models;
0040 – ... the model may identify regular trips to the same grocery store. If the user has not made such a trip recently, the model may increase a parameter (e.g., a task score) causing the travel plan to include a trip to the grocery store. In another example, the model may decrease a parameter if travel to a particular destination has not occurred after a period of time. For example, if a user has not travelled to a hobby store for a period of time, the likelihood that the user desires travel to the hobby store may be reduced and the task may receive a low weight or task score and/or may not be offered. As such, a weighting and/or task scoring system (propensity score representing a likelihood) may be used as part of the task model.)
Regarding Claim 4, Gross discloses: The system of Claim 3, wherein the computer-executable instructions, when executed, further cause the processor to cause the information related to the first end user of the first end user device to be updated with the propensity score. (0037, 0040– tasks parameters/scores updated within task model based on user history, user preferences, and user-identified tasks;
0040 – ... the model may identify regular trips to the same grocery store. If the user has not made such a trip recently, the model may increase a parameter (e.g., a task score) causing the travel plan to include a trip to the grocery store. In another example, the model may decrease a parameter if travel to a particular destination has not occurred after a period of time. For example, if a user has not travelled to a hobby store for a period of time, the likelihood that the user desires travel to the hobby store may be reduced and the task may receive a low weight or task score and/or may not be offered. As such, a weighting and/or task scoring system (propensity score representing a likelihood) may be used as part of the task model.)
Regarding Claim 6, Gross discloses: The system of Claim 1, wherein the computer-executable instructions, when executed, further cause the processor to:
generate user interface data that, when processed by a user device, causes the user device to render and display a user interface, wherein the user interface depicts the information related to the first end user and a geographic map identifying a location of the first parcel; and transmit the user interface data to the user device. [0069] As shown in optimal travel plan 106, user 104 identifies three tasks to perform at associated waypoints 114, 116, and 122.
Regarding Claim 7, Gross discloses: The system of Claim 6, wherein the user interface further comprises a filter, wherein selection of the filter causes the user interface to display the information related to the first end user in response to a determination that the first end user satisfies a criterion of the filter. (0079- a particular user’s profile filter; In some embodiments, interactive control 208 may be used to change users. User profiles may be used to enable user computing device 202 to generate one or more optimal travel routes for different users.)
Regarding Claim 8, Gross discloses: The system of Claim 7, wherein the filter comprises one of a geography filter, a new end user filter, a low-to-moderate (LMI) income filter, an end user behavior filter, a contact status filter, or a service user team member assignment filter. (0052(bottom), 0079 – changing to a particular user’s profile reflective of user behavior)
Regarding Claim 9, Gross discloses: The system of Claim 1, wherein the computer-executable instructions, when executed, further cause the processor to cause the information related to the first end user to be updated periodically. (0055 - the AM computing device may train a machine learning model using the user's travel behavior over a period of time (e.g., one week, two weeks, one month, etc.) such that the model learns the user's common routes and activities;
0040 - ... If the user has not made such a trip recently, the model may increase a parameter (e.g., a task score) causing the travel plan to include a trip to the grocery store. In another example, the model may decrease a parameter if travel to a particular destination has not occurred after a period of time. For example, if a user has not travelled to a hobby store for a period of time, the likelihood that the user desires travel to the hobby store may be reduced and the task may receive a low weight or task score and/or may not be offered. As such, a weighting and/or task scoring system may be used as part of the task model.
Regarding Claim 11, Gross discloses: The system of Claim 1. Gross does not explicitly state: Fox discloses: wherein the computer-executable instructions, when executed, further cause the processor to include the information related to the first end user in a customer relationship management (CRM) file owned by the service user.
[0089] ...Furthermore, the term “historical data” used herein relates to previous activities carried out by the user. Optionally, the historical data relates to a periodically stored data associated with activity performed by the user. In an instance, the historical data of a user may include data related to locations that are visited by the user. In such an instance, the historical data may also include data related to time of visit and nature of activity performed by the user at the visited locations. In an example, the user ‘ABC’ has visited 30 different places at different time slots. The historical data may include all the places visited by the user ‘ABC’ in the past along with the time of each visit....In yet another example, the historical data may also include all the previous locations visited by the user along with the time of visit for day-to-day activities such as lunch, dinner, movies and so forth. It will be appreciated that such, client-data, historical data and schedule-data may be received by the database-server from an existing company database or customer relationship management software.) (Examiner notes existing company data may be considered a “CRM file” owned by the company because this can file may be accomodated by CRM software) It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Gross’s information related to the first end user to include Fox’s customer relationship management file owned by a service user, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding Claim 12, Gross discloses: The system of Claim 1, wherein the computer-executable instructions, when executed, further cause the processor to cause the first end user device to display a digital advertisement that describes a service offered by the service user. (Figure 4, 0082 - User interface 400 may include a message notification window 402 to display rewards information and/or advertisements.)
Regarding Claim 13, Gross discloses: The system of Claim 12, wherein the first end user is an existing customer of the service user. (Figure 4, 0050 – incentives are offered to the user; “Use of the promoted options may earn the user points that may be used for future travel such as discounted or free entry to mass transit systems. In another example, a longer but lower-risk route may include a rebate, refund, and/or discounted insurance price.”
Regarding Claim 14, Gross discloses: The system of Claim 1, wherein the computer-executable instructions, when executed, further cause the processor to:
determine that the first end user device traveled to a first geographic location based on the mobility data; (0054-The contextual data may include information associated with a common or frequently travelled route. As used herein, a “common” route may include a route the user travels more than a threshold number of times within a certain duration (e.g., within a week, month, or year) and/or a route the user travels with a defined periodicity (e.g., every weekday morning, every Tuesday evening, etc.)(first time period). [0055] The AM computing device may synthesize this contextual data with the travel profile (e.g., the user's common routes, the time(s) the common routes are travelled, the user's speed along the common route, mode of travel, etc.) to generate a contextualized travel profile that includes a model of the user's travel along the common route.
[0057] The task model may be trained according to the contextual profile. The AM computing device may use the output from the trained model to identify an efficient route.
0077 - In the exemplary embodiment, user interface 200 displays, presents, or otherwise conveys to user 104 (shown in FIG. 1) optimal travel plan 106 (also shown in FIG. 1).
Regarding Claim 15, Gross discloses: The system of Claim 1, wherein the characteristic comprises an availability for purchase or rent. (0024, 0059, 0062 – the travelling used a rental vehicle)
Claims 16-20 stand rejected based on the same citations and rationale as the Claims above.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Gross in view of Fox in view of Rose in view of Soto Matamala (20170075910)
Regarding Claim 5, Gross discloses: The system of Claim 1, wherein the computer-executable instructions, when executed, further cause the processor to mask personally identifiable information comprised within the mobility data. Gross does not explicitly state: Soto Matamala discloses: (Figure 5, [0080] In step 510, an app usage record is stored. The app usage record may be stored locally on the device. The record may be encrypted to protect location data. An app usage record may comprise an application identifier corresponding to an application, a usage location corresponding to an execution of the application, and a usage timestamp corresponding to an execution of the application.
[0081] In step 512, an app usage record may be anonymized. In some embodiments, anonymization may occur on the device-side. In some embodiments, anonymization may occur server-side. Anonymization could be performed on both the device and server. In one embodiment, location data is rounded off in the anonymization process. In some embodiments, user identifiable data is removed or masked from the app usage data. This helps ensure privacy preserving rules are met. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to associate San Filippo’s masking to Gross’s in view of Fox’s in view of Rose’s mobility data, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Gross in
view of Fox in view of Rose in view of San Filippo (20140278071)
Regarding Claim 10, Gross discloses: The system of Claim 1. Gross does not explicitly state: San Filippo discloses: wherein the computer-executable instructions, when executed, further cause the processor to transmit the information related to the first end user in response to an application programming interface (API) call. (0034- data created by the trip planner unit may be cached at the server computer for delivery to the client computing device via API calls. For example, the client computing device may be configured with a client application or "app" that periodically issues an API call to the message digest unit requesting delivery of any cached notifications, events, messages or message digests that have been created for the user.) It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to associate San Filippo’s transmission to Gross’s in view of Fox’s in view of Rose’s information related to the first end user, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. [AltContent: rect]
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Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SCOTT ROSS whose telephone number is (571) 270-1555. The examiner can normally be reached on Monday-Friday 8:00 AM - 5:00 PM E.S.T..
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao Wu, can be reached on (571) 272-6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Scott Ross/
Examiner - Art Unit 3623
/RUTAO WU/Supervisory Patent Examiner, Art Unit 3623