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 .
Notice to Applicant
The following is a Non-Final Office action. Claims 1-20 are pending in this application and have been rejected below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/6/24 is being considered by the examiner.
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. an abstract idea) without reciting significantly more.
Step One - First, pursuant to step 1 in MPEP 2106.03, the claim 1 is directed to a method which is a statutory category.
Step 2A, Prong One - MPEP 2106.04 - The claim 1 recites–
A method for identifying local businesses visited by a user, the method, comprising:
receive location data and movement data for a user over a time period;
identifying, based on at least the location data, one or more points of interest (POIs) comprising one or more local businesses, wherein each POI of the one or more POIs is a local business of the one or more local businesses because such POI has less than a threshold number of retail locations;
generating one or more local business metrics for the one or more local businesses, wherein the one or more local business metrics are higher when users, while traveling along one or more routes, visit the one or more local businesses located along the one or more routes;
generating a map overlayed with one or more indicators identifying one or more local business frequencies for the one or more local businesses, wherein the one or more indicators comprise a first indicator corresponding to a first local business frequency of the one or more local business frequencies above a first threshold frequency, wherein the one or more indicators further comprise a second indicator corresponding to a second local business frequency of the one or more local business frequencies above a second threshold frequency higher than the first threshold frequency, wherein the one or more indicators further comprise a third indicator corresponding to a third local business frequency of the one or more local business frequencies above a third threshold frequency higher than the second threshold frequency, and wherein the first indicator, the second indicator, and the third indicator are different from each other;
transmitting, for display on an electronic device of the user, the one or more local business metrics and the map with the one or more indicators.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “certain methods of organizing human activity” (advertising, marketing or sales activities or behaviors; business relations). The claim is receiving location and movement data, identifying “points of interest” (e.g. retail, restaurants, etc), stating if there is less than threshold number of a retailer, the rule results in business being “local”; generating a local business metric based on number of visits for users while they travel along a route, generating a map for frequency indicators for number of visits. This is directed to marketing/sales activities as it is taking the location and movement data, identifying points of interest (e.g. restaurant, coffee shops, etc) for “local” retailers that have less than a certain number of locations, counting/adding the number of visits, and then having indicators (e.g. colors, numbers) for different frequency visits. The “overlay” can be a color, score, icon placed relative to a retailer/coffee shop/restaurant Accordingly, at this time, claim 1 is directed to an abstract idea.
Step 2A, Prong Two - MPEP 2106.04 - This judicial exception is not integrated into a practical application. In particular, the claim recites additional elements that are:
A method for identifying local businesses visited by a user, the method, comprising:
…
generating a map overlayed with one or more indicators identifying one or more local business frequencies for the one or more local businesses, …
transmitting, for display on an electronic device of the user, the one or more local business metrics and the map with the one or more indicators.
Notably, claim 1 does not even require a computer. Examiner suggests as an initial first step, amending claim to require a computer perform each limitation. The last limitation “transmitting, for display on an electronic device of the user” a digital map and the earlier limitation recites the map is “overlayed within one or more indicators.” Even in independent claim 11, where a computer performs each step, the claim is just a processor determining indicators and other values to “generate” a map; the processor and the calculations and generations it makes as claimed in combination are recited at a high-level of generality (i.e., as a generic processor performing each step) such that it amounts no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f). At best it is “field of use” (MPEP 2106.05h) in that the processor is generating a map that has various values/indicators “overlayed” on the map. Unlike the parent cases, there is no practical application for a) display with location and movement data and places that are passed but not visited; b) heat map; and c) overlay indicators. The current claims appear to be just calculating the values for a map, so just doing the calculations here is insufficient for the case to become a “practical application” under Step 2A, Prong 2. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim also fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55. The claim is directed to an abstract idea.
Step 2B in MPEP 2106.05 - The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, even if claim 1 is amended to require a computer in each step, the additional elements in claim 11 of “processor executing instruction”; and transmitting display to an electronic device of a user, are “apply it” on a computer. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. The claim is not patent eligible. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Moreover, the last step in claim 11 (and in claim 1 once computer amended in), “transmitting, for display on an electronic device of a user” is a conventional computer function - See 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.
Independent claim 11 is directed to a device at step 1, which is a statutory category. Claim 11 recites similar limitations as claim 1 and is rejected for the same reasons at step 2a, prong one. At step 2a, prong 2, claim 11 recites a computing device having a processor, computer-readable memory storing instructions executed to perform each step. Similar to the analysis of claim 1 above, which includes “processor executing instructions” if amended in and refers to claim 11, this is just “apply it” on a computer (MPEP 2106.05f) for the same reasons stated above with regards to claim 21 at step 2a, prong 2 and step 2B. The claim is not patent eligible.
Claims 2-10 and 12-20 further narrow the abstract idea.
Claims 2, 12 recite calculating metrics based on averages over a time period which further narrows the abstract idea and uses a mathematical relationship.
Claims 3, 13 narrows the abstract idea by basing metrics on visits over previous week.
Claims 4, 14 narrows the abstract idea by obtaining frequencies in a geographic area for those that visited local businesses.
Claims 5, 15 narrows the abstract idea by explaining that the “route” for user is based on user staying for some duration before starting. This is just explaining the calculation process for the metrics.
Claims 6, 16 narrows the abstract idea by having metrics for the map based on “changes” for different times. Based on Applicant’s [0066] as published, this can be based on a time interval.
Claims 7, 17 recite a number of alternatives for how the business is determined to be “local” or not, such as just “number of locations”, number of employees, revenue, etc., which are all part of the abstract idea.
Claims 8, 18 narrow the abstract idea as it is framed as “comparing location data [of a user] to location of POI based on time and distance for the POI visit to count.
Claims 9, 19 further narrow claim 8 by stating business rules for when a visit is determined which could just be “receiving” data regarding a purchase, check-in/review OR a social media post.
Claims 10, 20 narrows the abstract idea by having further marketing aspects in a reward/discount/indicator to catch a user’s attention for a user to visit.
Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
For more information on 101 rejections, see MPEP 2106.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Voronel (US 2015/0278211) in view of Upstill (US 8,620,579) and Weiss (US 2011/0099046).
Concerning claim 1, Voronel discloses:
A method for identifying local businesses visited by a user, the method executed (Voronel – See par 94 - The customization of the search results 520 reflect that the user may be less concerned with a new Thai restaurant and is likely looking for an established, well-known Thai restaurant or a Thai restaurant frequented by locals.), the method comprising:
receiving location data and movement data for a user (Voronel 2015/0278211 – See par 34 – computer-executable instructions; See par 42 - The location component 222 of the user's computing device 210 can determine and store the location data of the computing device 210. In aspects, the computing device 210 can be a GPS-enabled device. A GPS-enabled device can track its own location and keep a record of locations visited. See par 74 - The familiarity component 258 can determine the user's familiarity level with respect to the area of interest. The familiarity component 258 can store the user's familiarity level with one or more zones, as described above. The zones can be geographic in nature and/or based on points of interest; See par 107 - the familiarity thresholds can be established based on visits to an area within a period of time (e.g., one year, one month));
identifying, based on at least the location data, one or more points of interest (POIs) comprising one or more local businesses, (Voronel – see par 44 - Alternatively or additionally, the plurality of zones can include different points of interest, including entertainment or shopping districts. The zones based on points of interest may have a variable size and shape. For example, the zone for a shopping mall can include the mall proper and the surrounding area; see par 46 area of interest can be “restaurants in Leawood, Kansas”; See par 87, FIG. 3 - Familiarity zone 320 covers the user's commute route between Seattle and Bellevue. Familiarity zone 320 is assigned a medium level of familiarity. While the user is frequently present within familiarity zone 320, the user may not stop within familiarity zone 320 on a frequent basis. This illustrates that the familiarity zone can be assigned based on both a user's frequent presence within a zone and the type of activities that the user is engaged in while in the zone. Driving through an area frequently without stopping to eat, shop, or perform other activities may give the user a high or moderate understanding of roads and routes but may not give the user much chance to understand available restaurants or other businesses. See par 107 - the familiarity thresholds can be established based on visits to an area within a period of time (e.g., one year, one month). The thresholds between the levels may be set to determine or delineate the familiarity levels);
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Voronel discloses users looking for “local” businesses (See par 94) and assessing whether user is “likely already aware of the Local’s Favorite Thai Restaurant” (See par 92). However, Voronel does not explicitly disclose having a threshold number as recited for determining whether a business is a “local” business as the claim recites:
“wherein each POI of the one or more POIs is a local business of the one or more local businesses because such POI has less than a threshold number of retail locations.”
Upstill discloses the limitations (Upstill See Col. 29, lines 34 – 52 - For example, a reference to a particular chain store may exist in numerous query databases or POI databases. If the number of databases which reference a particular POI (or like-named POIs) exceeds a threshold (e.g., if more than half of the databases include a reference to a like-named POI) then a decrement value may be determined (or a decrement function may be applied) and an associated score may be decremented in a query or POI database. In one example, if references to a like-named nationally popular coffee chain are found to exist in databases associated with more than two thirds of all defined geographic areas, the scores associated with each of the references to the coffee chain may be halved, effectively rendering each franchise location “half as interesting.” (thus, national is based on being “more than a threshold”, and local is “less than a threshold); See col. 32, lines 31-40 - Interesting POIs may be distinguished from other POIs which might generally receive more attention by users on a national or regional level,… Information identifying these POIs may be displayed to a user who may be searching for a local neighborhood institution or gem along with… rating or other contextual information that might aid user in selecting a POI to visit;).
Voronel and Upstill in combination disclose:
Voronel discloses a “route” (e.g. commute route 320) and recording that users are stopping to eat or shop (See par 87, FIG. 3). Upstill discloses distinguishing between local and national businesses based on a threshold (See col. 29, 32). However, Voronel and Upstill do not explicitly disclose having a “local business metric” based on what is not visited, as the claim recites:
“generating one or more local business metrics for the one or more local businesses, wherein the one or more local business metrics are higher when users, while traveling along one or more routes, visit the one or more local businesses located along the one or more routes.”
Weiss discloses the limitations (Weiss – See par 29 – As an example of behavior characteristics that may be derived, information about locations visited and trips taken by consumers may be derived. This information may include determining that a consumer visited a point of interest for a particular study, such as a store owned by a sponsor of the study or a competitor of that sponsor. see par 54 - The consumer location data facility 204 may obtain location data for each of the locations through which the consumer 202 passed. The location data obtained by the facility 204 may be passed to the consumer analytics engine 208 for analysis. see par 93 - frequency of visits to a particular POI and POIs that are frequently passed but not visited may be used by the engine 208 to infer strength of brand preferences and loyalties of a consumer 202. For example, if a consumer 202 visits two stores of the same type, but visits one more frequently than the other, the engine 208 may infer that the consumer prefers the more-visited store to the other. see par 108 - For example, the market researchers 230 may wish to know about the identities of consumers 202, such as demographic characteristics for consumers 202 that regularly visit the business, that have visited the business, or that regularly pass by the business but have not visited).
Voronel, Upstill, and Weiss in combination disclose:
generating a map… identifying one or more … business frequencies for the one or more … businesses, wherein the one or more indicators comprise a first indicator corresponding to a first local business frequency of the one or more local business frequencies above a first threshold frequency (Voronel– See par 49, 106 - For example, the familiarity level could be built as a "heat map." "Hot" areas are those where the user visits frequently and "cold" areas are visited infrequently. See par 87 - Turning now to FIG. 3, a map 300 of a user's familiarity zones within the Seattle metropolitan area is provided. Familiarity zone 320 is assigned a medium level of familiarity. While the user is frequently present within familiarity zone 320, the user may not stop within familiarity zone 320 on a frequent basis. This illustrates that the familiarity zone can be assigned based on both a user's frequent presence within a zone and the type of activities that the user is engaged in while in the zone. Driving through an area frequently without stopping to eat, shop, or perform other activities may give the user a high or moderate understanding of roads and routes but may not give the user much chance to understand available restaurants or other businesses).
Voronel discloses having frequencies of visits to business on the map (Voronel See FIG. 3, par 85 – familiarity zones of high, medium, and low; par 106 – frequency of visits to zones; par 73 - tracking locations visited). Voronel discloses users looking for “local” businesses (See par 94) and assessing whether user is “likely already aware of the Local’s Favorite Thai Restaurant” (See par 92). However, Voronel does not explicitly disclose using the “local businesses” within the map.
Upstill discloses the limitations:
generating a map “overlayed with one or more indicators” identifying one or more “local” business frequencies for the one or more “local” businesses, wherein the one or more indicators comprise a first indicator corresponding to a first local business frequency of the one or more local business frequencies above a first threshold frequency (Upstill – See col. 3, lines 31-46 – POIs with higher scores determined to be interesting; information which identifies interesting POIs is provided for display to the user, as is other contextual information; See FIG. 4, col. 24, lines 49-67; col. 25, lines 1-7 – interface 400 has map of search results ; search query results in identification of local POIs that are considered to be interesting; See col. 32, lines 31-40 - Using the POI and/or query databases, local, interesting POIs may be distinguished from other POIs which might generally receive more attention by users on a national or regional level. See Col. 47, lines 32-40– FIGS. 13-17 – illustrate icons which provide information regarding corresponding POI, and indicate popularity of the POI; See Col. 61, lines 49-62 – identify POIs of interest based on users who have visited a particular POI in the past)
Voronel, Upstill, and Weiss disclose:
wherein the one or more indicators comprise a second indicator corresponding to a second local business frequency of the one or more local business frequencies above a second threshold frequency higher than the first threshold frequency, wherein the one or more indicators comprise a third indicator corresponding to a third local business frequency of the one or more local business frequencies above a third threshold frequency higher than the second threshold frequency, wherein the first indicator, the second indicator, and the third indicator are different from each other (MPEP 2144.04(VI)(B) Duplication of parts - the court held that mere duplication of parts has no patentable significance unless a new and unexpected result is produced;
see Voronel– see par 19, 44 – search input associated with geographic area of interest can similar for a “point of interest”; See par 34, 39, FIG. 1 - Aspects of the invention may be practiced in a variety of system configurations, including handheld devices, consumer electronics, general-purpose computers 100, etc.; Device 100 has Presentation component(s) 116 present data indications to a person or other device. Exemplary presentation components 116 include a display device; see par 90 - A great number of gradients between hot and cold are possible. See par 49, 106 - For example, the familiarity level could be built as a "heat map." "Hot" areas are those where the user visits frequently and "cold" areas are visited infrequently. See par 85 - Turning now to FIG. 3, a map 300 of a user's familiarity zones within the Seattle metropolitan area is provided;
Weiss – par 127 - analytics… for POIs visited by each consumer, and frequency of visit to a particular location;
see also Upstill See col. 32, lines 31-40 - Interesting POIs may be distinguished from other POIs which might generally receive more attention by users on a national or regional level,… Information identifying these POIs may be displayed to a user who may be searching for a local neighborhood institution or gem along with… rating or other contextual information that might aid user in selecting a POI to visit; Using the POI and/or query databases, local, interesting POIs may be distinguished from other POIs which might generally receive more attention by users on a national or regional level. see col. 43, lines 43-54 - As another example, the combined scores for POIs may be used to select the top N most popular categories of POIs. As yet another example, the top N most popular POIs of a particular category may be selected based on a user-selected category (e.g., the most popular POIs of a particular category may be displayed after a user selects a category); See Col. 47, lines 32-40– FIGS. 13-17 – illustrate icons which provide information regarding corresponding POI, and indicate popularity of the POI); and
transmitting, for display on an electronic device of the user, the one or more local business metrics and the map with the one or more indicators (Voronel– See par 34, 39, FIG. 1 - Aspects of the invention may be practiced in a variety of system configurations, including handheld devices, consumer electronics, general-purpose computers 100, etc.; see par 40, FIG. 2 – server 240 communicating over network with user’s computing device 210; Device 100 has Presentation component(s) 116 present data indications to a person or other device. Exemplary presentation components 116 include a display device; See par 49, 106 - For example, the familiarity level could be built as a "heat map." "Hot" areas are those where the user visits frequently and "cold" areas are visited infrequently. See par 85 - Turning now to FIG. 3, a map 300 of a user's familiarity zones within the Seattle metropolitan area is provided;
see also Upstill Fig. 3, Col. 17, lines 52-67 – server 302 connected to network 306 to communicate with mobile device 304; Col. 18, lines 1-16 – search engine 342 in server 302 includes a map search module 345; it identifies POIs, POI ratings).
Both Voronel and Upstill are analogous art as they are directed to tracking locations that a user visits and showing geographic recommendations for a geographic recommendations for an area (See Voronel par 49, 106; Upstill Abstract; Weiss par 127 – places of interest visited by consumer relative to travel and commute patterns; as well as brand preferences by places not visited). 1) Voronel discloses users looking for “local” businesses (See par 94) and assessing whether user is “likely already aware of the Local’s Favorite Thai Restaurant” (See par 92). Upstill improves upon Voronel by explicitly disclosing distinguishing “local” points of interest from “chain/national” locations (See col. 29, 32). 2) Voronel discloses having frequencies of visits to business on the map (Voronel See FIG. 3, par 85 – familiarity zones of high, medium, and low; par 106 – frequency of visits to zones; par 73 - tracking locations visited). Upstill improves upon Voronel by explicitly disclosing distinguishing POIs based on popularity and users visiting the POI in the past in the display or with icons. One of ordinary skill in the art would be motivated to further include having a threshold number of geographic areas to determine when a business is no longer “local” to efficiently determine which businesses will be considered “local”, based on Voronel’s disclosure of looking for “local” businesses (See par 92, 94), as well as to further use “local” POIs that are distinguished based on popularity/visits (See col. 32, 47, 61) to efficiently improve upon the frequency portions of the map of Voronel (See par 49, 106, 85, 87). 3) Voronel discloses a “route” (e.g. commute route 320) and recording that users are stopping to eat or shop (See par 87, FIG. 3). Upstill improves upon Voronel by distinguishing between local and national businesses based on a threshold (See col. 29, 32). Weiss improves upon Voronel and Upstill by explicitly disclosing looking at locations, frequencies of visits, and places that are passed (par 29, 54, 93). One of ordinary skill in the art would be motivated to further include considering places that are local that are passed and/or visited to efficiently improve upon locations delineated by local and national based on Upstill.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method of building a heat map of visits of a user in Voronel to further explicitly determine whether a business is local based on a threshold number of geographic areas (e.g. more than two thirds of all defined geographic areas) as well as distinguish local POIs based on popularity/visits as disclosed in Upstill, and to further assess frequencies of locations visited versus those that are passed for brand loyalties as disclosed in Weiss, since the claimed invention is merely a combination of old elements, and in 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 and there is a reasonable expectation of success.
Concerning independent claim 11, Voronel discloses:
A computing device for identifying local businesses visited by a user (Voronel – See par 94 - The customization of the search results 520 reflect that the user may be less concerned with a new Thai restaurant and is likely looking for an established, well-known Thai restaurant or a Thai restaurant frequented by locals; See par 34 – computer-executable instructions; par 40 - Turning now to FIG. 2, an exemplary computing environment 200 is depicted. The computing environment 200 includes a user's computing device 210 and a server 240, which are in communication with one another via a wide area network 235. The computing device 210 can be similar to the computing device 100 described above with reference to FIG. 1. The computing device 210 can include a query component 220, a location component 222, an area of interest component 224, a familiarity component 226, a search result customization component 228, and a digital assistant component 230), the computing device comprising:
one or more processors (Voronel – see par 34 - The invention may be described in the general context of computer code or machine-useable instructions, including computer-executable instructions, such as program components, being executed by a computer or other machine, such as a personal data assistant or other handheld device. See par 40 - Turning now to FIG. 2, an exemplary computing environment 200 is depicted in accordance with one aspect of the present invention. The computing environment 200 includes a user's computing device 210 and a server 240); and
a non-transitory computer-readable memory coupled to the one or more processors, and storing thereon instructions that, when executed by the one or more processors, cause the computing device (Voronel – see par 34 - The invention may be described in the general context of computer code or machine-useable instructions, including computer-executable instructions, such as program components, being executed by a computer or other machine, such as a personal data assistant or other handheld device; See par 36 - Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions; see claim 1).
The remaining limitations are similar to claim 1. Claim 11 is rejected for the same reasons as claim 1.
It would have been obvious to combine Voronel, Upstill, and Weiss for the same reasons as discussed with regards to claim 1.
Concerning claims 2 and 12, Voronel, Upstill, and Weiss disclose:
The method of claim 1, wherein the one or more local business metrics are based on averages over a time period (Voronel – see par 90 - an area the user visits five times a week may be differentiated from an area the user visits six times a week. The familiarity zones may be mapped to a threshold range in the heat map. For example, areas having a location frequency above a threshold may be assigned a certain familiarity range. Thus, an area a user visits five times a week may be grouped into the same familiarity zone as an area visited six times a week;
see also Upstill – see col. 21, lines 56-67, Col. 22, lines 1-3 - a high query term/POI pair score reflects that past users who, while looking at a map of a location at or near a particular geographic area, frequently selected the particular POI as a result of a query which was executed using the query term. A high query term/POI pair score thus indicates that a strong correlation exists between the geographic area, the query term, and the search result).
It would have been obvious to combine Voronel, Upstill, and Weiss for the same reasons as discussed with regards to claim 1. In addition, Upstill improves upon Voronel by explicitly disclosing distinguishing “local” points of interest from “chain/national” locations.
Concerning claims 3 and 13, Voronel, Upstill, and Weiss disclose:
The method of claim 1, wherein the one or more local business metrics are based on visits to the one or more local businesses during an immediately previous week (Voronel – par 89 - zones 322 and 324 were previously assigned a high familiarity level when the user lived or worked in the zones. The current medium familiarity level illustrates that the familiarity zones can be adjusted based on recent location activity. In effect, the familiarity assignment can give more weight to recent location data causing the familiarity zone rating to decay over time when the user spends less time in an area. see par 90 - an area the user visits five times a week may be differentiated from an area the user visits six times a week. The familiarity zones may be mapped to a threshold range in the heat map. For example, areas having a location frequency above a threshold may be assigned a certain familiarity range. Thus, an area a user visits five times a week may be grouped into the same familiarity zone as an area visited six times a week;
see also Weiss par 127 - behavior inferences may detect patterns in one or more manners, for example, the types of places of interest (POIs) visited by each consumer, the time of the day when the POI was visited, day of the week for the visit to the POI, seasonality and duration of each visit to a POI, the speed of travel between POIs, the regularity of each consumer's daily routine and travel, commute patterns, the frequency of visit to a particular location).
It would have been obvious to combine Voronel, Upstill, and Weiss for the same reasons as discussed with regards to claim 1.
Concerning claims 4 and 14, Voronel discloses looking at frequencies and how often one visits in a week (See par 90).
Upstill, and Weiss disclose:
The method of claim 1, wherein generating the one or more local business metrics further comprises:
obtaining frequencies in which the users in a geographic area visited (Voronel – par 90 – visits an area six times a week) the one or more local businesses (Upstill – see col. 21, lines 56-67, Col. 22, lines 1-3 - a high query term/POI pair score reflects that past users who, while looking at a map of a location at or near a particular geographic area, frequently selected the particular POI as a result of a query which was executed using the query term. A high query term/POI pair score thus indicates that a strong correlation exists between the geographic area, the query term, and the search result, suggesting that past users found the search result particularly relevant to that query term and for the geographic area;
see also Weiss par 76 - Similarly, patterns can be detected in how often anchors of particular types are visited together in the same paths or in different paths; see par 140, FIG. 6 – patterns may include frequency of visits; used in output of inferences).
It would have been obvious to combine Voronel, Upstill, and Weiss for the same reasons as discussed with regards to claim 1.
Concerning claims 5 and 15, Voronel discloses a “route” (e.g. commute route 320) and recording that users are stopping to eat or shop or where user location is showing “a significant amount of time” (See par 86-87, FIG. 3). Upstill discloses distinguishing between local and national businesses based on a threshold (See col. 29, 32).
Weiss disclose:
The method of claim 1, wherein the one or more routes are identified by determining that one or more starting locations of the one or more routes are one or more previous locations in which the users stayed for more than a threshold duration (Weiss – see par 70 - Once anchors are identified, the anchor and path classification facility 210 may define a set of anchors as a path. A path is a set of anchors, with a route between them, that a consumer 202 visited in series. A path includes two anchors that are endpoints and may or may not include anchors that are intermediary points, depending on what the consumer was doing and where the consumer stopped; Endpoints include personally-relevant locations for consumers, including places of residence and employment for the consumer 202, but may be anywhere that marks the ultimate destination or end of an outing.).
It would have been obvious to combine Voronel, Upstill, and Weiss for the same reasons as discussed with regards to claim 1. Voronel discloses a “route” (e.g. commute route 320) and recording that users are stopping to eat or shop or where user location is showing “a significant amount of time” (See par 86-87, FIG. 3). Upstill discloses distinguishing between local and national businesses based on a threshold (See col. 29, 32). Weiss improves upon Voronel and Upstill by disclosing path includes where a consumer stopped.
Concerning claims 6 and 16, Voronel, Upstill, and Weiss disclose:
The method of claim 1, wherein generating the map further comprises generating the map based on changes in the one or more local businesses from a first time to a second time (Upstill – see col. 23, lines 42-57 - In response to a user selection of a search result, the databases selected for update may be those associated with the current time in the user's time zone. For example, if the user selects a search result in the morning hours (e.g., 6 a.m. to 12 p.m.), a "morning" POI database or a "morning" query database may be updated. Similarly, one or more "afternoon," "evening," or "nighttime" database may be updated if the user selects a search result during the hours of 12 p.m. to 6 p.m., 6 p.m. to 12 a.m., or 12 a.m. to 6 a.m., respectively. The time period may also refer to a particular day of the week, a particular month, season, daytime or nighttime, year or other identifiable segment of time.).
It would have been obvious to combine Voronel, Upstill, and Weiss for the same reasons as discussed with regards to claim 1.
Concerning claims 7 and 17, Voronel, Upstill, and Weiss disclose:
The method of claim 1, wherein identifying the one or more POIs further comprises one or more of:
obtaining an indication of a quantity of the retail locations for a business of the one or more local businesses (Upstill See Col. 29, lines 34 – 52 - For example, a reference to a particular chain store may exist in numerous query databases or POI databases. If the number of databases which reference a particular POI (or like-named POIs) exceeds a threshold (e.g., if more than half of the databases include a reference to a like-named POI) then a decrement value may be determined (or a decrement function may be applied) and an associated score may be decremented in a query or POI database);
obtaining indications of each of the retail locations for the business and identifying a geographic area which encompasses each of the retail locations for the business (Voronel – see par 101 - the plurality of zones can include different points of interest, including entertainment or shopping districts. The zones based on points of interest may have a variable size and shape. For example, the zone for a shopping mall can include the mall proper and the surrounding area. The surrounding area may be editorially determined to encompass an area that a person is likely to associate with a mall area. Alternatively, the surrounding area may be derived by analyzing location data derived from multiple users over time to generate a location “hot spot” around the mall
see also Upstill - See Col. 29, lines 34 – 52 - For example, a reference to a particular chain store may exist in numerous query databases or POI databases. In one example, if references to a like-named nationally popular coffee chain are found to exist in databases associated with more than two thirds of all defined geographic areas, the scores associated with each of the references to the coffee chain may be halved, effectively rendering each franchise location “half as interesting);
obtaining an indication of a quantity of employees of the business;
obtaining reviews of the business and analyzing content included in the reviews (Voronel – see par 95 - For example, the related content 532 for the top search result 530 can include a relevant content portion 534 and a navigation portion 536. The relevant content portion 534 may include ratings and/or reviews from customers or other relevant information regarding the overall perception of the restaurant. ); or
obtaining an indication of an amount of revenue for the business.
Voronel Upstill discloses some of the alternatives, which is all that is needed).
It would have been obvious to combine Voronel, Upstill, and Weiss for the same reasons as discussed with regards to claim 1.
Concerning claims 8 and 18, Voronel, Upstill, and Weiss disclose:
The method of claim 1, further comprising:
analyzing the location data and the movement data of the user to identify the one or more POIs, wherein:
analyzing the location data and the movement data of the user to identify the one or more POIs (one or more points of interest (POIs) comprising one or more local businesses, (Voronel – see par 44 - Alternatively or additionally, the plurality of zones can include different points of interest, including entertainment or shopping districts. The zones based on points of interest may have a variable size and shape. For example, the zone for a shopping mall can include the mall proper and the surrounding area; see par 46 area of interest can be “restaurants in Leawood, Kansas”) further comprises:
comparing the location data to one or more locations of the one or more POIs (Voronel - see par 46 area of interest can be “restaurants in Leawood, Kansas ;
see also Weiss – See par 139 - In block 604, the locations visited by a consumer may be compared to known geographic locations to determine settings visited by a consumer. The settings may be personally-relevant locations known to be associated with the consumer, such as a place of residence or employment, or known points of interest (POIs) that can be visited by consumers);
in response to identifying that the location data corresponds to a location of one POI of the one or more POIs, determining an amount of time in which the user was located within a threshold distance of the one POI (Upstill – See col. 56, lines 36-49 – rating control for allowing the user rate the identified POI; see col. 61, lines 6-21 -Criteria for a coffee shop category may include criteria requiring that the distance between the POI and the mobile device be less than or equal to ten feet (e.g., coffee shops may be smaller in size than other POIs and therefore to verify that the user is “in” the coffee shop, the distance value to test against may be less than other POIs), that the duration time is greater than one minute (e.g., a person may go in and out of a coffee shop relatively quickly if they are getting their coffee “to go”); and
determining that the user visited the one POI in response to determining that the user was located within the threshold distance of the one POI for more than a threshold amount of time (Upstill – See col. 56, lines 36-49 – rating control for allowing the user rate the identified POI; See col. 60, lines 17-60 – “rating control” provided after satisfaction of criteria - can be based on “duration > 10 min” for a restaurant or retail store as well as being within a distance (<+ 100 ft); criteria for a restaurant category may include criteria requiring that the distance between the POI and the mobile device be less than or equal to one hundred feet, that the duration (or "linger" time) be greater than ten minutes, and that the time of day be either between 8 a.m. and 10 a.m;
see also Weiss – see par 70 - Once anchors are identified, the anchor and path classification facility 210 may define a set of anchors as a path. A path is a set of anchors, with a route between them, that a consumer 202 visited in series. A path includes two anchors that are endpoints and may or may not include anchors that are intermediary points, depending on what the consumer was doing and where the consumer stopped).
It would have been obvious to combine Voronel, Upstill, and Weiss for the same reasons as discussed with regards to claim 1.
Concerning claims 9 and 19, Voronel, Upstill, and Weiss disclose:
The method of claim 8, wherein identifying the one or more POIs further comprises:
receiving an indication that the user visited the one POI by identifying at least one of:
a financial statement indicating the user made a purchase at the one POI (Upstill – See col. 56, lines 36-49 – rating control for allowing the user rate the identified POI; See col. 60, lines 17-60 – “rating control” provided after satisfaction of criteria - can be based on “duration > 10 min” for a restaurant or retail store as well as being within a distance (<+ 100 ft); criteria for a retail store can be distance <25 ft; duration >15 min; and credit charge detected; See col. 61, 6-31 –Criteria for a retail store category may include criteria requiring that the distance between the POI and the mobile device be less than or equal to twenty five feet, that the duration time is greater than or equal to fifteen minutes, and that a credit card charge has been detected);
a check-in or review of the one POI; and
a social media post indicating a presence of the user at the one POI (Voronel – see par 71 - the location component 256 may also include user location data obtained from the user's or others' social posts. For example, a user could be tagged in a post that is associated with an entity having a known location, such as a concert venue. The social posts may include the name of a restaurant or other business having a known location).
It would have been obvious to combine Voronel, Upstill, and Weiss for the same reasons as discussed with regards to claim 1.
Concerning claims 10 and 20, Voronel, Upstill, and Weiss disclose:
The method of claim 1, further comprising:
providing a reward to the user to incentivize the user to visit the one or more local businesses, wherein the reward comprises at least one of (a) one or more indicators (b) one or more discounts and (c) one or more gifts (Voronel – see par 95 - For example, the related content 532 for the top search result 530 can include a relevant content portion 534 and a navigation portion 536. In the same or alternative aspects, the relevant content portion 534 may include any other type of information related to the search result, such as coupons or the night's specials.).
see also Upstill – See col. 52, lines 21-26 - The owner or operator of the POI may pay for the promotional directional icon to be selected for the POI. see col. 68, lines 32-46 - Returning to FIG. 22, information identifying the selected POIs is displayed on a user interface of the mobile device (2214), The interface 2350 displays a directional icon 2352, an address 2354, a recent user review score 2356, … and a discount offer 2360;
see also Weiss – see par 112 - In another example of a real-time response, information about consumers 202 may be presented to an adjustable advertisement such that the advertisement can be adjusted to suit the consumer 202 as the consumer 202 passes by the advertisement; discount coupons for the consumer 202 ).
It would have been obvious to combine Voronel, Upstill, and Weiss for the same reasons as discussed with regards to claim 1.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11,461,792 (parent 16/574,706) and claims 1-20 of U.S. Patent No. 12,079,827 (Parent 17/930,429) in view of Voronel (US 2015/0278211) in view of Upstill (US 8,620,579) and Weiss (US 2011/0099046). Although the claims at issue are not identical, they are not patentably distinct from each other because: independent claim 1 is broader than claim 1 of ‘792 –with “fewer” limitations.
Claim 1 – 18/822,409
Claim 1 – 11,461,792 (App 16/574,706)
A method for identifying local businesses visited by a user, the method comprising:
receiving location data and movement data of the user;
A method for identifying local businesses visited by a user, the method executed by one or more processors programmed to perform the method, the method comprising:
receiving, at one or more processors, location data and movement data for a user over a time period;
analyzing, by the one or more processors, the location and movement data to identify one or more points of interest (POIs) visited by the user during the time period;
identifying, based on at least the location data, one or more points of interest (POIs) comprising one or more local businesses, wherein each POI of the one or more POIs is a local business of the one or more local businesses because such POI has less than a threshold number of retail locations;
for each of the one or more POIs, determining, by the one or more processors, whether the POI is a local business having less than a threshold number of retail locations;
for each local business visited by the user, identifying, by the one or more processors, national/global businesses which the user did not visit along a route to the local business;
generating one or more local business metrics for the one or more local businesses, wherein the one or more local business metrics are higher when users, while traveling along one or more routes, visit the one or more local businesses located along the one or more routes;
generating, by the one or more processors, a local business metric for the user according to the one or more POIs visited by the user during the time period including the local businesses visited by the user and the national/global businesses which the user did not visit along the routes to the local businesses, wherein the local business metric is increased based on the user visiting the local businesses, wherein the local business metric is increased based on the user passing at least one of the national/global businesses along the routes to the local businesses;
generating a map overlayed with one or more indicators identifying one or more local business frequencies for the one or more local businesses,
generating, by the one or more processors, a heat map including a digital map of a geographic area for the user during the time period and a plurality of indicators depicting local business frequencies in which the user visited the local businesses within the geographic area, each indicator of the plurality of indicators depicting a local business frequency in which the user visited a respective local business within the geographic area, the plurality of indicators overlaying locations of the local businesses within the geographic area in the digital map,
wherein the one or more indicators comprise a first indicator corresponding to a first local business frequency of the one or more local business frequencies above a first threshold frequency, wherein the one or more indicators further comprise a second indicator corresponding to a second local business frequency of the one or more local business frequencies above a second threshold frequency higher than the first threshold frequency, wherein the one or more indicators further comprise a third indicator corresponding to a third local business frequency of the one or more local business frequencies above a third threshold frequency higher than the second threshold frequency, and wherein the first indicator, the second indicator, and the third indicator are different from each other
wherein the plurality of indicators includes a first indicator corresponding to a local business frequency above a first threshold frequency, wherein the plurality of indicators includes a second indicator corresponding to a local business frequency above a second threshold frequency, wherein the plurality of indicators includes a third indicator corresponding to a local business frequency above a third threshold frequency, wherein the first indicator, the second indicator, and the third indicator are different from each other; and
transmitting, for display on an electronic device of the user, the one or more local business metrics and the map with the one or more indicators.
providing, by the one or more processors, the heat map for display on a client device of the user.
Although the claims at issue are not identical, they are not patentably distinct from each other because: independent claim 1 is broader than claim 1 of ‘792 –with “fewer” limitations.
Claim 1 – 18/822,409
Claim 1 – 12,079,827 (App 17/930,429)
A method for identifying local businesses visited by a user, the method comprising:
receiving location data and movement data of the user;
A method for identifying local businesses visited by a user, the method comprising: receiving, at one or more processors, location data and movement data for the user;
analyzing, by the one or more processors, the location data and the movement data to identify one or more points of interest (POIs) visited by the user, wherein the one or more POIs comprise one or more local businesses and a plurality of national businesses;
identifying, based on at least the location data, one or more points of interest (POIs) comprising one or more local businesses, wherein each POI of the one or more POIs is a local business of the one or more local businesses because such POI has less than a threshold number of retail locations;
for each POI of the one or more POIs, determining, by the one or more processors, whether the each POI is a local business of the one or more local businesses having less than a threshold number of retail locations;
for each local business of the one or more local businesses visited by the user, identifying, by the one or more processors, one or more national businesses of the plurality of national businesses which the user did not visit along a route to the each local business;
generating one or more local business metrics for the one or more local businesses, wherein the one or more local business metrics are higher when users, while traveling along one or more routes, visit the one or more local businesses located along the one or more routes;
generating, by the one or more processors, a local business metric for the user according to the one or more POIs visited by the user, wherein the local business metric is increased based on the user passing and not visiting at least one of the one or more national businesses along the route to the each local business;
generating a map overlayed with one or more indicators identifying one or more local business frequencies for the one or more local businesses,
generating, by the one or more processors, a digital map of a geographic area for the user and a plurality of indicators depicting one or more local business frequencies in which the user visited the one or more local businesses within the geographic area, wherein the plurality of indicators overlays one or more locations of the one or more local businesses within the geographic area in the digital map,
wherein the one or more indicators comprise a first indicator corresponding to a first local business frequency of the one or more local business frequencies above a first threshold frequency, wherein the one or more indicators further comprise a second indicator corresponding to a second local business frequency of the one or more local business frequencies above a second threshold frequency higher than the first threshold frequency, wherein the one or more indicators further comprise a third indicator corresponding to a third local business frequency of the one or more local business frequencies above a third threshold frequency higher than the second threshold frequency, and wherein the first indicator, the second indicator, and the third indicator are different from each other
wherein the plurality of indicators includes a first indicator corresponding to a first local business frequency of the one or more local business frequencies and above a first threshold frequency, wherein the plurality of indicators includes a second indicator corresponding to a second local business frequency of the one or more local business frequencies and above a second threshold frequency, wherein the plurality of indicators includes a third indicator corresponding to a third local business frequency of the one or more local business frequencies and above a third threshold frequency, wherein the first indicator, the second indicator, and the third indicator are different from each other; and
transmitting, for display on an electronic device of the user, the one or more local business metrics and the map with the one or more indicators.
causing, by the one or more processors, a presentation of the local business metric and the digital map with the plurality of indicators for display on a device.
Claim 1 here is broader than ‘792 - it does not require a “for each local business visited by the user, identifying, by the one or more processors, national/global businesses which the user did not visit along a route to the local business.” Claim 1 here is broader than ‘827– it does not require a “for each local business of the one or more local businesses visited by the user, identifying, by the one or more processors, one or more national businesses of the plurality of national businesses which the user did not visit along a route to the each local business.” The remaining limitations are almost all the same or broader relative to claim 1 of ‘792 or ‘827. Any other differences are obvious in light of the prior art applied above. Claim 11 (independent with a preamble of a “computing device”) here corresponds to claim 11 of ‘792 and claim 11 of ‘827.
Claims 2-6, 10, 12-16, and 20 are obvious in light of the prior art (Voronel (US 2015/0278211) in view of Upstill (US 8,620,579) and Weiss (US 2011/0099046)) as applied in the 103 Rejection above.
Claims 7, 17 correspond to claim 7 of ‘792 (App 16/574,706); claim 7 of ‘827 (App 17/930,429).
Claims 8, 18 correspond to claim 8 of ‘792 (App 16/574,706); claim 8 of ‘827 (App 17/930,429).
Claims 9, 19 corresponds to claim 9 of ‘792 (App 16/574,706); claim 9 of ‘827 (App 17/930,429).
Conclusion
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/IVAN R GOLDBERG/Primary Examiner, Art Unit 3619