Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicants’ submission filed on 12/3/25 has been entered.
DETAILED ACTION
The instant application having Application No. 17466596 has a total of 36 claims pending in the application, of which claims 1-31 and 33-34 have been cancelled.
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 32 and 35-36 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 32 is a process type claim. Therefore, claims 32 and 35-36 are directed to either a process, machine, manufacture or composition of matter.
As per claim 32,
2A Prong 1:
“A context trace indicating a number of points spatially represented on a map and venue information including a plurality of venue locations, each venue location associated with a different venue” The user mentally or with pencil and paper keeps track of the places a person goes and marks them on a map, with date, time, and other contextual data and has additional data on the map which represents various venues on the map.
“identifying a hypothetical visit by the user to a venue, by comparing the context trace stored in the database to a location of the venue” The user mentally or with pencil and paper compares the noted locations of the user to potential venues nearby.
“generating, after identifying the hypothetical visit, a set of activity clusters from historical information from the context trace including location and time, the generating of the set of activity clusters comprising:” The user mentally or with pencil and paper keeps track of previous locations over time and makes clusters from them, including the location and time.
“comparing the historical information from the context trace to the plurality of venue locations to derive a set of venues for the context trace” The user mentally or with pencil and paper looks at the historical information to see which venues might meet the context trace.
“generating inference points including data values for a set of variables indicating an association between the context trace, the venue, and an activity” The user mentally or with pencil and paper sets up the options of what might meet the context trace, the venue, and associated activities.
“generating the set of activity clusters based on the inferences points such that each activity cluster includes substantially similar inference points, wherein at least one piece of multimedia content likely to be of interest to the user is further based on the inference points” The user mentally or with pencil and paper creates the clusters by clustering the inference points, and prepares multimedia content to direct to the customer.
“mapping the hypothetical visit to an inferred activity using the set of activity clusters that are generated” The user mentally or with pencil and paper predicts an activity for that particular visit.
“In response to a user query for content, identifying at least one piece of multimedia content relevant to interests of the user based on the inferred activity” The user mentally or with pencil and paper determines what content will be of interest to the user.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
Device, database (mere instructions to apply the exception using a generic computer component);
“receiving location data from location determination functionality a user’s device”, “presenting said multimedia content to the user” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
“storing, in a database…” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
Device, database (mere instructions to apply the exception using a generic computer component)
“receiving location data of a device of a user”, “presenting said multimedia content to the user” (MPEP 2106.05(d)(II) indicate that merely “sending or receiving data “is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed receiving/streaming steps are well-understood, routine, conventional activity is supported under Berkheimer).
“storing, in a database…” (MPEP 2106.05(d)(II) indicate that merely “Storing or retrieving data in memory” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed storing step is well-understood, routine, conventional activity is supported under Berkheimer).
As per claim 35-36, these claims denote additional mental steps and generic hardware of determining location and providing recommendations to the user based upon location, and are rejected for similar reasons to claim 32
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 32 and 35-36 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As per claim 32, this claim contains the limitation “identifying a hypothetical visit by the user to a venue by comparing the context trace stored in the database to a location of the venue” and then “generating, after identifying the hypothetical visit, a set of activity clusters from historical information from the context trace…” and then later on “mapping the hypothetical visit to an inferred activity using the set of activity clusters that are generated; in response to a user query for content, identifying the at least one piece of multimedia content likely be of interest to the user based on the inferred activity.” Essentially what this claim is calling for is identifying a hypothetical visit BEFORE the clusters are generated, creating the clusters, mapping the hypothetical visit to an activity, and then using that identified activity to provide content to the user. However, the specification explicitly states that the hypothetical visits used to create the clusters are performed off-line and are never used for content recommendation for the user. The “hypothetical visits” used to infer activities and provide recommendations use the generated clusters based upon the created model. This means that the claim is calling for a hypothetical visit to be identified, clusters to be created, use of those clusters to identify an activity for the hypothetical visit, and then use that identified activity to provide content for the user. However, this concept is not supported by the specification as shown below.
Paragraphs 0031 states:
“Inference model database 114 stores one or more activity inference models for one or more users. In some embodiments, inference system 102 performs an off-line preprocessing operation to generate an inference activity model for a user based in part on historical context traces and venue information stored in database 110-112. Furthermore, inference system 102 can perform a runtime query processing operation that uses an activity model to determine an activity 120 for a user.
Paragraph 0032 further elaborate
This shows that the recommendation for the user is based on run-time data, not on off-line preprocessing of creating the model.
Further, Paragraph 0032 states:
[0032] In general, the operation of the inference system can be divided
into two parts: an off-line preprocessing operation and a runtime query processing operation. During the off-line processing, the system collects a user's context traces, identifies hypothetical visits to various venues, and maps the venues to activity types. The system also generates one or more activity inference models, which map a context associated with a location to an inferred activity. Furthermore, the inference system can also use the same inference process to infer specific attributes of an activity such as "eating at a non-smoking venue." During the runtime query processing, the system determines an inferred activity based on the query context and the stored activity inference models.
As discussed above for paragraph 0031, paragraph 0032 further elaborates that the model creation process is run off-line, with recommendations provided to the user during runtime query processing. The current claim requires that the hypothetical visit be identified, clusters be created, inferred activity determined for the same hypothetical visit using the made clusters, and then providing recommendations to the user based on the inferred activity for that hypothetical visit. At no time does the specification disclose identifying a hypothetical visit, creating clusters, using the previously identified hypothetical visit to determine an activity, and then providing content to the user all in one process. At best, the specification discloses the identification of a hypothetical visit, creation of clusters, and then at another time, determining the users activity based on query context and the already created models. This causes this combination of limitations to be new matter, and therefore rejected under U.S.C. 112(a).
As per claims 35-36, these claims are rejected as being dependent on a claim rejected under U.S.C. 112(a) for new matter.
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 32 is rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Bellotti et al (“Activity-Based Serendipitous Recommendations with the Magitti Mobile Leisure Guide”) in view of Zhao et al (“Mining Personally Important Places from GPS Tracks”), Hristova et al (“Ad-me: Wireless Advertising Adapted to the User Location, Device, and Emotions”) and Zheng et al (“Mining Interesting Locations and Travel Sequences from GPS Trajectories”).
As per claim 32, Bellotti discloses, “A method for presenting recommendations” (Pg.1161, particularly C2, last paragraph; EN: this denotes recommending content to the user including things to do, things to read, or the like). “based on contextual information, the method comprising” (Pg.1161, particularly figure 3; EN: this denotes the use of context in making recommendations).
“receiving location data” (Pg.1159, C2, First bullet; Pg.1160, particularly C2, last paragraph; Pg.1161, Figure 3, Context sensing box; EN: this denotes monitoring the user’s current location). “from location determination functionality of a user’s device” (Pg.1161, particularly the System Architecture section; EN: this denotes the system running on a handheld device).
“storing, in a database, a context trace…” (Pg.1159, C2, second paragraph; EN: this denotes keeping track of a user’s behavior, including places visited, communications, and web browsing). “and venue information including a plurality of venue locations, each venue location associated with a different venue” (Pg.1161, particularly C2, last paragraph; Pg.1162, particularly C1, first paragraph; EN: this denotes matching the User’s location to the venues location in order to determine what venues the User might have visited).
“identifying a hypothetical visit by the user to a venue, by comparing the context trace stored in the database to a location of the venue” (Pg.1161, C2, last paragraph; Pg.1162, C1, first paragraph; EN: this denotes tracking the user’s location to determine their activities and potential businesses that they visited. It further denotes that GPS is imprecise, and therefore it predicts locations the person was likely to have visited, not those they necessarily did (i.e. a hypothetical visit)).
“mapping the hypothetical visit to an inferred activity…” (pg.1161, C2, last paragraph; EN: this denotes the type of venue visited relating to the type of activity).
“in response to a user query for content” (Pg.1161, particularly C1, third paragraph; EN: this denotes the user being able to ask for recommendations from the system). “identifying at least one piece of … content” (Pg.1160, C2, last paragraph; EN: This denotes getting numerous recommendations based upon the user’s current situation and profile). “relevant to interests of the user based on the inferred activity” (Pg.1162, particularly the Recommender system; EN: this denotes using the learned preferences of the user via observation over time to make recommendations).
“presenting the … content to the user” (pg.1160, C2, User interface section and Figure 1; EN: this denotes displaying the content to the user).
However, Bellotti fails to explicitly disclose, “indicating a number of points spatially represented on a map”, “generating, after identifying the hypothetical visit, a set of activity clusters from historical information form the context trace including location and time, the generating of the set of activity clusters comprising”, “comparing the historical information from the context trace to the plurality of venue location sot derive a set of venues for the context trace”, “generating inference points including data values for a set of variables indicating an association between the context trace, the venue, and an activity”, “generating the set of activity clusters based on the inference points such that each activity cluster includes substantially similar inference points wherein at least one piece of multimedia content likely to be of interest to the user is further based on the inference points”, “… using the set of activity clusters that are generated” , and “multimedia content.”
Zhao discloses, “generating, after identifying the hypothetical visit, a set of activity clusters from historical information from the context trace including location and time, the generating of the set of activity clusters comprising” (Pg.520-521, particularly section 3.2.1; EN: this denotes making clusters based off of tracking where a person goes via location).
“comparing the historical information from the context trace to the plurality of venue location to derive a set of venues for the context trace” (Pg.520, particularly C2, third to last paragraph; EN: this denotes looking for potential venues like “home” and “work” in relation to the persons location tracking).
“generating inference points including data values for a set of variables indicating an association between the context trace, the venue, and an activity” (Pg.520, particularly C2, third to last paragraph; EN: this denotes looking for potential venues like “home” and “work” in relation to the persons location tracking. Here the activity would be like living somewhere, or where they work or whatever activity goes on at that particular venue).
“generating the set of activity clusters based on the inference points such that each activity cluster includes substantially similar inference points…” (Pg.520, particularly C2, third to last paragraph; EN: this denotes combining the different points into clusters by the algorithm).
“… using the set of activity clusters that are generated” (Pg.520, particularly C2, third to last paragraph; EN: this denotes using clusters to identify different places for the user).
Zheng discloses, “indicating a number of points spatially represented on a map” (Pg.793, particularly C2, the Location History modeling; and Pg.792-793, particularly Definition 4, Location history; Pg.797, particularly section 5.1.2; EN: this denotes monitoring the User’s location via GPS and keeping a history of where they went, as well as these points being able to be represented on a map).
Hristova discloses, “multimedia content” (pg.4, particularly C2, second paragraph; EN: this denotes delivering location based advertisements to the user, including multimedia content).
Bellotti and Zhao are analogous art because both involve location based classification.
At the time of invention it would have been obvious to one skilled in the art of location based classification to combine the work of Bellotti and Zhao in order to use clustering to classify a user’s location.
The motivation for doing so would be to allow the system to “predict not only important and frequent places, but also important and not so frequent places” (Zhao, Pg.525, section 6) or in the case of Bellotti, allow the system to use the well-known classification algorithm of clustering in order to make determinations about the person’s locations and associated activities.
Therefore at the time of invention it would have been obvious to one skilled in the art of location based classification to combine the work of Bellotti and Zhao in order to use clustering to classify a user’s location.
Bellotti and Zheng are analogous art because both involve location based content recommendation.
At the time of invention it would have been obvious to one skilled in the art of location based recommendation to combine the work of Bellotti and Zheng in order to use a map for location history.
The motivation for doing so would be to represent “an individual’s location history (LocH) … as a sequence of stay points (s) they visited with corresponding arrival and leave times” (Zheng, Pg.793, first paragraph) or in the case of Bellotti, allow the location tracking of the user to be represented on a map with details such as arrival and departure times.
Therefore at the time of invention it would have been obvious to one skilled in the art of location based recommendation to combine the work of Bellotti and Zheng in order to use a map for location history.
Bellotti and Hristova are analogous art because both involve location based content recommendation.
At the time of invention it would have been obvious to one skilled in the art of location based content recommendation to combine the work of Bellotti and Hristova in order to make use of multimedia content.
The motivation for doing so would be to “provide context sensitive advertising capability and deliver multimedia presentations” (Hristova, Pg.4, C2, second paragraph) or in the case of Bellotti, allow the system to provide multimedia advertisements for nearby venues to inform the user’s on availability at those venues.
Therefore at the time of invention it would have been obvious to one skilled in the art of location based content recommendation to combine the work of Bellotti and Hristova in order to make use of multimedia content.
As per claim 35, Bellotti discloses, “identifying a plurality of activity types…” (Pg.1161, C2, second paragraph; Pg.1162, Figure 4 and paragraphs 1-3; EN: this denotes giving a distribution of potential activities based upon the user’s personal history including venue visits).
“assigning weights to the plurality of activity types based on a similarity to the context trace” (Pg.1162, particularly Figure 4 and associated paragraphs; EN: this denotes assigning probabilities (i.e. weights) to the various activities).
“generating a context to activity mapping function for the inference model” (Pg.1162, particularly the Recommender System section; EN: this denotes how the model determines recommendations for the user based on the context and the predicted activities).
“mapping the context trace to the identified activity type that has the highest weight” (Pg.1162, particularly the Recommender System section; EN: this denotes how the model determines recommendations for the user based on the context and the predicted activities, including giving recommendations related to the most likely interest of the user).
“wherein the at least one piece of … content likely to be of interest to the user is further based on the identified activity type that has the highest weight” (pg.1160, C2, User interface section and Figure 1; EN: this denotes displaying the content to the user).
Zhao discloses, “identifying a subset of inference points that match the context trace; (Pg.520-521, particularly section 3.2.1; EN: this denotes taking in new data and determining the cluster it belongs to).
“identifying a plurality of activity types based on the subset of inference points” (Pg.522, particularly section 3.3.2; EN: this denotes various activities associated with the locations, such as home, work, school, health clubs, doctors offices, etc).
Hristova discloses, “multimedia content” (pg.4, particularly C2, second paragraph; EN: this denotes delivering location based advertisements to the user, including multimedia content).
Claim Rejections - 35 USC § 103
Claim 36 is rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Bellotti et al (“Activity-Based Serendipitous Recommendations with the Magitti Mobile Leisure Guide”) in view of Zhao et al (“Mining Personally Important Places from GPS Tracks”), Hristova et al (“Ad-me: Wireless Advertising Adapted to the User Location, Device, and Emotions”) and Zheng et al (“Mining Interesting Locations and Travel Sequences from GPS Trajectories”) and further in view of Stopher et al (“Processing GPS Data from Travel Surveys”).
As per claim 36, Bellotti discloses, “the location data of the device include sensor-generated GPS data from the device, when available” (Pg.1161, particularly C2, first paragraph; EN: this denotes the use of GPS data for the system).
However, Bellotti fails to explicitly disclose, “when sensor-generated GPS data is unavailable, a location of the device is interpolated.”
Stopher discloses, “when sensor-generated GPS data is unavailable, a location of the device is interpolated” (Pg.4, particularly the third paragraph; EN: this denotes continuing to determine location by interpolation when GPS is not available).
Bellotti and Stopher are analogous art because both involve GPS location.
At the time of invention it would have been obvious to one skilled in the art of GPS location to combine the work of Bellotti and Stopher in order to interpolate location when GPS is not available.
The motivation for doing so would be to “interpolate position for some short period of time when the signal is lost or deteriorates. Therefore, when the satellite number is low, the HDOP is high, or under other conditions when position is not to be known accurately, the receiver may provide latitude, longitude, altitude, heading and speed by interpolation from the immediately preceding valid records” (Stopher, Pg.4, third paragraph) or in the case of Bellotti, allow the system to still provide information based on location when GPS is not immediately available by interpolating the user’s location in order to provide responses.
Therefore at the time of invention it would have been obvious to one skilled in the art of GPS location to combine the work of Bellotti and Stopher in order to interpolate location when GPS is not available.
Response to Arguments
In pg.7, the Applicant argues in regards to the rejection under U.S.C. 101,
Amended claim 32 addresses the technical limitations of consumer-grade location determination where raw positional coordinates alone are insufficient to reliably determine user activity. By storing a context trace, generating inference points, and forming activity clusters, the claimed method improves location-derived activity inference under noisy and/or using inaccurate measurements. In particular, the claims receive data from location-determination functionality of a user's device and convert that data into an inferred activity as claimed, thereby improving the technological field of device-based position determination by transforming imprecise coordinates into reliable, contextually meaningful information. This is not mere categorization of data, but a direct enhancement in how raw location measurements yield meaningful results, e.g., inferred activity, under real-world constraints, the same type of improvement recognized as eligible in Example 4.
In response, the Examiner maintains the rejection as shown above. The Applicant’s argument here is clearly contrary to example 4. The claimed improvement is not to GPS or determining the user’s location to a finer precision as seen in Example 4, it is improvement to the abstract idea of reliably determining user activity. The GPS location is not improved, and a more precise location is not determined based upon this activity. The system makes an estimation based on GPS as to where the user is, and makes recommendation of content based on that location. There is no improvement to GPS or any other technology here, the improvement is to the abstract idea of content recommendation based upon user activity, which is not a technology, and therefore the rejection is maintained as shown above.
In pg.8, Applicant further argues in regards to the rejection under U.S.C. 101,
The present claims therefore address the same type of performance limitation identified in Example 4. In Example 4, the improvement was improved positioning under weak signal conditions. In the present claims, the improvement is enhanced position-related activity determination under noisy, imprecise, real-world conditions. Both inventions solve a technological limitation inherent to device-based positioning, and both do so without modifying the underlying hardware or architecture.
In response, the Examiner maintains the rejection as shown above. As discussed above, Applicant’s improvement is to the abstract idea of providing recommendations to the user based upon their inferred activity, which includes the use of GPS to determine their location. Example 4 explicitly states that:
The meaningful limitations placed upon the application of the claimed mathematical operations show that the claim is not directed to performing mathematical operations on a computer alone. Rather, the combination of elements impose meaningful limits in that the mathematical operations are applied to improve an existing technology (global positioning) by improving the signal-acquisition sensitivity of the receiver to extend the usefulness of the technology into weak-signal environments and providing the location information for display on the mobile device.
This explicitly states that the improvement an existing technology (global positioning) by improving the signal-acquisition sensitivity to the receiver. Here there is no improvement to the existing technology of GPS. There is no receiver that has extended usefulness in weak-signal environments. There is no improvement to determining the more precise location of the user in reference to GPS. It merely uses GPS as a tool in order to infer the user’s location and make recommendations based upon the activity they predict the user to be in. This is not an improvement to a technology, and therefore the rejection is maintained as shown above.
In pg.10 the Applicant further argues in regards to the rejection under U.S.C. 101,
The Federal Circuit found eligibility in McRO because the claims recited rules that provided a specific, technological solution to a problem in computer animation (lip- synchronization). The controlling principle is not the domain of application, but the presence of structured analytical rules that produce more accurate results through computer-implemented processing. See McRO, Inc. V. Bandai Namco Games America, Inc., 837 F.3d 1299, 1314 (Fed. Cir. 2016) (the claim's construction incorporated rules of a particular type that improved an existing technological process).
The same reasoning applies here. The present claims recite specific analytical rules and processing steps including determining a hypothetical venue visit, generating inference points, generating activity clusters, and mapping the hypothetical visit to an inferred activity, which results in accurate activity inference using location information that is noisy or imprecise. The claims do not simply generate or display recommendations. Rather, they transform raw location measurements into reliable activity determinations.
In response, the Examiner maintains the rejection under U.S.C. 101 as shown above. Once again, the Applicant hinges their argument on a court case that has a very specific technology, that of improving video lip-synchronization. There is no such associated technology here. Taking in location data and making a prediction about where the user is and what they are doing and providing content recommendation to the user is not an improvement to a technology, it is improvement to an abstract idea as discussed above, and therefore the rejection is maintained as shown above.
Applicant's arguments with respect to claims 32, and 35-36 have been considered but are moot in view of the new ground(s) of rejection or are repetitions of the above arguments and maintained for similar reasons.
Conclusion
The examiner requests, in response to this Office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BEN M RIFKIN whose telephone number is (571)272-9768. The examiner can normally be reached Monday-Friday 9 am - 5 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached at (571) 270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BEN M RIFKIN/Primary Examiner, Art Unit 2123
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