DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendments
The action is responsive to the Applicant’s arguments filed on 1/14/2026.
Claims 1-20 are pending in the application.
Response to Arguments
Applicant’s arguments with respect to the rejections previously made filed on 1/14/2026 have been fully considered but they are not persuasive. In view of the claim amendments, the rejections are being updated accordingly.
In regards to independent claim 1, Applicant argued that cited reference Dotan-Cohen does not disclose "determining a level of confidence for one or more query results, wherein the level of confidence indicates a correlation between the semantic identifiers and the identified information."
In response to the arguments, it is submitted the cited limitations are being properly addressed by Dotan-Cohen based at least on Dotan-Cohen disclosing the following:
Dotan-Cohen discloses determining a level of confidence for one or more query results in paragraph [0058]-[0059] by referring to Fig. 2, and stating, “The user hub inference engine 220 can further analyze the one or more clusters 310, 320 to determine a confidence score… the user hub inference engine 220 is configured to return a location value associated with the cluster to the user for confirmation thereof, for instance, through presentation component 240”. Dotan-Cohen continues by disclosing in paragraphs [0062]- [0063], “a corresponding location value may be extracted from the semantic labeling component 264… It is contemplated that any one of these parameters, such as event identifiers, temporal descriptors, or location labels, can be searched independently or in combination, to identify one or more potential search results.” Thus, Dotan-Cohen discloses determining the level of confidence for one or more query results indicating a correlation between the semantic identifiers and the identified information.
Additionally, Dotan-Cohen discloses details in paragraphs [0007]-[0009], where the score corresponds to a potentially significant location. Dotan-Cohen states, “… to compute a confidence score corresponding to each potentially significant location. To this end, a potentially significant location with a high confidence score may be determined as a user-significant location.” Dotan-Cohen relies heavily on user semantic identifiers in association with the location data associated with the users, by continuing to disclose, “In some embodiments, a semantic labeling component associated with the user can be provided to associate a location label with each user-significant location or “user hub.” The location label is, in essence, a semantic identifier, which can be any one or more terms having semantic significance to the user for use in conjunction with any particular user hub.” Thus, Dotan-Cohen teaches that the level of confidence indicates a correlation between the semantic identifiers and the identified information, as recited in claim 1.
Also, Applicant argued that Dotan-Cohen does not disclose "providing the one or more query results in response to the semantic query based on the level of confidence." However, as explained above, the confidence score plays a major role in determining which query results are provided in response to a semantic query. In Fig. 2 and paragraph [0058], Dotan-Cohen discloses, “The user hub inference engine 220 can further analyze the one or more clusters 310, 320 to determine a confidence score… the user hub inference engine 220 is configured to return a location value associated with the cluster to the user for confirmation thereof, for instance, through presentation component 240”. Dotan-Cohen continues by giving an example in paragraph [0065], “In another example, if the search result for example event query “What was the name of the restaurant I ate at with John last week?” resulted in a single location label or value, it is contemplated that, by way of example, a restaurant review or a map displaying the restaurant name and location is automatically displayed by the presentation component 240 in response to the event query.” Thus, Dotan-Cohen discloses the search result not only concerns a presentation format, but is also based on a level of confidence.
In regards to independent claims 16 and 19, the emphasized limitations that the Applicant argues in claims 16 and 19 are similar to the emphasized limitations of claim 1, which have been addressed above. See the response of claim 1 above for explanation.
Furthermore, it is also submitted that all limitations in pending claims, including those not specifically argued, are properly addressed. The reason is set forth in the rejections. See claim analysis below for detail.
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 Langi, 759 F.2d 887,225 USPQ 645 (Fed. Cir. 1985); In re VanOrnum, 686 F.2d 937,214 USPQ 761 (CCPA 1982); In re Vogel, 422F.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 CPR 1.321(c) or l.32l(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 CPR l.32l(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 CPR 1.111(a). For a reply to final Office action, see 37 CPR l.113(c). A request for reconsideration while not provided for in 37 CPR l.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, ref er to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
5. Claims 1-20 are rejected under 35 U.S.C. 101 on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,164,584 (reference patent).
Although the claims at issue are not identical, they are not patentably distinct from each other because both are directed to a similar invention with similar limitations as demonstrated in the table below.
Instant Application Number 18/951,090
Reference - US Patent No. 12,164,584
A method, comprising:
receiving, by one or more processors, a semantic query input by a user in a computing device;
identifying, by the one or more processors, semantic identifiers in the semantic query, wherein the semantic identifiers refer to user specific context associated with prior or planned activities of the user, the semantic identifiers including keywords identifying at least one of place, location, time, value, person, activity, or event types in the user specific context;
accessing, by the one or more processors, user data stored in one or more user data sources on the computing device;
identifying, by the one or more processors based on the semantic identifiers, information in the accessed user data correlated with the user specific context;
determining, by the one or more processors, a level of confidence for one or more query results, wherein the level of confidence indicates a correlation between the semantic identifiers and the identified information;
and providing, by the one or more processors, the one or more query results in response to the semantic query based on the level of confidence.
A method, comprising:
receiving, by one or more processors, a semantic query input by a user in a computing device;
identifying, by the one or more processors, semantic identifiers in the semantic query, wherein the semantic identifiers refer to user specific context associated with prior or planned activities of the user, the semantic identifiers including keywords identifying at least one of place, location, time, value, person, activity, or event types in the user specific context;
determining, by the one or more processors, a priority order of data types, wherein each data type is weighted based on user preferences and user habits, and wherein each data type corresponds to a user data source;
accessing in the determined priority order of data types, by the one or more processors, user data stored in the two or more user data sources on the computing device;
identifying, by the one or more processors based on the semantic identifiers, information in the accessed user data correlated with the user specific context;
determining, by the one or more processors, a level of confidence for one or more query results, wherein the level of confidence indicates a correlation between the semantic identifiers and the identified information;
and providing, by the one or more processors, the one or more query results in response to the semantic query based on the level of confidence.
2. The method of claim 1, further comprising: generating, by the one or more processors, a recommendation score based on the level of confidence.
2. The method of claim 1, further comprising: generating, by the one or more processors, a recommendation score based on the level of confidence
3. The method of claim 2, further comprising providing the recommendation score along with the one or more query results.
3. The method of claim 2, further comprising providing the recommendation score along with the one or more query results.
4. The method of claim 1, wherein identifying the information correlated with the user specific context comprises identifying at least one of sent or received text messages, incoming/outgoing phone calls, emails, photos, videos, calendar events, social media, contact information, names of associated friends, local traffic patterns, places or visited restaurants, or recorded or marked locations in a digital map
4. The method of claim 1, wherein identifying the information correlated with the user specific context comprises identifying at least one of sent or received text messages, incoming/outgoing phone calls, emails, photos, videos, calendar events, social media, contact information, names of associated friends, local traffic patterns, places or visited restaurants, or recorded or marked locations in a digital map.
5. The method of claim 1, further comprising: outputting the one or more query results in a digital map utilized in the computing device, wherein the one or more query results are associated with event locations displayed in the digital map.
5. The method of claim 1, further comprising: outputting the one or more query results in a digital map utilized in the computing device, wherein the one or more query results are associated with event locations displayed in the digital map.
6. The method of claim 1, further comprising: outputting the one or more query results with an associated event photo
6. The method of claim 1, further comprising: outputting the one or more query results with an associated event photo.
7. The method of claim 1, wherein the semantic query is received in a natural language form.
7. The method of claim 1, wherein the semantic query is received in a natural language form.
8. The method of claim 1, further comprising: receiving, by the one or more processors, an input for selection of one of the one or more query results.
8. The method of claim 1, further comprising: receiving, by the one or more processors, an input for selection of one of the one or more query results.
9. The method of claim 1, wherein determining one or more query results comprises processing the identified information using neural networks.
9. The method of claim 1, wherein determining one or more query results comprises processing the identified information using neural networks.
10. The method of claim 9, wherein processing the identified information comprises generating at least one of place embeddings or correlated data and query embedding by running a neural network.
10. The method of claim 9, wherein processing the identified information comprises generating at least one of place embeddings or correlated data and query embedding by running a neural network.
11. The method of claim 1, further comprising: detecting, by the one or more processors, a user pattern based on the accessed user data; and saving, by the one or more processors, the user pattern in the computing device.
11. The method of claim 1, further comprising: detecting, by the one or more processors, a user pattern based on the accessed data; and saving, by the one or more processors, the user pattern in the computing device.
12. The method of claim 1, wherein identifying the information in the accessed user data further comprises: correlating visited places with the semantic identifiers from the user specific context.
12. The method of claim 1, wherein identifying the information in the accessed data further comprises: correlating visited places with the semantic identifiers from the user specific context.
13. The method of claim 12, further comprising: correlating event information with the visited places and the semantic identifiers from the user specific context.
13. The method of claim 12, further comprising: correlating event information with the visited places and the semantic identifiers from the user specific context.
14. The method of claim 13, further comprising: correlating timestamps with the event and visited places and the semantic identifiers from the user specific context.
14. The method of claim 13, further comprising: correlating timestamps with the event and visited places and the semantic identifiers from the user specific context
15. The method of claim 1, further comprising: receiving, by the one or more processors, an input for selection of one of the query results; and providing, by the one or more processors, navigation in the computing device for travel to a destination based on the selected query result.
15. The method of claim 1, further comprising: receiving, by the one or more processors, an input for selection of one of the query results; and providing, by the one or more processors, navigation in the computing device for travel to a destination based on the selected query result.
16. A computing device, comprising: one or more memories: one or more processors in communication with the one or more memories, the one or more processors configured to:
receive a semantic query input by a user; identify semantic identifiers in the semantic query, wherein the semantic identifiers refer to user specific context associated with prior or planned activities of the user, the semantic identifiers including keywords identifying at least one of place, location, time, value, person, activity, or event types in the user specific context;
access user data stored in one or more data sources on the computing device;
identify, based on the semantic identifiers, information in the accessed user data correlated with the user specific context;
determine a level of confidence for one or more query results, wherein the level of confidence indicates a correlation between the semantic identifiers and the identified information; and provide the one or more query results in response to the semantic query based on the level of confidence.
16. A computing device, comprising: one or more memories: one or more processors in communication with the one or more memories, the one or more processors configured to:
receive a semantic query input by a user; identify semantic identifiers in the semantic query, wherein the semantic identifiers refer to user specific context associated with prior or planned activities of the user, the semantic identifiers including keywords identifying at least one of place, location, time, value, person, activity, or event types in the user specific context;
determine a priority order of data types, wherein each data type is weighted based on user preferences and user habits, and wherein each data type corresponds to a user data source;
access, in the determined priority order of data types, user data stored in the two or more data sources on the computing device;
identify, based on the semantic identifiers, information in the accessed user data correlated with the user specific context;
determine a level of confidence for one or more query results, wherein the level of confidence indicates a correlation between the semantic identifiers and the identified information; and provide the one or more query results in response to the semantic query based on the level of confidence.
17. The computing device of claim 16, wherein the one or more processors are further configured to: generate a recommendation score based on the level of confidence.
17. The computing device of claim 16, wherein the one or more processors are further configured to: generate a recommendation score based on the level of confidence.
18. The computing device of claim 16, wherein the one or more processors are further configured to: receive an input in the computing device to select one of the query results; and generate navigation information in a digital map in the computing device.
18. The computing device of claim 16, wherein the one or more processors are further configured to: receive an input in the computing device to select one of the query results; and generate navigation information in a digital map in the computing device.
19. A non-transitory computer-readable storage medium storing instructions executable by one or more processors for performing a method, comprising:
receiving, by one or more processors, a semantic query input by a user in a computing device;
identifying, by the one or more processors, semantic identifiers in the semantic query, wherein the semantic identifiers refer to user specific context associated with prior or planned activities of the user, the semantic identifiers including keywords identifying at least one of place, location, time, value, person, activity, or event types in the user specific context;
accessing, by the one or more processors with authorization from the user, user data stored in one or more user data sources on the computing device;
identifying, by the one or more processors based on the semantic identifiers, information in the accessed user data correlated with the user specific context; correlating, by the one or more processors, visited places with the semantic identifiers from the user specific context;
determining, by the one or more processors, on a level of confidence for one or more query results, wherein the level of confidence indicates a correlation between the semantic identifiers and the identified information; and providing, by the one or more processors, the one or more query results in response to the semantic query based on the level of confidence.
19. A non-transitory computer-readable storage medium storing instructions executable by one or more processors for performing a method, comprising:
receiving, by one or more processors, a semantic query input by a user in a computing device;
identifying, by the one or more processors, semantic identifiers in the semantic query, wherein the semantic identifiers refer to user specific context associated with prior or planned activities of the user, the semantic identifiers including keywords identifying at least one of place, location, time, value, person, activity, or event types in the user specific context;
determining, by the one or more processors, a priority order of data types, wherein each data type is weighted based on user preferences and user habits, and wherein each data type corresponds to a user data source;
accessing, in the determined priority order of data types, by the one or more processors with authorization from the user, user data stored in the two or more user data sources on the computing device;
identifying, by the one or more processors based on the semantic identifiers, information in the accessed user data correlated with the user specific context; correlating, by the one or more processors, visited places with the semantic identifiers from the user specific context;
determining, by the one or more processors, on a level of confidence for one or more query results, wherein the level of confidence indicates a correlation between the semantic identifiers and the identified information; and providing, by the one or more processors, the one or more query results in response to the semantic query based on the level of confidence.
20. The computer-readable storage medium of claim 19, further comprising: generating a recommendation score based on the level of confidence.
20. The computer-readable storage medium of claim 19, further comprising: generating a recommendation score based on the level of confidence.
As demonstrated by the mappings in the table above, US Patent No. 12,164,584 discloses or renders obvious all the features of the claims of the instant application.
Claim Rejections - 35 USC § 102
5. 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. 6.The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
6. Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Dotan-Cohen et al. (US 20170116285 A1).
Regarding Claim 1, Dotan-Cohen discloses a method, comprising:
receiving, by one or more processors ([0089] With reference to FIG. 7, computing device 700 includes… one or more processors 714), a semantic query input by a user in a computing device (Fig. 2; [0060]: Semantic recollection component 230 is generally responsible for receiving and/or processing an event query or the query parameters thereof);
identifying, by the one or more processors, semantic identifiers in the semantic query (Fig. 2; [0062]: In another instance, event query parameters may include at least an event identifier. As such, and by way of example only, an event identifier can filter data from the event history register 262 to determine one or more potential events having a classification associated with the event identifier (i.e., an email, a text, a phone call, etc.)), wherein the semantic identifiers refer to user specific context associated with prior or planned activities of the user, the semantic identifiers including keywords identifying at least one of place, location, time, value, person, activity, or event types in the user specific context ([0004]: The event query may include a keyword that references a type or classification of the prior event, a semantic identifier associated with where the event took place, and/or a temporal descriptor associated with the prior event);
accessing, by the one or more processors, user data stored in one or more user data sources on the computing device ([0018]: user-specific raw data is typically stored on a cloud-based server to, ideally, be accessed and utilized by all computing devices associated with the user; [0047]: Continuing with FIG. 2, user-data collection component 210 is generally responsible for accessing or receiving (and in some cases also identifying) user data from one or more data sources, such as data sources 104a and 104b through 104n of FIG. 1);
identifying, by the one or more processors based on the semantic identifiers, information in the accessed user data correlated with the user specific context ([0027]: Moreover, by analyzing the temporal data associated with the user data, correlations between location values, device events, and temporal data can be identified);
determining, by the one or more processors, a level of confidence for one or more query results, wherein the level of confidence indicates a correlation between the semantic identifiers and the identified information ([0058]-[0059]: In some embodiments, the size or relative number of data points for each cluster can be a major factor in determining a confidence score for a cluster being evaluated as a potential user hub); and
providing, by the one or more processors, the one or more query results in response to the semantic query based on the level of confidence (Fig. 2; [0065]: a personalization-related service or application operating in conjunction with presentation component 240 determines when and how to present the search result; [0066]: Turning now to FIG. 4, an example of a search result generated in response to and based on a received event query is described).
Regarding Claim 2, Dotan-Cohen discloses the method of claim 1, further comprising: generating, by the one or more processors, a recommendation score based on the level of confidence (Fig. 2; [0058]- [0065]: A confidence score may be calculated for each cluster analyzed by the user hub inference engine 220… the search result may be understood as a recommendation to the presentation component 240).
Regarding Claim 3, Dotan-Cohen discloses the method of claim 2, further comprising providing the recommendation score along with the one or more query results (Fig. 2; [0058]- [0059]: A confidence score may be calculated for each cluster analyzed by the user hub inference engine 220… the user hub inference engine 220 is configured to return a location value associated with the cluster to the user for confirmation thereof, for instance, through presentation component 240; [0065]: the search result may be understood as a recommendation to the presentation component 240).
Regarding Claim 4, Dotan-Cohen discloses the method of claim 1, wherein identifying the information correlated with the user specific context comprises identifying at least one of sent or received text messages, incoming/outgoing phone calls, emails, photos, videos, calendar events, social media, contact information, names of associated friends, local traffic patterns, places or visited restaurants, or recorded or marked locations in a digital map ([0024]: For instance, a device event may include an incoming/outgoing phone call, a sent/received text message or email, a voicemail received, a picture taken/shared/viewed, a detection of a location, a webpage visited, and the like; Fig. 2; [0062]: In another instance, event query parameters may include at least an event identifier. As such, and by way of example only, an event identifier can filter data from the event history register 262 to determine one or more potential events having a classification associated with the event identifier (i.e., an email, a text, a phone call, etc.)).
Regarding Claim 5, Dotan-Cohen discloses the method of claim 1, further comprising: outputting the one or more query results in a digital map utilized in the computing device, wherein the one or more query results are associated with event locations displayed in the digital map ([0023]: In some other aspects, the user can proactively input and associate a location label with a particular location (i.e., via a location point on a map, a presently-detected location, or in association with an inferred user hub); Fig. 2; [0065]: In another example, if the search result for example event query “What was the name of the restaurant I ate at with John last week?” resulted in a single location label or value, it is contemplated that, by way of example, a restaurant review or a map displaying the restaurant name and location is automatically displayed by the presentation component 240 in response to the event query).
Regarding Claim 6, Dotan-Cohen discloses the method of claim 1, further comprising: outputting the one or more query results with an associated event photo ([0065]: For instance, if the search result for example event query “What was the picture I took at Jane's house yesterday?” resulted in a single image, it is contemplated that, by way of example, the single image result may automatically be displayed by the presentation component 240 in response to the event query).
Regarding Claim 7, Dotan-Cohen discloses the method of claim 1, wherein the semantic query is received in a natural language form ([0021]: More particularly, embodiments may recall past computing device event information by interpreting an event query in natural language form).
Regarding Claim 8, Dotan-Cohen discloses the method of claim 1, further comprising: receiving, by the one or more processors, an input for selection of one of the one or more query results (Fig. 4; [0069]: the user interface 400 may be configured to present each search result as a selectable item, as illustrated by search result thumbnails 430, 440, 450, and 460. In this regard, one or more of the search results may be selected by the user to receive more detail about the one or more event records associated therewith).
Regarding Claim 9, Dotan-Cohen discloses the method of claim 1, wherein determining one or more query results comprises processing the identified information using neural networks (Figs. 5-6; [0070]; Each block or step of method 500 and other methods described herein comprises a computing process that may be performed using any combination of hardware, firmware, and/or software; [0087]-[0088]: The computing device 700 is but one example of a suitable computing environment… Embodiments of the invention may be practiced in a variety of system configurations, including handheld devices, consumer electronics, general-purpose computers, more specialty computing devices, etc.).
Regarding Claim 10, Dotan-Cohen discloses the method of claim 9, wherein processing the identified information comprises generating at least one of place embeddings or correlated data and query embedding by running a neural network ([0027]: This user data can also be used to determine correlations between the location values and device events that occurred at the location values. Moreover, by analyzing the temporal data associated with the user data, correlations between location values, device events, and temporal data can be identified).
Regarding Claim 11, Dotan-Cohen discloses the method of claim 1, further comprising: detecting, by the one or more processors, a user pattern based on the accessed user data; and saving, by the one or more processors, the user pattern in the computing device (Fig. 1; [0007]: In some embodiments, a user hub inference engine can be provided to analyze the location data recorded in the location value history register… The user hub inference engine may further consider the number of unique location values within each cluster, along with patterns detected therein; [0026]: The received user data may be monitored and information about the user may be stored in a user profile).
Regarding Claim 12, Dotan-Cohen discloses the method of claim 1, wherein identifying the information in the accessed user data further comprises: correlating visited places with the semantic identifiers from the user specific context ([0007]: When provided with location data, the user hub inference engine can generate one or more inferences that certain locations visited by a user are significant to the user; [0081]-[0085]: several additional examples are described for providing personalized computing experiences to a user based on semantic location information associated with user-related activity… In a fifth example, a user asks ‘what was the weather like the last time I visited Atlanta).
Regarding Claim 13, Dotan-Cohen discloses the method of claim 12, further comprising: correlating event information with the visited places and the semantic identifiers from the user specific context ([0007]: When provided with location data, the user hub inference engine can generate one or more inferences that certain locations visited by a user are significant to the user; [0081]-[0085]: several additional examples are described for providing personalized computing experiences to a user based on semantic location information associated with user-related activity… In a fifth example, a user asks ‘what was the weather like the last time I visited Atlanta).
Regarding Claim 14, Dotan-Cohen discloses the method of claim 13, further comprising: correlating timestamps with the event and visited places and the semantic identifiers from the user specific context ([0006]: Each location data point in the location value history register can include a location value (e.g., a location coordinate) and a timestamp corresponding to the time the location value was generated and/or received).
Regarding Claim 15, Dotan-Cohen discloses the method of claim 1, further comprising:
receiving, by the one or more processors, an input for selection of one of the query results; and providing, by the one or more processors, navigation in the computing device for travel to a destination based on the selected query result (Fig. 4; [0069]: the user interface 400 may be configured to present each search result as a selectable item, as illustrated by search result thumbnails 430, 440, 450, and 460. In this regard, one or more of the search results may be selected by the user to receive more detail about the one or more event records associated therewith).
Regarding Claim 16, Dotan-Cohen discloses a computing device, comprising:
one or more memories: one or more processors in communication with the one or more memories ([0089]: With reference to FIG. 7, computing device 700 includes a bus 710 that directly or indirectly couples the following devices: memory 712, one or more processors 714), the one or more processors configured to:
receive a semantic query input by a user (Fig. 2; [0060]: Semantic recollection component 230 is generally responsible for receiving and/or processing an event query or the query parameters thereof);
identify semantic identifiers in the semantic query (Fig. 2; [0062]: In another instance, event query parameters may include at least an event identifier. As such, and by way of example only, an event identifier can filter data from the event history register 262 to determine one or more potential events having a classification associated with the event identifier (i.e., an email, a text, a phone call, etc.)), wherein the semantic identifiers refer to user specific context associated with prior or planned activities of the user, the semantic identifiers including keywords identifying at least one of place, location, time, value, person, activity, or event types in the user specific context ([0004]: The event query may include a keyword that references a type or classification of the prior event, a semantic identifier associated with where the event took place, and/or a temporal descriptor associated with the prior event);
access user data stored in one or more data sources on the computing device ([0018]: user-specific raw data is typically stored on a cloud-based server to, ideally, be accessed and utilized by all computing devices associated with the user; [0047]: Continuing with FIG. 2, user-data collection component 210 is generally responsible for accessing or receiving (and in some cases also identifying) user data from one or more data sources, such as data sources 104a and 104b through 104n of FIG. 1);
identify, based on the semantic identifiers, information in the accessed user data correlated with the user specific context ([0027]: Moreover, by analyzing the temporal data associated with the user data, correlations between location values, device events, and temporal data can be identified);
determine a level of confidence for one or more query results, wherein the level of confidence indicates a correlation between the semantic identifiers and the identified information ([0058]-[0059]: In some embodiments, the size or relative number of data points for each cluster can be a major factor in determining a confidence score for a cluster being evaluated as a potential user hub); and
provide the one or more query results in response to the semantic query based on the level of confidence (Fig. 2; [0065]: a personalization-related service or application operating in conjunction with presentation component 240 determines when and how to present the search result; [0066]: Turning now to FIG. 4, an example of a search result generated in response to and based on a received event query is described).
Regarding Claim 17, Dotan-Cohen discloses the computing device of claim 16, wherein the one or more processors are further configured to: generate a recommendation score based on the level of confidence (Fig. 2; [0058]- [0065]: A confidence score may be calculated for each cluster analyzed by the user hub inference engine 220… the search result may be understood as a recommendation to the presentation component 240).
Regarding Claim 18, Dotan-Cohen discloses the computing device of claim 16, wherein the one or more processors are further configured to: receive an input in the computing device to select one of the query results; and generate navigation information in a digital map in the computing device (Fig. 2; [0065]: In another example, if the search result for example event query “What was the name of the restaurant I ate at with John last week?” resulted in a single location label or value, it is contemplated that, by way of example, a restaurant review or a map displaying the restaurant name and location is automatically displayed by the presentation component 240 in response to the event query).
Regarding Claim 19, Dotan-Cohen discloses a non-transitory computer-readable storage medium storing instructions executable by one or more processors for performing a method ([0090]: Computing device 700 typically includes a variety of computer-readable media), comprising:
receiving, by one or more processors ([0089] With reference to FIG. 7, computing device 700 includes… one or more processors 714), a semantic query input by a user in a computing device (Fig. 2; [0060]: Semantic recollection component 230 is generally responsible for receiving and/or processing an event query or the query parameters thereof);
identifying, by the one or more processors, semantic identifiers in the semantic query (Fig. 2; [0062]: In another instance, event query parameters may include at least an event identifier. As such, and by way of example only, an event identifier can filter data from the event history register 262 to determine one or more potential events having a classification associated with the event identifier (i.e., an email, a text, a phone call, etc.)), wherein the semantic identifiers refer to user specific context associated with prior or planned activities of the user, the semantic identifiers including keywords identifying at least one of place, location, time, value, person, activity, or event types in the user specific context ([0004]: The event query may include a keyword that references a type or classification of the prior event, a semantic identifier associated with where the event took place, and/or a temporal descriptor associated with the prior event);
accessing, by the one or more processors with authorization from the user, user data stored in one or more user data sources on the computing device ([0018]: user-specific raw data is typically stored on a cloud-based server to, ideally, be accessed and utilized by all computing devices associated with the user; [0047]: Continuing with FIG. 2, user-data collection component 210 is generally responsible for accessing or receiving (and in some cases also identifying) user data from one or more data sources, such as data sources 104a and 104b through 104n of FIG. 1);
identifying, by the one or more processors based on the semantic identifiers, information in the accessed user data correlated with the user specific context ([0027]: Moreover, by analyzing the temporal data associated with the user data, correlations between location values, device events, and temporal data can be identified);
correlating, by the one or more processors, visited places with the semantic identifiers from the user specific context ([0007]: When provided with location data, the user hub inference engine can generate one or more inferences that certain locations visited by a user are significant to the user; [0085]-[0086]: In a fifth example, a user asks ‘what was the weather like the last time I visited Atlanta… Accordingly, we have described various aspects of technology directed to recalling information related to past computing device events);
determining, by the one or more processors, on a level of confidence for one or more query results, wherein the level of confidence indicates a correlation between the semantic identifiers and the identified information ([0058]-[0059]: In some embodiments, the size or relative number of data points for each cluster can be a major factor in determining a confidence score for a cluster being evaluated as a potential user hub); and
providing, by the one or more processors, the one or more query results in response to the semantic query based on the level of confidence (Fig. 2; [0065]: a personalization-related service or application operating in conjunction with presentation component 240 determines when and how to present the search result; [0066]: Turning now to FIG. 4, an example of a search result generated in response to and based on a received event query is described).
Regarding Claim 20, Dotan-Cohen discloses the computer-readable storage medium of claim 19, further comprising: generating a recommendation score based on the level of confidence (Fig. 2; [0058]- [0065]: A confidence score may be calculated for each cluster analyzed by the user hub inference engine 220… the search result may be understood as a recommendation to the presentation component 240).
Conclusion
27. THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHIRLEY D. HICKS whose telephone number is (571)272-3304. The examiner can normally be reached Mon - Fri 7:30 - 4:00.
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/S D H/Examiner, Art Unit 2168
/CHARLES RONES/Supervisory Patent Examiner, Art Unit 2168
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