DETAILED ACTION
This is a response to applicant’s submissions filed on 8/6/2025. Claims 1-3, 6, 8-13 and 16-18 are pending.
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 .
Response to Arguments
Applicant's arguments filed 8/6/2025 have been fully considered but they are not persuasive.
It is noted that applicant’s amendments to the claims have overcome the rejections under 35 U.S.C. § 101.
In response to applicant’s argument that the CDMI chip implementation with its non-generic functionality configuration offers significant technological advantages over both prior art references (applicant’s remarks; pp. 18-19), the examiner respectfully disagrees. Paragraph 67 discloses the only function of the CDMI chip is enabling the device OS it is in motion. Further, the acronym CDMI is not defined, thus it unclear if a CDMI chip is capable of performing any functions other than determining it is moving. Rhodes, in paragraph 69, discloses a speed sensor configured to output a signal to a controller indicating the speed of the vehicle implementing the routing system. Rhodes further discloses, in paragraph 37, determining when a user has departed the vehicle when a mobile device of the user detects movement indicative of a user walking and sends and indication of the motion to the vehicle or remote server. Therefore, the CDMI chip implementation is generically recited and does not appear to provide specific advantages over the prior art. See rejections below.
In response to applicant's argument that the CDMI chip automatically detects tour-related motion patterns, automatically switches the device into on-route mode, and implements geo-fencing capabilities dynamically integrating user profile affinity-based locations (applicant’s remarks; p. 19), the examiner respectfully disagrees. Paragraph 67 discloses the only function of the CDMI chip is enabling the device OS it is in motion. Further, the acronym CDMI is not defined, thus it unclear if a CDMI chip is capable of performing any functions other than determining it is moving. There does not appear to be disclosure of the CDMI chip detecting motion patterns, switching device modes, or implementing geo-fencing. It appears that processor of the mobile device implements these functions using input from the CDMI chip. See rejections below.
In response to applicant’s argument that the combination of three and four prior art references is indicative of undue hindsight (applicant’s remarks; p. 20), the examiner respectfully disagrees. Reliance on a large number of references in a rejection does not, without more, weigh against the obviousness of the claimed invention. See In re Gorman, 933 F.2d 982, 18 USPQ2d 1885 (Fed. Cir. 1991). See rejections below.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference characters "200" (para. 46, l. 5) and "204" (para. 47, l. 1) have both been used to designate a cloud-based real estate sales and purchasing platform. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The abstract of the disclosure is objected to because of undue length. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The disclosure is objected to because of the following informalities:
The paragraph numbers of the amendments to the specification do not match the original filing. It appears that amended paragraph 30 should be changed to 32, 31 to 33, 53 to 55, 90 to 96, and 19 to 20. The paragraphs should be renumbered to be consistent with the originally filed specification.
In paragraph 11, lines 1-2, “for an artificial neural network optimized user profile-based journey planning” should read “for artificial neural network optimized user profile-based journey planning”. This appears to be a typographical error.
Paragraph 46, line 5, discloses cloud-based real estate sales and purchasing platform 200, and paragraph 47, line 1, discloses cloud-based real estate sales and purchasing platform 204. It is unclear which functions in the disclosure are allocated to which hardware in the system between the cloud-based real estate sales and purchasing platforms is unclear.
In paragraph 36, lines 9 and 10, the extra characters “.).” should be removed. This appears to be a typographical error.
In paragraph 67, line 1, “prompt table” should read “prompt the tablet”. This appears to be a typographical error.
In paragraph 67, line 6, the structure of the CDMI chip is unclear because the acronym is not defined.
In paragraph 89, line 6, “it is note” should read “it is noted”. This appears to be a typographical error.
In paragraph 89, lines 7-8, “the real estate property tour tablet point out various points of interest” should read “the real estate property tour tablet points out various points of interest”.
In paragraph 90, lines 1-2, “detects when within a geofence of property to be toured has been entered” should read “detects when a geofence of a property to be toured has been entered”. This appears to be a typographical error.
Appropriate correction is required.
Claim Objections
Claims 1 and 11 are objected to because of the following informalities:
In claim 1, lines 1-3, “a computerized method for generating and using user profile-based routing machine-learned model(s) to optimize a route planner system:” should read “a computerized method for generating and using user profile-based routing machine-learned model(s) to optimize a route planner system, the computerized method comprising:” because the relationship between the preamble and the subsequent limitations is unclear.
Claims 1 and 11 should be limited to a single colon because using multiple colons in a single sentence to form nested lists is grammatically incorrect and makes determining the relationships between limitations confusing.
In claim 1, lines 8 and 12, and claim 11, line 10, “the historical use profile data” should read “the historical user profile data”. This appears to be a typographical error.
In claims 1 and 11, lines 32 and 30, respectively, the acronym “CDMI” should be defined.
In claim 1 and 11, lines 41 and 39, respectively, “automatically includes series of local points” should read “automatically includes a series of local points”. This appears to be a typographical error.
In claim 11, lines 12-13, “data an input” should read “data input”. This appears to be a typographical error.
Appropriate correction is required.
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 1-3, 6, 8-13 and 16-18 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.
Regarding claim 1, lines 4-5, the limitation “with a computing system of a cloud-based platform: providing a first data store … [and] providing a second data store” appears to be new matter because there is no disclosure of a computing system of a cloud-based platform providing the data stores. Figure 2 discloses data store(s) 208 are separate from the cloud-based real-estate sales and purchasing platform 204. Figure 2 further discloses cloud-based real estate sales and purchasing platform 200 includes data store(s) 208, however, it is not disclosed that platform 200 is implemented on computing system 500. Although paragraph 87 discloses computing system 500 can perform any of the disclosed processes (e.g., 600), paragraph 95 discloses process 600 is limited to using (i.e., consuming, not providing) data from the data stores for training. The disclosure appears to limit the computing system of the cloud-based platform to accessing separate first and second data stores using the computer/cellular data networks.
Regarding claim 1, lines 24-31, the limitation “with a user-side mobile device: obtaining a set of tour stops … generating a tour route of the set of tour stops … identify a set of user profile affinity-based locations near the tour route; and generating an updated tour route” appears to be new matter because there is no disclosure of the mobile device performing these steps. Figure 7 includes the steps obtain a set of possible route matches to elements of user profile data (706), wherein the elements of user profile data include lifestyle points of interest (para. 99) which can be a set of user profile affinity-based locations (para. 98), obtain a set of tour stops (710), and generate a tour route (712), however, it is not disclosed that process 700 is executed on the mobile device. Paragraph 105 discloses processes 600 and 700 can be used to update the output of a route planner, however, it is not disclosed that processes 600 or 700 are executed on the mobile device. Paragraph 87 discloses computing system 500 can perform processes 600 and 700, however, it is not disclosed that computing system 500 is exemplary of the mobile device. Paragraph 63 further discloses real estate tour mobile device 206 “only interfaces with cloud-based real estate sales and purchasing platform 204”. It appears that the user-side mobile device is limited to receiving tour stops and routes from the cloud-based platform.
Regarding claim 11, lines 4-21, the limitation “a user-side mobile device configured to: obtain a set of tour stops … generate a tour route of the set of tour stops … identify a set of user profile affinity-based locations near the tour route; and generate an updated tour route” appears to be new matter because there is no disclosure of the mobile device performing these steps. Figure 7 includes the steps obtain a set of possible route matches to elements of user profile data (706), wherein the elements of user profile data include lifestyle points of interest (para. 99) which can be a set of user profile affinity-based locations (para. 98), obtain a set of tour stops (710), and generate a tour route (712), however, it is not disclosed that process 700 is executed on the mobile device. Paragraph 105 discloses processes 600 and 700 can be used to update the output of a route planner, however, it is not disclosed that processes 600 or 700 are executed on the mobile device. Paragraph 87 discloses computing system 500 can perform processes 600 and 700, however, it is not disclosed that computing system 500 is exemplary of the mobile device. Paragraph 63 further discloses real estate tour mobile device 206 “only interfaces with cloud-based real estate sales and purchasing platform 204”. It appears that the user-side mobile device is limited to receiving tour stops and routes from the cloud-based platform.
Regarding claim 11, lines 4-7, the limitation “a memory in the computing system of a cloud-based platform containing instructions when executed on the processor, causes the processor to perform operations that: provide a first data store” appears to be new matter because there is no disclosure of the cloud-based platform providing the data stores. Figure 2 discloses data store(s) 208 are separate from the cloud-based real-estate sales and purchasing platform 204. Figure 2 further discloses cloud-based real estate sales and purchasing platform 200 includes data store(s) 208, however, it is not disclosed that platform 200 is implemented on computing system 500. Although paragraph 87 discloses computing system 500 can perform any of the disclosed processes (e.g., 600), paragraph 95 discloses process 600 is limited to using (i.e., consuming, not providing) data from the data stores for training. The disclosure appears to limit the computing system of the cloud-based platform to accessing separate first and second data stores using the computer/cellular data networks.
Regarding claims 1 and 11, lines 32-36 and 30-34, respectively, the limitation “a CDMI chip configured to … detect a series of motions related to a tour and, automatically switch the user-side mobile device into an on-route mode” appears to be new matter because there is no disclosure of the CDMI chip detecting a series of motions or controlling the user-side mobile device. Paragraph 67 discloses the CDMI chip enables the device OS to know it is in motion, and further discloses once the device detects it in a series of motions related to a tour, the device can automatically switch into an on-route mode. The disclosure appears to limit the CDMI chip to merely notifying the device OS that it is in motion.
Regarding claims 1 and 11, lines 37-42 and 35-40, respectively, the limitation “the on-route mode causes … the cloud-based platform to implement a geo-fencing operation that automatically includes series of local points of interest based on the set of user profile affinity-based locations within a geo-fenced area” appears to be new matter because there is no disclosure that, when in the on-route mode, the local points of interest included in the geo-fencing operation are based on the set of user profile affinity-based locations. Paragraph 68 discloses, in the on-route mode, a series of local points of interest within each geo-fenced area are automatically included in the geo-fencing operations implemented by the cloud-based real estate sales and purchasing platform. Paragraph 89 discloses, in a separate embodiment, in the client mode, as the client drives through a geo-fenced circle, a set of thought bubbles can appear announcing a point of interest based on the client’s profile. The disclosure does not appear to include an embodiment with an on-route mode wherein a geofencing operation includes local points of interest that are user profile affinity-based locations.
Claims 2-3, 6, 8-10, 12, 13 and 16-18 are rejected as being dependent on a rejected claim and for failing to cure the deficiencies listed above.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1-3, 6, 8-13 and 16-18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 1 and 11, lines 32 and 30, respectively, the limitation “a CDMI chip” renders the claim indefinite because it is unclear what a CDMI chip comprises. Where applicant acts as his or her own lexicographer to specifically define a term of a claim contrary to its ordinary meaning, the written description must clearly redefine the claim term and set forth the uncommon definition so as to put one reasonably skilled in the art on notice that the applicant intended to so redefine that claim term. Process Control Corp. v. HydReclaim Corp., 190 F.3d 1350, 1357, 52 USPQ2d 1029, 1033 (Fed. Cir. 1999). The term “CDMI chip” does not have an accepted meaning, and it is therefore unclear what the term includes. The term is indefinite because the specification does not clearly define the term. Paragraph 67 discloses a tablet can include a CDMI chip and that it can determine when it is in motion, however there is no further disclosure of what a CDMI chip may comprise. For purposes of examination, the CDMI chip is interpreted as any integrated circuit capable of processing motion data.
Regarding claim 8, line 1, the scope of the claim is unclear because the claim depends on a cancelled claim. For the purposes of examination, it will be assumed that claim 8 depends on claim 6.
Claims 2-3, 6, 8-10, 12-13 and 16-18 are rejected as being dependent on a rejected claim and for failing to cure the deficiencies listed above.
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 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.
Claim(s) 1-3, 6, 8-13 and 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rhodes et al. (US 2020/0026279) in view of Singh et al. (US 10,907,983) and Kalik et al. (US 8,306,921), hereinafter Rhodes, Singh, and Kalik, respectively, and the Wikipedia article titled "Training, validation, and test data sets", published March 15th, 2022.
Regarding claims 1 and 11, as best understood, Rhodes discloses a computerized method for generating and using user profile-based routing machine- learned model(s) to optimize a route planner system: with a computing system of a cloud-based platform; and with a user-side mobile device (Rhodes; para. 26: To generate the route 120, one or more computer processors coupled to at least one memory of a computer system (such as one or more remote servers, the autonomous vehicle 110, etc.) may determine a first set of inputs indicative of desired real estate. The one or more computer processors may correspond to the processor(s) 802 illustrated in FIG. 8 and/or the controller 604 of the vehicle illustrated in FIG. 6. In some embodiments, various operations may be performed by either or both the vehicle itself (e.g., a controller of the vehicle) or one or more remote servers): obtaining a set of tour stops (Rhodes; para. 21: Before, during, or after the autonomous vehicle journey is started or ordered, a machine learning algorithm may be used to generate a set of all the available real estate options.); with at least one route planning and optimization algorithm, generating a tour route of the set of tour stops (Rhodes; para. 21: The user and/or occupant may select a route based on several routing options (e.g., shortest path, fastest path, greenest path, etc.).); using the user profile-based routing ML model to identify a set of user profile affinity-based locations near the tour route; and generating an updated tour route that includes a portion of the set of user profile affinity-based locations near the tour route (Rhodes; para. 30: If the user is interested, the computer processor(s) may determine that the user is interested in a neighborhood of the first location (e.g., location of House 1 in this example), and may generate a neighborhood tour routing for the neighborhood that surrounds House 1, which may include points or locations of interest, which may be based on historical information associated with prior tours and/or the user. The neighborhood tour routing may include identified locations of interest, such as schools (e.g., if the user has children, etc.), parks, public facilities, shopping malls, and so forth.), wherein the user-side mobile device comprises a CDMI chip (Rhodes; fig. 6: controller 606) configured to: enable an operating system of the mobile device to detect that the user-side mobile device is in motion (Rhodes; para. 69: speed sensor 626 is configured to output a signal to the controller 606 indicating the speed of the vehicle), detect a series of motions related to a tour (Rhodes; para. 116: determine that the autonomous vehicle is within a distance of the second location), and automatically switch the user-side mobile device into an on-route mode (Rhodes; para. 64: After the route is generated, the vehicle is autonomously driven along the route), wherein the on-route mode causes a screen of the user-side mobile device to display a map of the tour route (Rhodes; fig. 4: second user interface 410; para. 58: Although illustrated as user device user interfaces, in some embodiments, user interfaces may be presented at a display of an autonomous vehicle.) and a current location of the current location of the user-side mobile device (Rhodes; para. 62: the segments may be defined between vehicle action points. An action point may be the origin, turns, intermediate stops, and the final destination. For example, the portion of the route between the origin and the first turn is the first segment) and cause the cloud-based platform to implement a geo-fencing operation that automatically includes series of local points of interest (Rhodes; para. 61: the computer processor(s) may determine a first location of interest within a predetermined distance of the first location of the first real estate property. The first location of interest may be, for example, one or more of: a playground, a park, a school, a hospital, and/or a shopping plaza. The computer processor(s) may determine a second location of interest within the predetermined distance, and may determine the neighborhood tour routing using the first location, the first location of interest, and a second location of interest. For example, as illustrated at a second user interface 410, the routing may include not only the selected properties, but locations of interest as well) based on the set of user profile affinity-based locations (Rhodes; para. 59: the user may input properties of interest, user preferences, price range, crime rate, and so forth. Such information may be used to generate a set of candidate real estate options).
Rhodes does not explicitly disclose providing a first data store of a plurality of historical user profile and a plurality of historical similar user profile data wherein the historical user profile data comprises a historical user preference data and a plurality of historical user lifestyle data wherein the historical use profile data comprises a user demographic data, a user spouse profile data, a user educational data, a user answers to an intake questionnaire, and a data input by an agent interviewing the user such that the agent updates the profile at a later time, and wherein the historical similar use profile data comprises a similar user demographic data, a similar user spouse profile data, a similar user educational data, a similar user answers to an intake questionnaire, and the data input by an agent interviewing the similar; and using the plurality of historical user profile data and the plurality of historical similar user profile data to train a user profile-based routing ML model.
Singh, in the same field of endeavor (personalized navigation information), discloses providing a first data store of a plurality of historical user profile (Singh; col. 5, ll. 3-4: The user profile store 300 stores user profiles of users of the online system 100.) and a plurality of historical similar user profile data (Singh; col. 9, ll. 10-27: In some embodiments, to determine predictions for a given user, the model 360 may also process input signals including user profile information or an action performed by one or more other users associated with the given user (e.g., social data). The other users may be connected to the given user on the online system 100, for instance, as friends, family, or co-workers. For example, the model 360 may include information about other users, e.g., friends, posting about a particular travel destination as an indication that the given user might also be interested in that travel destination. As another example, the navigation engine 350 determines other users that have at least one attribute in common with the given user, or at least one similar attribute to the given user. The attribute may be based on demographic data such as age, gender, ethnicity, socioeconomic status, geographical location (e.g., home residence), etc. Moreover, the attribute may be based on shared affinities such as membership to the same loyalty program, club, or other organization.), wherein the historical user profile data comprises a historical user preference data and a plurality of historical user lifestyle data (Singh; col. 5, ll. 11-19: Examples of information stored in a user profile include biographic, demographic, and other types of descriptive information, such as family information, travel history, residence history, geographical locations, pets, membership of organizations (e.g., American Automobile Association (AAA), loyalty programs of providers 120, government organizations, veteran or military groups, or senior citizen groups), occupation, educational history, gender, hobbies or preferences, etc.) wherein the historical use profile data comprises a user demographic data, a user spouse profile data, and a user educational data (Singh; col. 5, ll. 11-19: Examples of information stored in a user profile include biographic, demographic, and other types of descriptive information, such as family information, travel history, residence history, geographical locations, pets, membership of organizations (e.g., American Automobile Association (AAA), loyalty programs of providers 120, government organizations, veteran or military groups, or senior citizen groups), occupation, educational history, gender, hobbies or preferences, etc.), and wherein the historical similar use profile data (Singh; col. 5, ll. 3-4: The user profile store 300 stores user profiles of users of the online system 100.) comprises a similar user demographic data, a similar user spouse profile data, and a similar user educational data (Singh; col. 5, ll. 11-19: Examples of information stored in a user profile include biographic, demographic, and other types of descriptive information, such as family information, travel history, residence history, geographical locations, pets, membership of organizations (e.g., American Automobile Association (AAA), loyalty programs of providers 120, government organizations, veteran or military groups, or senior citizen groups), occupation, educational history, gender, hobbies or preferences, etc.); and using the plurality of historical user profile data and the plurality of historical similar user profile data to train a user profile-based routing ML model (Singh; col. 8, ll. 39-45: The machine learning engine 365 trains the model 360 using training data, which may include dense and/or sparse features. The machine learning engine 365 may retrieve training data from the user profile store 300, action log 320, edge store 325, other components of the online system 100, or other sources outside of the online system 100.).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, with a reasonable expectation of success, to have modified the route planning system of Rhodes with user-profile training data, as disclosed by Singh, to yield the predictable result of tailoring the ML model outputs to a user’s preferences thereby increasing convenience.
Rhodes, as modified, does not explicitly disclose the historical use profile data comprises a user answers to an intake questionnaire, and a data input by an agent interviewing the user such that the agent updates the profile at a later time, and wherein the historical similar use profile data comprises a similar user demographic data, a similar user spouse profile data, a similar user educational data, a similar user answers to an intake questionnaire, and the data input by an agent interviewing the similar; and using the plurality of historical user profile data and the plurality of historical similar user profile data to train a user profile-based routing ML model.
Kalik, in a reasonably pertinent field of endeavor (mobile recommendation systems), discloses a user answers to an intake questionnaire (Kalik; col. 9, l. 67 to col. 10, l. 3: A questionnaire or other method of acquiring and entering opinion data into an electronically accessible database can also be used for the direct collection of preferences.), and a data input by an agent interviewing the user such that the agent updates a profile at a later time (Kalik; col. 10, ll. 4-6: An interview process can be given that requires largely explicit statement of the requestor's preferences, and the data stored in a preference file.).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, with a reasonable expectation of success, to have modified the user and similar user profile data of Rhodes, as modified, to include a questionnaire and interview data, as disclosed by Kalik, to yield the predictable result of capturing user preferences.
Rhodes, as modified, does not explicitly disclose using the other plurality of historical user profile data and the plurality of historical similar user profile data to validate the user profile-based routing ML model.
However, the use of a separate validation data set is well known in the machine learning art, as documented in the Wikipedia article titled “Training, validation, and test data sets”, as a method of determining when a model is overfit to a training data set.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, with a reasonable expectation of success, to have modified the route planning system of Rhodes, as modified, with a validation data set as taught by the Wikipedia reference, to yield the predictable result of measuring the accuracy of the ML model.
Regarding claims 2 and 12, Rhodes, as modified, discloses the machine-learned model comprises an artificial neural network model (Singh; col. 8, ll. 34-39: The machine learning engine 365 may implement any number of machine learning techniques known to one skilled in the art including boosted decision trees, factorization machines, support vector machines, classifiers (e.g., a Naive Bayes or linear regression), gradient boosting, neural networks, deep learning, etc.).
Regarding claims 3 and 13, Rhodes, as modified, discloses the machine-learned model comprises a deep learning model (Singh; col. 8, ll. 34-39: The machine learning engine 365 may implement any number of machine learning techniques known to one skilled in the art including boosted decision trees, factorization machines, support vector machines, classifiers (e.g., a Naive Bayes or linear regression), gradient boosting, neural networks, deep learning, etc.).
Regarding claims 6 and 16, Rhodes, as modified, discloses the historical similar user profile data (Singh; col. 5, ll. 3-4: The user profile store 300 stores user profiles of users of the online system 100.) comprises a historical similar user preference data and a plurality of historical similar user lifestyle data (Singh; col. 5, ll. 11-19: Examples of information stored in a user profile include biographic, demographic, and other types of descriptive information, such as family information, travel history, residence history, geographical locations, pets, membership of organizations (e.g., American Automobile Association (AAA), loyalty programs of providers 120, government organizations, veteran or military groups, or senior citizen groups), occupation, educational history, gender, hobbies or preferences, etc.).
Regarding claims 8 and 18, as best understood, Rhodes, as modified, discloses a similarity between the user and a selection of any similar user data (Singh; col. 9, ll. 16-19: For example, the model 360 may include information about other users, e.g., friends, posting about a particular travel destination as an indication that the given user might also be interested in that travel destination.) is determined using a K-nearest neighbors algorithm (Singh; col. 8, ll. 34-39: The machine learning engine 365 may implement any number of machine learning techniques known to one skilled in the art including boosted decision trees, factorization machines, support vector machines, classifiers (e.g., a Naive Bayes or linear regression), gradient boosting, neural networks, deep learning, etc.; a person of ordinary skill in the machine learning art will recognize the k-nearest neighbors algorithm as a non-parametric, supervised learning classifier).
Regarding claim 9, Rhodes, as modified, discloses pushing the updated tour route to a user-side mobile device mapping application (Rhodes; fig. 4; para. 58: The content for display may be sent to the autonomous vehicle and/or user device by one or more remote servers, such as that illustrated in FIG. 8, for presentation to the user.).
Regarding claim 10, Rhodes, as modified, discloses displaying the updated tour route on the user-side mobile device mapping application (Rhodes; fig. 4; para. 61: For example, as illustrated at a second user interface 410, the routing may include not only the selected properties, but locations of interest as well.).
Regarding claim 17, Rhodes, as modified, discloses the historical similar use profile data (Singh; col. 5, ll. 3-4: The user profile store 300 stores user profiles of users of the online system 100.) comprises a similar user demographic data, a similar user spouse profile data, a similar user educational data (Singh; col. 5, ll. 11-19: Examples of information stored in a user profile include biographic, demographic, and other types of descriptive information, such as family information, travel history, residence history, geographical locations, pets, membership of organizations (e.g., American Automobile Association (AAA), loyalty programs of providers 120, government organizations, veteran or military groups, or senior citizen groups), occupation, educational history, gender, hobbies or preferences, etc.), a similar user answers to an intake questionnaire (Kalik; col. 9, l. 67 to col. 10, l. 3: A questionnaire or other method of acquiring and entering opinion data into an electronically accessible database can also be used for the direct collection of preferences.), and data input by an agent interviewing the similar user (Kalik; col. 10, ll. 4-6: An interview process can be given that requires largely explicit statement of the requestor's preferences, and the data stored in a preference file.).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) 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 JOSEPH THOMPSON whose telephone number is (571)272-3660. The examiner can normally be reached Mon-Thurs 9:00AM-3:00PM ET.
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, Erin Bishop can be reached on (571)270-3713. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JOSEPH THOMPSON/Examiner, Art Unit 3665
/Erin D Bishop/Supervisory Patent Examiner, Art Unit 3665